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The crude oil market and the gold market: Evidence for cointegration, causality and price discovery
Abstract
Given that the gold market and the crude oil market are the main representatives of the large commodity markets, it is of crucial practical significance to analyze their cointegration relationship and causality, and investigate their respective contribution, from the perspective of price discovery, to the common price trend so as to interpret the dynamics of the whole large commodity market and forecast the fluctuation of crude oil and gold prices.Empirical analysis indicates that, first, there are consistent trends between the crude oil price and the gold price with significant positive correlation coefficient 0.9295 during the sampling period, from January of 2000 to March of 2008. Second, there can be seen a long-term equilibrium between the two markets, and the crude oil price change linearly Granger causes the volatility of gold price, but not vice versa; moreover, the two market prices do not face a significant nonlinear Granger causality, which overall suggests their fairly direct interactive mechanism. Finally, with regard to the common effective price between the two markets, the contribution of the crude oil price seems larger than that of the gold price, whether with the permanent transitory (PT) model (86.50% versus 13.50%) or the information share (IS) model (50.28% versus 49.72%), which implies that the influence of crude oil on global economic development proves more far-reaching and extensive, and its role in the large commodity markets has attracted more attention in recent years.
... Using a long span of annual data , Baffes (2007) shows that the prices of precious metals, including gold, strongly respond to the price of oil. A similar result is produced by Zhang and Wei (2010), who, based on daily data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008), find that a rising oil price drives up the price of gold, but they do not find a reverse link. ...
... Hence, the Efficient Market Hypothesis (EMH) is the basis that motivates an analysis of cointegration among the assets under research. Zhang and Wei (2010) identify a cointegration link between two assets on daily data. Narayan et al. (2010) improve on the cointegration approach and analyze the long-run relationship between gold and oil futures prices over the period 1963-2008 at different levels of maturity in order to gauge differences in hedging behavior. ...
... Further, gold is quite resistant with respect to inflation while increases in oil prices usually affect the aggregate price level and lead to an increase in inflation, which makes investment in gold more attractive. Finally, when major oil deliveries are paid during periods of higher oil prices, producers might use excess proceeds to buy gold whose price would increase due to higher demand (Zhang & Wei, 2010). ...
... Their main findings suggest (i) that the U.S. futures market is the most efficient in processing information, (ii) that the U.S. and U.K. spot markets are less efficient than their corresponding futures markets, and (iii) that the volatility spillovers across markets exhibit asymmetric responses to news. Zhang and Wei (2010) use a monthly dataset, from January of 2000 to March of 2008, to analyze the cointegration and causality relationships between crude oil and gold markets. Their results suggest a unidirectional Granger causality running from crude oil to gold. ...
... Their results suggest a unidirectional Granger causality running from crude oil to gold. Based on panel cointegration and Granger causality methods and a monthly dataset ranging from January 1980 to February Zhang &Wei, 2010, Nazlioglu andSoytas (2012), examine the linkages between oil prices and twenty-four agricultural commodity prices. Their results suggest that oil prices exert a significant impact on agricultural commodity prices. ...
... Net connectedness, which is the difference between 'contribution to others' and 'from others' directional connectedness, reflects whether a commodity is a net transmitter (positive values of net connectedness) or a net receiver (negative value of net connectedness) of volatility. We notice that, for Basket 1, OIL and HOI are, on average over the entire period, net volatility transmitters to all other commodities in the basket, and, mechanically, GAS and GLD are net volatility receivers, which is consistent with the results of Zhang and Wei (2010). As for Basket 2, the results show that, on average over the full sample period, OIL, HOI, CRN and SOY are net volatility transmitters to all other commodities, and, at the opposite side, GAS, WHT and SOI are net volatility receivers, which is largely in line with the results of Ahmadi et al. (2016). ...
In this paper we investigate cross-commodity futures markets connectedness over different nearest-to-maturities. We thus implement time and time-frequency estimations for two constructed baskets of commodities, classified based on common delivery months. Using daily data spanning the period 1995–2020, we provide a set of stylized facts on the extent to which commodity markets are integrated or segmented. More specifically, our results show that the total connectedness is broadly insensitive to maturity. However, after 2008 financial crisis, the connectedness among commodity futures prices increases when the maturity increases. Furthermore, the overall connectedness amplifies during crises periods compared to tranquil periods. Moreover, certain pairwise markets are comparatively highly linked such as crude oil and heating oil, wheat and corn, corn and soybean, and soybean and soybean oil. The results also demonstrate that crude oil and heating oil are net transmitters all the time and across maturities, while natural gas, gold, and wheat are net receivers all the time and across maturities. More interestingly, the frequency decomposition reveals that most of periods of high total connectedness are driven mostly by high frequency components, which may indicate that commodity markets process information rapidly, except for the COVID-19 crisis period where total connectedness has been driven by lower frequency components.
... Another group of papers refer to the relationships between crude oil and precious metal prices (Zhang and Wei [8], Jain and Ghosh [9]) or between crude oil and agricultural items prices (Kristoufek et al. [6], Eissa and Al Refai [10], Sarwar et al. [11]). Zhang and Wei [8] analysed cointegration and causality among the gold market and the crude oil market and observed consistent trends with a significant positive correlation between the price of crude oil and the gold price from January 2000 to March 2008. ...
... Another group of papers refer to the relationships between crude oil and precious metal prices (Zhang and Wei [8], Jain and Ghosh [9]) or between crude oil and agricultural items prices (Kristoufek et al. [6], Eissa and Al Refai [10], Sarwar et al. [11]). Zhang and Wei [8] analysed cointegration and causality among the gold market and the crude oil market and observed consistent trends with a significant positive correlation between the price of crude oil and the gold price from January 2000 to March 2008. Moreover, there was a long-term equilibrium between these markets, and the price of oil Granger-caused the volatility in the price of gold. ...
... where n denotes the number of observations used in Equation (7), ESSU is the error sum of squares for Equation (7), and ESSR is the error sum of squares for the restricted model (8). ...
A fuel market is an important sector of the economy and fuel prices influence the prices of numerous products and services. This paper focuses on the analysis of the interrelationships between markets of fuels in the Visegrad Group (V4) countries. The research is based on weekly prices of Pb95 gasoline and diesel in the Czech Republic, Hungary, Poland, and Slovakia observed from January 2016 through December 2020. After performing the preliminary statistical analysis, the long-term relationships between the prices of fuels are investigated through application of the cointegrated regression Durbin–Watson (CRDW) test. Next, Granger causality is tested to answer the question of whether changes in prices of fuels in separate V4 countries Granger-cause changes in prices of fuels in other V4 countries. The cointegration research uses logarithmic prices, whereas causality investigation is based on their first differences. The results reveal long-term relationships between the prices of Pb95 gasoline in the Czech Republic and prices in other V4 countries as well as Granger causality flowing from diesel price changes in Poland to diesel price changes in other V4 countries and bilateral causation between changes in the prices of Pb95 gasoline in Poland, Hungary and Slovakia.
... Nevertheless, what is the true nature of the relationship, linear or non-linear, is yet debatable, where one school of researchers (e.g. Zhang & Wei, 2010) claim a linear relationship between oil prices and metals, others (e.g. Bildirici & Turkmen, 2015) argue a non-linear relationship between commodity variables. ...
... The literature on the interaction between oil and precious metal prices primarily highlights three significant association transmissions between oil and metal prices: First, oil-metal prices correlational association (Baffes, 2007), second, oil-metal prices long term causal association (Sari et al., 2010;Zhang & Wei, 2010), and third, the impact of oil prices shocks on metal prices (Hammoudeh & Yuan, 2008). Researchers such as Baffes (2007), Pindyck and Rotemberg (1990), study the correlational association between oil-metal prices and find a direct correlation between oil prices and commodity prices. ...
... Nevertheless, some of the studies (e.g. Sari et al., 2010;Zhang & Wei, 2010) find a fragile, null, or unidirectional association between oil and metal prices. For example, Zhang and Wei (2010) show a oneway relationship from oil to metal prices, whereas Sari et al. (2010) exhibit a highly insignificant bidirectional association between oil and metal prices. ...
This paper is to find how the existence of a long-run relationship between oil prices and metals prices evolved for the time from January 1979 to December 2017. The rolling-window autoregressive lag modeling (RARDL) testing approach of cointegration has been introduced and applied to assess the long-run relationship considering four rolling windows of 5, 10, 15, and 20 years. The empirical evidence concludes that for a small rolling window of 5 years, there is no evidence of the long-run relationship between oil prices and metals prices, i.e. gold, platinum, and silver. However, there is a long-run relationship between oil prices and steel prices from December 2003 to December 2014. At larger rolling windows of 10, 15 and 20 years, oil prices and gold prices are not cointegrated; however, steel, silver, and platinum have a long-run relationship with oil prices in different periods.
... Numerous studies examine the relationship between commodity markets by applying various techniques such as conditional correlations (e.g., Shivered, 2019), multivariate GARCH (Cabrera and Schulz, 2016;Kang et al., 2017), vector autoregressive (e.g., Barbaglia et al., 2020), cointegration (Zhang and Wei, 2010;Ciaian, 2011;Wang and Wu, 2012;Manera et al., 2013;Al-Maadid et al., 2017;Shiferaw, 2019), wavelets (e.g., Tiwari et al., 2020;Kirikkaleli and Güngör, 2021), copula (Koirala et al., 2015;Mensi et al., 2017;Ji et al., 2018;Mokni, 2018;Ji et al., 2018Ji et al., , 2018Yuan et al., 2020;Albulescu et al., 2020;Kumar et al., 2020), variance decomposition spillover (e.g., Dahl et al., 2020;Bouri et al., 2021), and cointegration and Granger causality test (Nazlioglu and Soytas, 2011;Kaltalioglu and Soytas, 2011;Coronado et al., 2018;Vu et al., 2019;Fernandes and Araújo, 2020;Talbi et al., 2020;Mokni and Ben-Salha, 2020;Youssef and Mokni, 2021). They generally consider returns linkages and volatility linkages separately, 3 and disregard evidence of time-variation in the Granger causal relationships for a long sample period. ...
... In this context, several approaches are considered. While the results are mixed, almost all studies reveal a significant relationship between these two markets, suggested generally by the attractiveness of precious metals as hedging and safe haven assets against energy price variations (Cashin et al., 2002;Baffes, 2007;Hammoudeh and Yuan, 2008;Lescaroux, 2009;Zhang and Wei, 2010;Sari et al., 2010;Charlot and Marimoutou, 2014;Reboredo and Ugolini, 2016;Behmiri and Manera, 2015;Yaya et al., 2016;Mokni et al., 2020, among others). However, there are some studies providing evidence of an insignificant linkage between oil and precious metals (Soytas et al., 2009)). ...
... While several methodological approaches are employed in the energy-precious metals nexus, Granger causality analysis takes an important place. For example, Zhang and Wei (2010) use the causality between the crude oil and gold markets over the period between 2000 and March 2008 and report evidence of a consistent trend between the prices of crude oil and gold. Yıldırım et al. (2020) investigate the return and volatility spillover effects between oil price and precious metal prices based on a time-varying causality-in-variance test. ...
The existing literature on the Granger causality between various commodity sector (energy, agriculture, and precious metals) indices remains limited in several aspects such as the long length of the sample period, time-variation in the causality relationship, and the examination of both returns and volatility. In the context of an enduring debate on the dynamic relationships among commodity sector indices during various turbulent and crisis periods, we use a long sample period, from January 1960 to May 2021, and detect changes in the relationships for both returns and volatility series via the application of the recent time-varying Granger causality test of Shi et al. (2020). The results show evidence of significant time changes in the relationships across all pairs of commodity sector indices, for both return and volatility and, in some cases, significant sequential feedback effects that are heterogenous across the three commodity sector indices. The evidence of temporary spillovers between energy and agriculture and energy and precious metals is more frequent than between agricultural and precious metals, especially for the return series. The time-variation and heterogeneity in the Granger causal relationships among the commodity sector indices do not always coincide with stress and crisis periods but can be seen in light of continuous developments and challenges in the commodity markets such as biofuel expansion and the financialization phenomenon.
... This argument is supported by the findings of Baffes (2007) who reports that a rise in the price of oil by $1 would result in a $0.34 increase in the gold price. Zhang and Wei (2010) show that the oil market and gold market are cointegrated at all maturities indicating that the markets are jointly efficient. Ewing and Malik (2013), using neural network methodology, confirm the association between oil and gold market. ...
... Ahmed and Huo (2021) find reciprocal shock transmissions between oil prices and the Chinese stock market, and one-way volatility spillovers from oil to gold. These findings support the results of Zhang and Wei (2010) who report that oil Granger causes gold price volatility. ...
... This finding suggests that the sensitivity of the gold (energy) market's current variance to past shocks from the energy (gold) market is (not) significantly different between pre-pandemic and pandemic periods. Similar to our results, Zhang and Wei (2010) find that oil linearly Granger causes gold price volatility, but not vice versa. Ewing and Malik (2013) report evidence of substantial volatility transmissions in two directions between gold and oil futures. ...
This study sets out to provide fresh evidence on the dynamic interrelationships, at both return and volatility levels, between global equity, gold, and energy markets prior to and during the outbreak of the novel coronavirus. We undertake our analysis within a bivariate GARCH(p, q) framework, after orthogonalizing raw returns with respect to a rich set of relevant universal factors. Under the COVID-19 regime, we find bidirectional return spillover effects between equity and gold markets, and unidirectional mean spillovers from energy markets to the equity and gold counterparts. The results also suggest the presence of large reciprocal shock spillovers between equity and both of energy and gold markets, and cross-shock spillovers from energy to gold markets. Most probably driven by the recent oil price collapse, energy markets appear to have a substantial cross-volatility spillover impact on the others. Our results offer implications for policymakers and investors.
... That attention has been re-enforced since early 2000s, consequently to the phenomenon of financialization of commodities. Despite the tremendous empirical literature (among others: Ahmadi et al., 2016;Ai et al., 2006;Antonakakis & Kizys, 2015;Bruno et al., 2017;Charlot et al., 2016;Ding & Zhang, 2020;Gardebroek & Hernandez, 2013;Green et al., 2018;Le Pen & Sévi, 2018;Mensi et al., 2014;Natanelov et al., 2011;Nazlioglu & Soytas, 2012;Pindyck & Rotemberg, 1990;Tang & Xiong, 2012;Yip et al., 2017;Zhang & Wei, 2010), the conclusions vary substantially. Indeed, while the literature recognizes that the linkages between commodity markets are asymmetric (see, e.g., Hammoudeh et al., 2010;Nazlioglu, 2011;Du et al., 2011; https://doi.org/10.1016/j.qref.2022.04.009 1062-9769/© 2022 Board of Trustees of the University of Illinois. ...
... Tang and Xiong (2012) find that the link between oil prices and the prices of non-energy commodity futures in the United States has increased since the early 2000 s, reflecting the financialization of the commodity markets. Moreover, this increasing correlation provides an explanation for the sharp increase in the volatility of non-energy commodity prices around 2008. Zhang and Wei (2010) analyze the cointegration relationship and causality between the crude oil market and the gold market from January 2000 to March 2008. Their results suggest a long-term equilibrium between the two markets, as well as a unidirectional Granger causality running from crude oil price to gold price. ...
... Methodologically, the literature studying the interdependence between commodity markets covers a very broad spectrum. Researchers have used several models and tools to explore the extent to which commodity markets are integrated or segmented: While some authors have used models belonging to the GARCH family (Cabrera & Schulz, 2016;Silvennoinen & Thorp, 2013), others have focused on causality (Bastianin et al., 2014;Ben Amar & Hachicha, 2021;Nazlioglu & Soytas, 2012;Zhang & Wei, 2010), others have relied on spillover models (Chevallier & Ielpo, 2013;Barunik & Krehlik, 2018;Barbaglia et al., 2020;Nakajima & Toyoshima, 2020), and others have used copulas (Albulescu, Tiwari, & Ji, 2020). The main aim of this paper is to detect the potential cyclical asymmetries in spillovers that may originate during bull and bear markets. ...
In this article, we introduce a new approach to investigate the asymmetric connectedness between different commodity markets. Indeed, we build on Barunik et al. (2016) and extend the connectedness framework of Diebold and Yilmaz (2012) by incorporating the cyclical components of the underlying variables. This new approach allows us to capture possible asymmetries in cyclical behavior in commodity market. Using this new method, we compute the connectedness between five commodity sectors: energy, agriculture, metals, precious metals, and fertilizers for a 60-year period (from January 1960 to March 2021). First, we find that the spillover between commodity market sectors selected become intense in political and financial crisis periods. Second, the connectedness between commodity markets is asymmetric, inequal, and clustering across the sample selected. Third, in tension periods, the magnitude of connectedness implied by bullish cycles is greater than that implied by bearish cycles.
... This further increases their prices in the same direction. Studies including Sari et al. (2010) and Zhang and Wei (2010) demonstrate this evidence in line with the strong connection between oil and commodity (gold) prices through US exchange rate. For instance, Sari et al. (2010) show that during the period of expected inflation that resulted in the weak U.S. dollar against other notable currencies, investors took shift to physical assets that are denominated in the U.S. dollar from soft assets that are denominated likewise. ...
... For instance, Sari et al. (2010) show that during the period of expected inflation that resulted in the weak U.S. dollar against other notable currencies, investors took shift to physical assets that are denominated in the U.S. dollar from soft assets that are denominated likewise. Similarly, Zhang and Wei (2010) establish strong causality linking oil and gold prices through exchange rate. ...
This study examines the transmission of volatility risks between the EU carbon market and various commodity and financial markets across different frequency bands, while accounting for the role of the U.S. economic policy uncertainty (EPU). Our findings show that the connectedness between the carbon market and others is non-trivial and heterogeneous. In particular, the volatility connectedness increases as frequency cycle increases, indicating that risks transmission is most intense when assets are held for a longer-time. On average, the carbon, gold and the U.S. currency markets are net receivers of shocks, and the carbon market is further revealed to be a net receiver of shocks from all other markets except the copper and the U.S. currency markets at higher frequency cycle. Finally, we establish that the U.S. EPU is a notable driver of the connectedness between the carbon market and each of the remaining markets.
... Similar significance can also be attributed to crude oil for an economy (Berument and Taşçı, 2002;Cheng et al., 2019;Huntington, 1998). Furthermore, both can also have a joint impact on the economy (Zhang and Wei, 2010;Rastogi, 2016b), as crude oil and gold are very important commodities in financial market. ...
... Both gold and crude oil seem to have been the part and parcel of many scholarly investigations (Joshi, 2012;Pandey and Vipul, 2018;Zhang and Wei, 2010); however, the linking of both with the macroeconomic indicators in the literature is relatively less (Chang et al., 2013). There is abundance of literature on the volatility effect between the pairs of markets, but studies on connectivity of gold and crude oil to interest rates are scarce (Apergis et al., 2019). ...
Purpose
Crude oil, gold and interest rates are some of the key indicators of the health of domestic as well as global economy. The purpose of the study is to find the shock volatility and price volatility effects of gold and crude oil market on interest rates in India.
Design/methodology/approach
This study finds the mutual and directional association of the volatility of gold, crude oil and interest rates in India. The bi-variate GARCH models (Diagonal VEC GARCH and BEKK GARCH) are applied on the sample data of gold price, crude oil price and yield (interest rate) gathered from November 30, 2015 to November 16, 2020 (weekly basis) to investigate the volatility association including the volatility spillover effect in the three markets.
Findings
The main findings of the study focus on having a long-term conditional correlation between gold and interest rates, but there is no evidence of volatility spillover from gold and crude oil on the interest rates. The findings of the study are of great importance especially to the policymakers, as they state that the fluctuations in prices of gold and crude oil do not adversely impact the interest rates in India. Therefore, the fluctuations in prices of gold and crude may generally impact the economy, but it has nothing to do with interest rate in particular. This implies that domestic and foreign investments in the country will not be affected by gold and crude oil that are largely driven by interest rates in the country.
Practical implications
Gold and crude oil are two very important commodities that have their importance not only for domestic affairs but also for international business. They veritably influence the economy including forex exchange for any nation. In addition to this, the researchers believe the findings will provide insights to policymakers, stakeholders and investors.
Originality/value
Gold and crude oil undoubtedly influence the exchange rates but their impact on the interest rates in an economy is not definite and remains ambiguous owing to the mixed findings of the studies. The lack of studies related to the impact of gold and crude oil on the interest rates, despite them being essentials for the health of any economy is the main motivation of this study. This study is novel as it investigates the volatility impact of crude oil and gold on interest rates and contributes to the existing literature with its findings.
... For example, Escribano and Granger (1998) focused solely on the relationship between silver and gold. In contrast, Zhang and Wei (2010) focused on the relationship between oil and gold prices. To better understand gold price action, multiple variables must be examined, and few studies have set out to achieve this. ...
... Shafiee and Topal (2010) found a high correlation between crude oil price and gold. The positive correlation was further supported by Zhang and Wei (2010), who found that gold price followed a similar trend when oil prices soared. The resulting finding was consistent with Sari times, respectively. ...
Gold price research has attracted considerable attention in recent years due to the sharp increasing price trend. The price of gold hit an all-time high in August 2020 as the price increased by more than 30 per cent since January despite the COVID pandemic. The main objective of this research is to examine the determinants of gold
price by investigating the relationship of the four key influencing variables affecting the price of gold; the US dollar, inflation, crude oil and silver.
Prior studies have primarily focused on one variable. This study contributes to research and practice by producing a comprehensive analysis of various factors and their relationship with gold to gain an in-depth understanding of the driving forces that influence the price of gold. To advance the current literature, quantitative methods were used. Data from 1979 to 2019 was used to calculate the mean,
median, maximum, minimum and range for both the dependent and independent variables. A linear regression analysis was then conducted to investigate the correlation between the independent variables and gold price.
Keywords: Gold price; Oil Price; Linear Regression; Inflation.
... Oil And Gold Are Related Through Different Channels. First, The Oil-Gold Linkage May Be Explained Through The Inflationary Shocks Kilian (2009), Kilian and Park (2009), Kilian and Murphy (2012), Ready (2018), And Baumeister and Hamilton (2019) Have Shown That Oil Price Shocks Can Be Distinctly Decomposed Into Oil Demand, Oil Supply And Global Demand Shocks With Asymmetric Effect On Macroeconomic Variables (Guerrero-Escobar et al., 2018;Uddin et al., 2018, Das et al., 2018Climents Et Al., 2019;Salisu and Adediran, 2020;Mokni, 2020;Mokni et al., 2020 (Reboredo, 2013;Sadorsky, 2014;Turhan et al., 2014;Ftiti et al., 2016;Kumar, 2017;Mo et al., 2018;Mokni, 2018;Sephton and Mann, 2018;Khalfaui, 2018;Jin et al., 2019;Salisu et al., 2019c;Das et al., 2018;Salisu and Adedrian, 2020 (Baffes, 2007;Kilian, 2009;Zhang and Wei, 2010;Turhan et al., 2014;Mokni, 2018 (Kim et al., 2008). In Such Circumstances, Models Incorporating Regime-Switching Outperform Regime-Independent Models (See Hamilton, 1989;Fong and See, 2002;Lee and Chen, 2006 ...
Previous Studies Examining The Oil-Gold Nexus Do Not Account For The Oil And Gold Markets Conditions Jointly. This Study Examines The Oil-Gold Price Relationship By Considering The Switching-Regime In Oil Price Shocks And The Gold Market Conditions. The Different Sources Of Oil Shocks (Supply, Demand, And Risk Shocks) Are Identified Based On The Recent Procedure Of Ready (2018), While The Effect Of Oil Price Shocks Is Investigated Based On A New Approach That Combines The Regime-Switching And Quantile-Regression Models. Results Suggest That The Response Of Gold Returns To Oil Price Changes Depends On The Forces-Driven Oil Shocks And The Gold Market Conditions. Moreover, Gold Price Responds More Intensively To Demand Shocks Rather Than Supply And Risk Shocks. We Also Confirm That The Gold Price Reaction To Oil Shocks Changes In Sign, Magnitude, And Significance According To Oil Price Shocks Regimes And Gold's Conditional Distribution. These Findings Have Important Implications For Investors Regarding The Hedging And Safe-Haven Role Of Gold Against Oil Shocks.
... A causal relationship between these two macroeconomic indicators is observed in many predominant research works [80]; [81]; [82]. An effective relationship is established by [83]; [84]; etc. The absence of any interdependencies among oil and gold prices is validated. ...
In the modern era of Friedman's Flat World, investments are neither confined to a single economy nor to one type of investment. These days' global investors seek investment across the different countries and explore the diverse markets, including equity, commodities, currency, and oil markets around the globe. Interdependencies among these key markets play a vital role in the success or ultimate failure of any such markets. This current research paper focuses on demonstrating the interlinkages between Crude and Gold rates, forex-rates and equity prices of the emerging Indian economy from 1993 to 2019 by deploying the cointegration tests given by Johansen and tests of unrestricted univariate Vector Autoregressive Model (VAR). The results failed to establish any kind of cointegration amongst the selected macroeconomic variables. Further, the results positively confirm the significant influence of the gold and equity prices on the crude-oil rates; the noticeable significant influence of crude and forex on the gold-prices; and marked the effect of forex-rates and equity-prices on each-other.
... However, some studies arrived at different conclusions. The Engle-Granger test (Zhang, Wei 2010), exponential smooth transition autoregression (Bahmani-Oskooee et al. 2008) and quantile unit root (Ma et al. 2017) were employed to test PPP and were found not to hold true. Based on the aboveconfused -results, the authors had to adopt a more powerful method to detect whether PPP holds or not. ...
... However, Yiew et al. (2019) suggest that the adjustment process between the exchange rate and the oil price is constant for a certain period that facilitates the prediction of the direction of exchange rate movement. Zhang and Wei (2010) suggest long-term equilibrium between the oil and gold markets where the change in the prices of former linear Granger causes the volatility of the gold prices. Simakova (2011) established the existence of a long-term relationship between gold and oil prices. ...
This research study empirically examines the price linkages among oil, dollar, gold and stock markets in India over period from 1999:1 to 2019:12. We employ cointegrated vector error correction model (VECM) and Granger causality test to study the long-run and short-run relationships between commodity and financial markets before, during and after the global financial crisis. Our analysis finds the dependency on price movements in asset markets is time-varying and countercyclical in India. Findings suggest the asymmetric structure of price correlations among asset markets across three temporal periods on either side of the crisis. Our study offers useful insights into the strategic asset allocations to investors in response to economic cycles, to help optimise potential portfolio returns and provide protection towards some downside risks.
JEL Codes: C58, D53, F51
... Besides its pronounced impact on economic activi-Energies 2021, 14, 3752 3 of 13 ties [10][11][12], the crude oil price volatility also exerts a great impact on commodity markets. For instance, Zhang and Wei [13] report a unidirectional Granger causality running from crude oil to gold prices. Esmaeili and Shokoohi [14] demonstrate the effects crude oil price has on food prices by means of principal component and Granger causality analysis. ...
China is a global leader in methanol production volume, while coal is a major feedstock. The country also has the world’s largest commercial coal-to-methanol operations. Coal-based methanol is used widely within China and is a competitive substitute for gasoline. Owing to this, it is plausible that the price of coal may be linked to international crude oil prices, with methanol prices serving as the connecting channel. We add supporting evidence to a recently emerging area of literature and observe statistically significant relationships among the three prices, and, therefore, the influence from international crude oil and methanol prices on the coal price determination in China. This paper investigates the relationships among these prices for the period from January 2010 to December 2019 through spectral Granger causality analysis, alongside more traditional cointegration tests to develop a comprehensive picture of causal association between the price series in both the frequency and time domain. Cointegration is found in our tri-variate system while the frequency domain Granger causality tests reveal the long-run causality in all directions except from crude oil to methanol, thus, emphasizing the structure of coal price dependence. According to the generalized impulse response functions, the coal price reacts positively to shocks in crude oil prices.
... Since gold is a financial item against inflation, the demand for gold tends to increase, which is followed by the growth of oil prices. Such an example exhibits the dependency of inter-sector relationship between oil and gold sectors [31]. Nevertheless, it is difficult to represent such prior knowledge on which sector-sector pairs are with high dependency in stock price movement. ...
Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort is made for profitable stock recommendation. Besides, in existing approaches on modeling time series of stock prices, the relationships among stocks and sectors (i.e., categories of stocks) are either neglected or pre-defined. Ignoring stock relationships will miss the information shared between stocks while using pre-defined relationships cannot depict the latent interactions or influence of stock prices between stocks. In this work, we aim at recommending the top-K profitable stocks in terms of return ratio using time series of stock prices and sector information. We propose a novel deep learning-based model, Financial Graph Attention Networks (FinGAT), to tackle the task under the setting that no pre-defined relationships between stocks are given. The idea of FinGAT is three-fold. First, we devise a hierarchical learning component to learn short-term and long-term sequential patterns from stock time series. Second, a fully-connected graph between stocks and a fully-connected graph between sectors are constructed, along with graph attention networks, to learn the latent interactions among stocks and sectors. Third, a multi-task objective is devised to jointly recommend the profitable stocks and predict the stock movement. Experiments conducted on Taiwan Stock, S&P 500, and NASDAQ datasets exhibit remarkable recommendation performance of our FinGAT, comparing to state-of-the-art methods.
... Like gold, crude oil enters the safe-haven currency system as a physical asset, so there is a correlation between the crude oil market and the gold market [33,34]. Zhang and Wei [35] found that the relation between oil and gold prices is positive. Although the correlation only exists in the short term [36], interest rates influence the US dollar, which in turn influences international crude oil prices. ...
Since the commodity and financial attributes of crude oil will have a long-term or short-term impact on crude oil prices, we propose a de-dimension machine learning model approach to forecast the international crude oil prices. First, we use principal component analysis (PCA), multidimensional scale (MDS), and locally linear embedding (LLE) methods to reduce the dimensions of the data. Then, based on the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models, we build eight models for predicting the future and spot prices of international crude oil. From the analysis and comparison of the prediction results, we find that reducing the dimension of the data can improve the accuracy of the model and the applicability of RNN and LSTM models. In addition, the LLE-RNN/LSTM models can most successfully capture the nonlinear characteristics of crude oil prices. When the moving window size is twenty, that is, when crude oil price data are lagging by almost a month, each model can minimize its error, and the LLE-RNN /LSTM models have the best robustness.
... siliverstovs, l'hégaret, neumann, and von hirschhausen (2005) showed a high level of integration within the european/Japanese and north american markets natural gas and oil prices while the Japanese and the north american markets were not integrated. additionally, Zhang and Wei (2010) showed consistent correlation and long-term equilibria between gold and crude markets. similarly, singh and sharma (2018) suggested that a long-term relationship exists between crude oil, gold prices, the us dollar and stock markets only prior to the recession, while crude oil and the stock market displayed a causal relationship during the recession. ...
The study evaluated the interlinkages and diversification opportunities in the context of emerging bond markets from 2007:1 to 2020:5, using the vector autoregressive (VAR) model and sub‐period analyses to compare BRIC (2007:1–2010:11) and BRICS (2010:12–2020:5) regimes. As indicated by the breaking unit‐root test, dummies for the global financial crisis and COVID‐19 were incorporated in the analyses. VAR results showed that the Indian bond market responds positively to the previous change in the Chinese bond market during the BRIC era while BRICS bond markets are mostly uninfluenced by prior behavior patterns of one another. These suggested that the diversification opportunity has been increased following the admission of South Africa to the league. In addition, variance decomposition and impulse response provide proofs to suggest that BRICS bond markets are more exogenous and independent compared to what is obtained during the BRIC period. Consequently, the authors concluded that the BRICS bloc has provided greater diversification opportunities for emerging markets’ bondholders in the recent past.
... On the contrary, other empirical studies concluded in the opposite direction, indicating that changes in exchange rate Granger cause oil price volatility. For instance, a study by Zhang and Wei (2010) documented a significant co-integration relationship between oil price and exchange rate and found return causality from dollar exchange rate to oil price but not in the opposite direction. An earlier study by Sadorsky (2000) examined empirically the relation connecting future prices of crude oil and exchange rate dynamics. ...
This paper examines the relationship and related causality patterns of oil price volatility and exchange rate volatility of a group of oil-dependent economies before and after the 2008-2009 global financial crisis. We employed weekly time-series data of oil price and exchange rates for 2000-2007 (pre-crisis) and 2010-2016 (post-crisis). United States dollar exchange rates are for Ghanaian cedi, Nigerian naira, Russian ruble, Indian rupee, South African rand, and the Euro. To investigate the volatility impacts that exist between oil price and exchange rates during both sub-sample periods, we merged Vector Autoregressive (VAR) with GARCH and EGARCH models in the form of Bivari-ate VAR-GARCH and VAR-EGARCH. We further adopted the Toda-Yamamoto causality test to investigate related causality patterns. Empirical findings revealed both bidirec-tional and unidirectional relationship between oil price volatility and the exchange rates volatility of four out of the six oil-dependent economies considered for the study. These findings were more prevalent in the post-crisis period than the pre-crisis period. We also confirmed both bidirectional and unidirectional causality pattern between oil price volatility and exchange rate volatility of the same four currencies as observed with the VAR results in both sub-sample periods.
... This indicates that before COVID-19, exchanges rates improve oil price returns. This finding supports studies such as Hiemstra and Jones (1994); Kumar (2019); Sadorsky (2000); Zhang and Wei (2010); Reboredo (2012); Salisu et al. (2020); Devpura (2021); Prabheesh and Kumar (2021); Candila et al. (2021). For example, Salisu et al. (2020) claimed that oil price is a good predictor of exchange rate returns in BRICS (Brazil, Russia, India, China and South Africa) economies. ...
This paper re-examines the performances of stock prices, oil prices and exchange rates in twelve oil exporting countries amidst the ravaging consequences of the ongoing worldwide coronavirus pandemic. Consequently, the study adopted a panel Vector Autoregressive (pVAR) model which applied data from the pre- and post-COVID-19 periods. Contrary to the pre-COVID-19 pandemic period, the pVAR Granger causality test indicates that the stock market can as well affect the exchange rate market, though positively. Furthermore, the Impulse response functions (IRFs) shows that a shock to crude oil prices provokes a negative response by exchange rates in the post-COVID-19 pandemic era only. The Forecast Error Variance Decomposition (FEVD) estimates that such innovations to crude oil prices account for the varying fluctuations in exchange rates and stock returns at different periods, but is neither influenced by the stock market activities nor the exchange rate market in the post-COVID-19 pandemic era. This suggests that before COVID-19, the different markets in the selected oil producing economies were only affected by their market fundamentals and dynamics only, but this changed with the plummeting oil prices in the COVID-19 pandemic era. The development of vaccines and the immediate vaccination of the world people will ease the lockdowns and increase the demand for crude oil by the high oil importing countries. With the improved earnings from this, and the associated appreciation of the local currencies against the US dollars, the capital market activities of these net oil exporting countries improve. Policy makers and investors should consider the dynamics in the oil market while making decisions.
... O ouro possui importância histórica consolidada na ideia de poder geopolítico, assim como, exerce influência sobre a geoeconomia global, possivelmente ficando atrás do petróleo, dentre os recursos naturais. Do mesmo modo, os processos que lhe confere o preço cotado em bolsa também estão relacionados aos processos globais geopolíticos e geoeconômicos (KAUFMANN; WINTERS, 1989;SHAFIEE;TOPAL, 2010;WEI, 2010;BAUR, 2011). O ouro opera como uma commodity comum, negociado em bolsas de mercadorias e futuros, mas apresenta a peculiaridade de além de servir como mercadoria e matéria-prima para as indústrias, também deter valor monetário, que lhe aufere maior grau de importância econômica, simbólica, além de sentido estratégico. ...
A mineração de ouro foi um importante elemento da formação histórica e geográfica da Amazônia brasileira contemporânea. Apesar das primeiras descobertas remontarem ao período colonial, o metal aurífero amazônico só ganhou notoriedade nacional e expressividade regional quando milhares de indivíduos migraram rumo aos garimpos de ouro no fim do século XX. O sonho do eldorado e a esperança de enriquecimento rápido e repentino moveram as frentes garimpeiras sobre a densa floresta tropical e as terras ocupadas por povos tradicionais. As descobertas de depósitos auríferos e os intensos fluxos migratórios deram origens a cidades e municípios, dinamizaram antigos núcleos urbanos, deixaram uma massa de trabalhadores no campo e nas cidades e também atraíram grandes mineradoras.
Junto a outras frentes e processos socioespaciais, o garimpo contribuiu para ocupar a fronteira demográfica e consolidar o espaço regional. Todavia, mesmo com a adoção de políticas e medidas pelo Estado brasileiro, com finalidade de industrializar a extração do ouro a partir dos anos 1990, o garimpo não desapareceu, como muitos acreditam, nem tampouco foi plenamente substituído pela expansão da mineração industrial. O garimpo se mecanizou, passou a demandar menos mão de obra e, principalmente, se tornou um problema social, sendo criminalizado e reprimido pelo Estado. A região amazônica atual, especialmente a porção meridional, também apresenta novas feições, mais moderna, conectada por redes de transporte e comunicação, e planejada por diversos atores de múltiplas escalas geográficas, que almejam distintos e incompatíveis projetos para a região e seus recursos.
A presente tese analisa, a partir da porção meridional, as mudanças na geografia do ouro e a participação da mineração aurífera – garimpeira e industrial - na organização do espaço amazônico brasileiro ao longo do tempo, demonstrando as alterações e transformações frente aos diferentes contextos regionais, nacionais e globais.
... The second channel stems from the fact that the US Dollar denominates the prices of crude oil and industrial metals. 1 Given the common denomination, a depreciating dollar, for example, boosts the purchasing power of global consumers, which, in turn, induces the demand for these commodities, increasing their prices simultaneously. Studies supporting this perspective are Sari et al. (2010) and Zhang and Wei (2010). The third one is the interest rate channel. ...
This paper examines the safe-haven and hedging potential of oil and gold against industrial metals and agricultural commodities using a novel approach of quantile-on-quantile regression (QQR). For empirical analysis, we use the data on these commodities from January 2000 to December 2018, which further splits up into two sub-periods based on the global financial crisis (GFC). The results from the time-varying correlation of oil (gold) with metals and agriculture commodities suggest that oil (gold) has a lower correlation with metals and agriculture in the pre-GFC period than post-GFC. Further, the QQR model for two time periods (pre-GFC and post-GFC) was used to examine whether oil (gold) serves as a hedge (safe-haven) during the two periods. We conclude that oil was a safe-haven for metals and agricultural commodities pre-GFC but lost that ability post-GFC. Finally, we analyze the hedge ratio and hedge effectiveness pre- and post-GFC and confirm that oil had higher hedge effectiveness than gold during the pre-GFC period.
... As gold and oil are the commodities traded most commonly in the derivatives markets, the entire market dynamics and movements in prices of these two commodities are supposed to have important ramifications for the financial markets during the current pandemic of COVID-19. Finally, we observe a large body of literature that looks into the linkages among gold price, oil price and stock price (Bedoui et al., 2019;Ewing & Malik, 2013;Narayan et al., 2010;Soytas et al., 2009;Zhang & Wei, 2010). However, the nexus between commodity markets and financial markets, in particular, is not much explored, specifically in the emerging market context like India, in the face of the recent pandemic. ...
This chapter explores the relationship between stock and commodity prices in the derivatives market, in the Indian context. In order to estimate the long-term relationship, ARDL model is employed on daily data during the period of 2017–2020. The chapter also incorporates the impact of market disruptions on the relationship, following the recent COVID-19 pandemic. The findings indicate that the stock returns and the prices of crude oil and gold are closely related. Interestingly, findings also suggest that the pandemic has altered the relationship. For example, there was no evidence of cointegration among the stock, gold and crude oil prices during the pre-COVID period. However, post-pandemic, evidence of cointegrating relationships exists. Apart from that, some interesting insights from the short-run relationship between the two markets include a mutual influence on each other in the pre-pandemic period; e.g. stock returns are determined by past values of gold and oil price, whereas stock market returns affect volatility in oil price. However, during the COVID period, volatility of gold prices, in addition to crude oil prices, seems to be driving the stock returns.
... Some scholars have failed to consider the impact of energy price fluctuations on metal prices (Chang et al., 2013;Soliman and Nasir, 2018). Others found that there exists co-movement or the bilateral relationship between metal and energy markets (Wang and Chueh, 2013;Zhang and Wei, 2010). For instance, Zhang and Tu (2016) provided supports to the result that the crude oil price has a symmetric impact on metal prices. ...
The study investigates static and dynamic returns spillover effects between metal (gold, silver, copper and aluminum), energy (oil, natural gas and coal) and carbon markets in different frequency domains using the Diebold Yilmaz (2012) and the Baruník and Křehlík (2018) method. The results show that total connectedness in the post-COVID world is significantly higher compared to pre-COVID-19 outbreak period. The total spillover is contributed mainly by short-term spillover effects. Moreover, metal markets especially copper and silver have higher explanatory power. Spillover within markets is stronger than across these markets. In addition, the carbon market is more heavily interactive with other markets, and the metal market especially copper has relatively high explanatory power for the carbon price fluctuations in post-COVID-19outbreak periods. According to the net spillover, copper and gold has a hedge function in the short- and long-term, respectively. Furthermore, the relationship among these markets is time-varying, affected by market uncertainty such as the outbreak or major events.
... In her study, she showed evidence of an existing long-term relationship between gold and crude oil markets, and after market fluctuations, both times, the series returned to the long-term equilibrium. Furthermore, Zhang and Wei [24] reported that there was an impact of oil on gold prices but not the opposite. On the other hand, Soytas et al. [25] found evidence that oil prices did not determine the precious metal prices. ...
The United Nations Framework Convention on Climate Change Paris Agreement has been announced as a crucial step towards combating the global threat of climate change. In the light of ambitious plans for further renewable energy sources development, high demand for nonenergy materials critical for RES is greatly expected. In conclusion, future energy security will be surely based on nonenergy commodities critical for them. As this article directly relates to issues related to new technologies and energy security in new form, the main purpose of this study is to examine the impact of energy commodity prices, namely crude oil, natural gas and coal prices on selected metal prices such as aluminium, chromium, cobalt, copper, lead, nickel, silver, tin, or zinc, both before and over the Paris Agreement period. We are looking for new insights in terms of relationships between traditional fossil fuels and metals used in clean energy technologies potentially established or strengthened shortly after the Paris Agreement was adopted. Currently, the analyses of the impact of institutional conditions such as global agreements (institutional factors) on the emerging or strengthening of relationships between energy and nonenergy resources are very limited. Hence, an autoregressive distributed lag and error correction model are employed.
... It is the beginning ingredient for the majority of the items we use most often, ranging from petroleum products to polymers. Crude oil price changes have a significant effect on world markets; hence, pricing analysis can help to mitigate the risks connected with economic fluctuations [2]. Price projections are critical to a variety of stakeholders, including legislatures, public and private organizations, politicians, and investors. ...
... e primary reasons include shifting the price fluctuation risk through hedging and performing the price discovery function and price reference for the spot market [29,30]. Futures markets serve risk transference and price discovery functions to the economy [31][32][33][34][35]. e presence of speculators facilitates a risk transference function by buying a futures contract from hedgers. ...
Price discovery function analyses the dynamics of futures and spot price behavior in an asset’s intertemporal dimensions. The present study examines the price discovery function of the bullion, metal, and energy commodity futures and spot prices through the Granger causality and Johansen–Juselius cointegration tests. The Granger causality test results show bidirectional causality between the spot and futures returns for gold, silver, aluminum, lead, nickel, and zinc. The Johansen cointegration test shows that spot and futures prices are in the long-run equilibrium path for silver, aluminum, lead, nickel, zinc, crude oil, and natural gas. The vector error correction model results suggest that both the spot and futures markets are equally efficient in price discovery for the nickel. The spot market leads the futures market in price discovery for copper and zinc. However, the futures market leads the spot market in price discovery for silver, aluminum, and lead. The findings of the study suggest the market participants for implementing hedging and arbitrage strategies. It also helps the market regulators to examine the stability of these rapidly growing commodity futures markets in India.
... Furthermore, , for example, showed that the fear of the coronavirus (measured as the google search volume on this topic) is a valuable variable to predict stock price changes around the world. Moreover, , Zaremba et al. (2020), Zhang et al. (2020), Gao et al. (2021) and Mazur et al. (2021) allude that all crises, including the COVID-19 pandemic, have one common feature, i.e., extreme market volatility (Zhang and Wei 2010;Kang et al. 2017). Stock market volatility has been a topic of interest in the academic literature, since stock market volatility is a key feature for option pricing, financial market regulation, investment or hedging decisions (Poon and Granger 2003;Chen et al. 2019;Shiba and Gupta 2021), so that many papers attempt to predict stock market volatility. ...
In the context of the great turmoil in the financial markets caused by the COVID-19 pandemic, the predictability of daily infectious diseases-related uncertainty (EMVID) for international stock markets volatilities is examined using heterogeneous autoregressive realised variance (HAR-RV) models. A recursive estimation approach in the short-, medium- and long-run out-of-sample predictability is considered and the main findings show that the EMVID index plays a significant role in forecasting the volatility of international stock markets. Furthermore, the results suggest that the most vulnerable stock markets to EMVID are those in Singapore, Portugal and The Netherlands. The implications of these results for investors and portfolio managers amid high levels of uncertainty resulting from infectious diseases are discussed.
This study examines the quantile relationships among silver, gold, gold mining, oil and energy sector uncertainty indexes. Using a quantile cross-spectral approach, results show that the uncertainty indexes have a time- and quantile-dependent structure. Moreover, the extent of dependence is higher at the long term than at the short- and medium-terms, irrespective of time horizons. Gold and silver show negative and positive short-term dependence during bearish and bull market conditions, respectively, at two-day trading. The dependence switches to positive beyond two trading days. We find asymmetric dependence between crude oil and energy sector uncertainty indexes at two-day trading. The dependence is positive at medium and long terms. Furthermore, the magnitude of dependence between metal (gold, gold mining and silver) and energy uncertainty indexes (crude oil and energy sectors) is sensitive to market conditions.
We aim at facilitating risk management in the oil market by identifying the long-run gold-oil dynamic correlation using the DCC-MIDAS approach. We regard economic uncertainty and dollar realized volatility as the low-frequency driving forces of the correlation movement. The findings are in favor of gold as a diversifier rather than a hedge for oil as the gold-oil long-run correlation on average is positive and not perfectly correlated. However, the negative gold-oil correlation observed after 2020 may cast new light in the field of study. We support that gold is a safe haven for oil market participants under economic uncertainty in the long run. We also suggest the oil market participants better reduce their gold holdings under the threat of dollar fluctuation.
Unsupervised machine learning can interpret logarithmic returns and conditional volatility in commodity markets. This article applies machine learning in order to visualize and interpret log returns and conditional volatility in commodities trading. We emphasize two classes of unsupervised learning methods: clustering and manifold learning for the reduction of dimensionality. We source daily prices from September 18, 2000 through July 31, 2020, for precious metals, base metals), energy commodities and agricultural commodities. Our results highlight that at the very least, returns-based clusters conform more closely to traditional boundaries between precious metals, base metals, fuels, temperate-climate agricultural commodities, and tropical agricultural commodities. On the other hand, volatility-based clustering succeeds in identifying periods of extreme market distress, such as the global financial crisis of 2008–09 and the Covid-19 pandemic.
Gold is usually regarded as having the potential to hedge or to act as a safe haven in the financial market. Does this follow onto the oil market and if so at what frequencies and to what extent? To answer this we integrate a two-stage framework to investigate the nonlinear oil-gold relationship using the GARCH-EVT-VaR model and the continuous wavelet transform. We also explore the multiscale robust economic determinants of gold's hedging intensity for oil using extreme bound analysis (EBA). The result shows that gold could hedge against oil price fluctuations across time horizons on nearly half of the occasions. The stock market is found to be the most robust determinant but the influencing strength is small. Interest rates have a strong impact on gold's hedging property but are not robust in some time scales. Further, the strength of influencing factors in short-term time horizons is relatively larger than it in long-term time horizons. Moreover, gold could also provide strong safe-haven power against the extreme oil price movements during about half of the cases. And the safe-haven capability of gold versus extreme oil prices has relatively better performance in medium-and long-term time horizons.
This paper utilises a trivariate VAR-BEKK-GARCH model to investigate the dynamic relationships between global oil price, gold price, and European stock markets. This paper observes weak return spillover effects from the oil market to 6 European stock markets (Netherlands, Lithuania, Portugal, Czech Republic, Romania, and Slovenia) and from gold to Iceland, while there is no evidence of return spillovers from stock markets to oil and gold. The non-existence of return linkages between gold and stock (oil) suggests that the gold market plays a haven role. With reference to volatility spillovers, the results show obvious asymmetric bidirectional volatility interaction between the European stock markets and the global oil/gold markets. Stronger shock and volatility contagions from the European stock market to both oil and gold markets are observed compared with the opposite direction. For the volatility nexus between oil and gold, weak and moderate evidence of shock and volatility transmission from gold to oil markets is reported. Additionally, the study documents important and effective empirical implications for portfolio management and investment hedge strategies: firstly, adding European stock markets to a diversified oil/gold portfolio can achieve the expected returns while reducing risk; and secondly, the European investors can use the gold and oil markets to hedge against their stock market portfolio.
While the literature is obviously flooded with studies on the nexus between oil and metal market, the volatility connectedness dynamics that measures the forward and backward spillover linkage between different classes of oil price shocks and the metal market remains understudied. Thus, little is known about the feedback risk transmissions between individual components of oil shocks and globally traded metals. Extending the recent work of Mokni et al. (2020) which partly addresses this concern, we find strong connectedness between the four different types of oil shocks considered and five precious and industrial metals. Besides, all the oil shocks serve as net pairwise receivers of volatility spillovers, showcasing the increased financialization of oil, but its inability to hedge investors in all the metals. In entirety, gold is a net shock receiver in the system, indicating its inappropriateness to provide diversification benefits for other assets. Another important discovery is that the strong connectedness is found to be driven by economic policy uncertainty both for causality-in-conditional mean and causality-in-conditional variance. Appropriate policy suggestions are drawn accordingly.
We empirically examine the connectedness between the oil market and the major precious metals (gold, silver platinum, palladium) and the base metal copper over the available period covering December 06, 1988 to August 31, 2018, using the wavelet coherence and quantile cross-spectral analyses. The major findings can be summed up as follows. First, regarding the connectedness among the considered precious metal markets, there is no evidence of identifying the causal interplays. Second, gold and platinum are highly connected with oil and they influence oil prices, especially during global markets' turmoil. Third, the cross-quantile coherency analysis shows that gold and silver exhibit the highest quantile coherency. Fourth, the intensity of the nexus between gold and copper attains its nadir at the extreme upper and lower quantiles. Our empirical findings have relevant implications for investors. It is important for investors in commodity markets to make appropriate investment market decisions in extreme market circumstances. On the other hand, policymakers may benefit from our research, as they could generate suitable strategies.
This paper uses the high-frequency oil price after ensemble empirical mode decomposition, combines crude oil stocks and the US dollar index to construct co-movement threshold networks according to different periods divided by multiple structural breakpoint method. The results show that in the period of sharp fluctuations in oil prices, the inverse relationship between the US dollar index and high-frequency oil prices is more obvious, and the co-movement network of the three is closer to the “small world network”. After oil prices returned to stable fluctuations, coupled with the weakening role of the United States in leading the global economy, the reverse relationship between the US dollar index and high-frequency oil prices has gradually weakened, and the reverse relationship between stocks and high-frequency oil prices has become increasingly prominent. The research not only reveals the main co-movement characteristics of the three, but also provides a new idea for future research on oil price fluctuations and its influencing factors.
Oil price shocks and economic policy uncertainty are the two main drivers of many macroeconomic and financial variables. In the context of commodity financialization, these two shocks are more interrelated and even have a combined effect on the precious metals market. Therefore, using the time-varying parameter vector autoregression (TVP-VAR) framework, we actively analyze the dynamic impacts of oil price shocks and economic policy uncertainty on precious metal returns using monthly data from April 1990 to April 2018. The results show that oil price shocks had positive effects on precious metal returns before the international financial crisis, while these impacts have been negative since the international financial crisis. The impacts of economic policy uncertainty on precious metal returns change over time and are positive in most cases. The effects of oil price shocks on precious metal returns are amplified by economic policy uncertainty. In the field of transmission channels of economic policy uncertainty, we find that news uncertainty and inflation uncertainty are the most significant. In addition, during a major economic crisis or emergency, we discover some evidence of overreactions in the precious metal markets.
To investigate the nonlinear interactions among the international oil price, the RMB exchange rate and China's gold price, we construct a Markov-switching vector autoregression (MS-VAR) model that contains the three aforementioned variables and conduct an empirical study with monthly data from July 2005 to December 2017. The results verify that the nonlinear interactions exhibit regime switching, which can be precisely represented using three regime states. A significant momentum effect exists between the international oil price and the RMB exchange rate. China's gold price is mainly affected by international factors, such as the international oil price, while the influence of the RMB exchange rate on China's gold price is limited. Additionally, the dynamic interactions among the three variables vary in different regimes and have obvious asymmetric effects.
Crude oil, “the blood of industry” plays an irreplaceable role in the field of industrial production. The fluctuation in oil prices directly affects metal mining and processing, which in turn affects the dynamic development of the nonferrous metal market. This study examines the dynamic connection between international crude oil and China's nonferrous metal market using a change-point detection copula method. Under hypothesis testing, the change points in the dynamic connection are well detected using our method. The results show that the occurrence of change points may be related to the production policy of the Organization of Petroleum Exporting Countries (OPEC). Information about the connection before the change points will obscure the useful information, resulting in inaccurate knowledge about the dynamic connection. In addition, the connection has a profound impact on return forecasts. The test results based on sample data from the post-change point are more likely to accept the null hypothesis. This accuracy of the return forecasts based on sample data from the post-change point is attributed to the exact establishment of the dynamic connection between crude oil and nonferrous metal market. When returns are forecast accurately, useful market information can be developed into “good knowledge” about the dynamic connection. Hence, the connection before the change points makes little contribution to forecast returns; the useful information for forecasting is the connection starting from the change points. This “good knowledge” about the dynamic connection is conducive to accurately predicting returns, thereby solving the relation between risk prevention and sustained development.
The crude oil prices experience high volatility during the last two decades. These crises mainly include the global financial crisis (GFC) and the COVID-19 pandemic. Subsequently, the global crude oil market has become highly volatile and suffered substantial losses due to the reduced demand for crude oil during both crises. Investors can rely upon other commodities to hedge their investments and diversify their portfolios. Global events such as GFC and COVID-19 play a critical role in investments and hedge funds to diversify a portfolio. This paper focuses on the empirical investigation of the time-varying correlation between crude oil futures and gold during GFC and the COVID-19 pandemic by employing the DCC GARCH model and found that gold futures do not play as a haven against crude oil futures.
This paper explores the effectiveness of gold as a hedging and safe haven instrument for a variety of market risks. Rather than confining the analysis to specific countries, we treat gold as a global asset and apply the novel Phillips, Shi and Yu (2015a) and Phillips, Shi and Yu (2015b) methodology to identify extreme price movements. This method accounts for both the level and speed of changes in price dynamics that better characterises periods of abnormally high risks. We uncover safe haven properties for the European sovereign debt crisis, stock market crash, and oil inflationary pressures. We also show that gold exhibits hedging properties when investors are faced with currency, European sovereign debt, stock market, and oil inflation risks. Finally, we demonstrate that gold was a weak safe haven for investors during the 2020 Covid-19 crisis, and highlight the importance of accounting for speed and price levels in the identification of abnormal risk periods.
This study investigates the causal relationships among spot and futures prices of crude oil and gold for the Indian market by applying rolling, recursive evolving and asymmetric causality tests, for the period spanning from January 2006 to February 2019. Using the rolling and recursive evolving Granger causality test, this study investigates the time-varying nature of causality between the prices of crude oil and gold. The results indicate that there exist causal linkages both from crude oil prices to gold prices, and vice-versa, during different sub-periods between January 2006 and February 2019, for the spot prices as well as the futures prices. The asymmetric causality tests reveal that negative shocks and positive shocks in oil prices Granger causes gold prices, and vice-versa, considering both spot and futures prices. In addition, the asymmetric causality tests show that positive shocks in crude oil prices and gold prices have a more significant impact on gold prices and crude oil prices respectively as compared to a negative shock in crude oil prices and gold price. This study's findings may be of particular relevance to policymakers and risk management professionals who need to comprehend the behavior of crude oil and gold commodity markets and the relationships that exist between them.
Does news tone help forecast oil? In this paper, we study the relationship between news tone and crude oil prices and evaluate the role news tone plays in the ability to forecast oil prices. Specifically, we use a recently developed oil-specific dictionary as well as a widely used general financial dictionary, to directly measure the sentiment of 3579 oil news articles from Financial Times for actual oil price forecasting. We find compelling evidence that news tone constructed by the oil dictionary helps forecast monthly oil prices out-of-sample over short horizons, while the news tone constructed by financial dictionary shows no out-of-sample forecasting power at all. We verify and document the economic significance of the best performing forecasting model against the others and a naive buy-hold strategy. We argue that the forecasting power of news tone is data and method dependent, and we underscore the correct use of domain-specific dictionaries in financial sentiment analysis.
This study investigates the linear and nonlinear Granger causality relationship between the Baltic Dry Index (BDI) and global economic policy uncertainty (GEPU). There is a unidirectional linear Granger causality relationship running from GEPU to the BDI and no nonlinear Granger causality relationship between the BDI and GEPU. Hence, the Granger causality running from GEPU to the BDI is relatively direct, concise and swift but not very complicated. The linear Granger causality relationship between them is time-varying.
Using a time-varying vector autoregressive (TVP-VAR) model combined with a spillover index, we study the dynamic spillovers between trade policy uncertainty (TPU) and precious metal markets during the Sino-US trade war. The results show obvious spillover effects between the Chinese TPU and American TPU and the precious metal markets, and the strength and direction of the spillover effects are time-varying and asymmetrical. The uncertainty of the Sino-US trade policy has a heterogeneous impact on the precious metal markets. American TPU dominates the markets, followed by Chinese TPU. In the face of trade war conflict, the spillover fluctuation of American TPU to Chinese TPU is very significant. In addition, in the face of trade policy uncertainty, gold and silver have strong self-adjustment abilities and stabilities, making them highly suitable for hedging investments. International investors and policymakers should consider the impacts of international trade policy uncertainty when conducting risk monitoring and building portfolios in precious metal markets.
Finansal piyasalarda alınan kararları etkileyen birçok faktör bulunmaktadır. Bu faktörlerden birisi de korku indeksidir. Oynaklık indeksi olarak da bilinen bu parametre karar alıcıların piyasadaki hare-ketlerine yön vermektedir. Bu bağlamda, söz konusu indeks ile finansal piyasaların işleyişi hakkında bilgi veren diğer değişkenler arasında yakın bir ilişkinin varlığı literatürde derinlemesine incelen-mektedir. Petrol ve altın fiyatları bu alanda öne çıkmaktadır. Dolayısıyla korku indeksi ile petrol ve altın fiyatları arasında doğrudan veya dolaylı olarak bir etkileşimin varlığından bahsedilebilir. Bu çalışmada, yatırımcıların finansal piyasalara dair beklentilerinin bir ölçümü olan VIX indeksi ile petrol ve altın fiyatları arasındaki nedensellik ilişkisi 1990-2020 dönemi için aylık veriler kullanılarak incelenmiştir. Elde edilen bulgular doğrultusunda VIX değişkeninin özellikle kısa ve uzun dönemde petrol fiyatlarının nedeni olduğu aynı zamanda petrol fiyatlarının da altın fiyatlarının orta ve uzun dönemde nedeni olduğu sonucuna ulaşılmıştır.
Popular investment choices such as fixed income, gold, and real estate have generated low returns over long horizons. Equity seems to have performed much better despite its inherent risk. Although, investors prefer safe-haven assets, they are increasingly moving to equities in search for better returns. We consider whether equity could be a safe-haven investment if chosen from quality stocks’ basket. We examine the safe-haven and hedging properties of the Nifty-50 constituent stocks over the period 2008–2020. To address this, we employ copula-based framework to model the dependence structure between stocks and five indices. We distinguish between safe-haven attributes and hedging features of the individual stocks. We show that the safe-haven properties of the Nifty-50 listed stocks are not as concentrated as gold but they show much low co-movement with the market. We call them pseudo–safe-haven as they are the safe-bets for investors seeking relatively safe-haven assets with impressive returns.
JEL Classification: G11, G12, G15
The association among financial and commodity markets provokes thought for investors now a days. The fluctuation of price index in one market may affect the price index of the other. This paper attempts to identify the impact of crude oil price, GDP and USD Index on gold price or not during the times of the pandemic as frequent volatility in gold price was seen which left the investors into dilemma. The most frequently affected volatility is happening for gold price which has driven the focus of different pockets of investors, research analysts, educators and so on. This study has done a stationarity test for the data which is on time series, and verified with ADF Unit toot test, and Granger Causality test is applied to analyse the causality. The outcome of unit root test depicts that time series are not stationary at levels and selected time series are stationary at the level of first difference. Granger Causality test shows that there is nil causal relationship between gold price and USD Index and gold price and GDP, whereas crude oil price depicted impact on price of gold.
In this article the intraday price discovery process between regular index futures (floor trading) and E-mini index futures (electronic trading) in the S&P 500 and Nasdaq 100 index futures markets is examined, using intraday data from the introduction of the E-mini index futures to 2001. Using both information shares (Hasbrouck, J., 1995) and common long-memory factor weights (Gonzalo, J., & Granger, C. W. J., 1995) techniques, we find that both E-mini index futures and regular index futures contribute to the price discovery process. However, since September 1998, the contribution made by E-mini index futures has been greater than that provided by regular index futures. Based on regression analysis, we have also found direct empirical evidence to support the hypothesis that the joint effects of operational efficiency and relative liquidity determine the greater contribution made towards price discovery by electronic trading relative to open-outcry trading over time. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25: 679–715, 2005
Using synchronous transactions data for IBM from the New York, Pacific and Midwest Stock Exchanges, we estimate an error correction model to investigate whether each of the exchanges is contributing to price discovery. Johansen's multi-variate cointegration test yields two cointegrating vectors which together verify the expected long-run equilibrium of equal prices across the three exchanges. Price discovery in our sense then refers to the adjustment process by which informationally linked security markets resolve price disparities after coincident but unequal price changes. Two error correction terms specified as the difference between IBM prices on the Pacific versus NYSE and on the Midwest versus NYSE indicate that adjustments maintaining the long-run cointegration equilibrium take place on all three exchanges. That is, IBM prices on the NYSE adjust toward IBM prices on the Midwest and Pacific Exchanges, just as Midwest and Pacific prices adjust to the NYSE. The findings are robust to several data collection procedures yielding different observation intervals, different orderings of the data tuples, and different time periods.
If a financial asset is traded in more than one market, common factor models may be used to measure the contribution of these markets to the price discovery process. We examine the relationship between the Hasbrouck (J. Finance (50) (1995) 1175) and Gonzalo and Granger (J. Bus. Econ. Stat. 13 (1995) 27) common factor models. These two models complement each other and provide different views of the price discovery process between markets. The Gonzalo and Granger model focuses on the components of the common factor and the error correction process, while the Hasbrouck model considers each market's contribution to the variance of the innovations to the common factor. We show that the two models are directly related and provide similar results if the residuals are uncorrelated between markets. However, if substantive correlation exists, they typically provide different results. We illustrate these differences using analytic examples plus a real world example consisting of electronic communications networks (ECNs) and other Nasdaq market makers.
Energy security issues coupled with increased concern over the natural environment are driving factors behind oil price movements. While it is widely accepted that rising oil prices are good for the financial performance of alternative energy companies, there has been relatively little statistical work done to measure just how sensitive the financial performance of alternative energy companies are to changes in oil prices. In this paper, a four variable vector autoregression model is developed and estimated in order to investigate the empirical relationship between alternative energy stock prices, technology stock prices, oil prices, and interest rates. Our results show technology stock prices and oil prices each individually Granger cause the stock prices of alternative energy companies. Simulation results show that a shock to technology stock prices has a larger impact on alternative energy stock prices than does a shock to oil prices. These results should be of use to investors, managers and policy makers.
The study of cointegration in large systems requires a reduction of their dimensionality. To achieve this, the authors propose to obtain the I(1) common factors in every subsystem and then analyze cointegration among them. A new way of estimating long-memory components is proposed. The identification of these I(1) common factors is achieved by imposing that they be linear combinations of the original variables X[subscript]t, and that the error correction terms do not cause them at low frequencies. Estimation is done from an error correction model, which makes it possible to test hypotheses on the common factors using standard chi-squared tests.
The study of cointegration in large systems requires a reduction of their dimensionality. To achieve this, we propose to obtain the 1(1) common factors in every subsystem and then analyze cointegration among them. In this article, a new way of estimating common long-memory components of a cointegrated system is proposed. The identification of these 1(1) common factors is achieved by imposing that they be linear combinations of the original variables Xt, and that the error-correction terms do not cause the common factors at low frequencies. Estimation is done from a fuliy specified error-correction model, which makes it possible to test hypotheses on the common factors using standard chi-squared tests. Several empirical examples illustrate the procedure.
Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix obtained by regressing the series onto its first lag. Critical values for the tests are tabulated, and their power is examined in a Monte Carlo study. Economic time series are often modeled as having a unit root in their autoregressive representation, or (equivalently) as containing a stochastic trend. But both casual observation and economic theory suggest that many series might contain the same stochastic trends so that they are cointegrated. If each of n series is integrated of order 1 but can be jointly characterized by k > n stochastic trends, then the vector representation of these series has k unit roots and n — k distinct stationary linear combinations. Our proposed tests can be viewed alternatively as tests of the number of common trends, linearly independent cointegrating vectors, or autoregressive unit roots of the vector process. Both of the proposed tests are asymptotically similar. The first test (qf) is developed under the assumption that certain components of the process have a finite-order vector autoregressive (VAR) representation, and the nuisance parameters are handled by estimating this VAR. The second test (qc) entails computing the eigenvalues of a corrected sample first-order autocorrelation matrix, where the correction is essentially a sum of the autocovariance matrices. Previous researchers have found that U.S. postwar interest rates, taken individually, appear to be integrated of order 1. In addition, the theory of the term structure implies that yields on similar assets of different maturities will be cointegrated. Applying these tests to postwar U.S. data on the federal funds rate and the three- and twelve-month treasury bill rates provides support for this prediction: The three interest rates appear to be cointegrated.
This paper re-examines the Garbade and Silber (1983) model with the objective of finding out if the crude oil futures market performs the functions of price discovery and risk transfer. The model is estimated, using daily data, as a system of two seemingly unrelated time series equations allowing the coefficients to be time-varying. The empirical results reveal that the futures market performs about 60 per cent of the price discovery function, and that the elasticity of supply of arbitrage services is adequately high for the market to perform the risk transfer function.
(J.E.L: G13, C22).
This paper applies the causality test to examine the causal relationship between primary energy consumption (EC) and real Gross National Product (GNP) for Turkey during 1970–2006. We employ unit root tests, the augmented Dickey–Fuller (ADF) and the Philips–Perron (PP), Johansen cointegration test, and Pair-wise Granger causality test to examine relation between EC and GNP. Our empirical results indicate that the two series are found to be non-stationary. However, first differences of these series lead to stationarity. Further, the results indicate that EC and GNP are cointegrated and there is bidirectional causality running from EC to GNP and vice versa. This means that an increase in EC directly affects economic growth and that economic growth also stimulates further EC. This bidirectional causality relationship between EC and GNP determined for Turkey at 1970–2006 period is in accordance with the ones in literature reported for similar countries. Consequently, we conclude that energy is a limiting factor to economic growth in Turkey and, hence, shocks to energy supply will have a negative impact on economic growth.
The purpose of this study is to re-investigate the relationship between electricity consumption and economic growth in Malaysia from 1972:1 to 2003:4. This study adopted the newly developed ECM-based F-test [Kanioura, A., Turner, P., 2005. Critical values for an F-test for cointegration in the multivariate model. Applied Economics 37(3), 265–270] for cointegration to examine the presence of long run equilibrium relationship through the autoregressive distributed lag (ARDL) model. The empirical evidence suggests that electricity consumption and economic growth are not cointegrated in Malaysia. However, the standard Granger's test and MWALD test suggest that electricity consumption and economic growth in Malaysia Granger causes each other. This finding provides policymakers with a better understanding of electricity consumption and allows them to formulate electricity consumption policy to support the economic development and to enhance the productivity of capital, labour and other factors of production for future economic growth in Malaysia.
This paper re-examines the causality between energy consumption and GDP by using updated Taiwan data for the period 1954–1997. As a secondary contribution, we investigate the causal relationship between GDP and the aggregate as well as several disaggregate categories of energy consumption, including coal, oil, natural gas, and electricity. Applying Granger’s technique, we find bidirectional causality between total energy consumption and GDP. We find further that different directions of cause exist between GDP and various kinds of energy consumption.
This paper presents evidence that two ERM exchange rates are Granger caused in a nonlinear fashion by relative money supply. This finding can be interpreted as evidence that the underlying relationship between money and exchange rates is nonlinear in a target-zone arrangement, which is consistent with the target-zone literature introduced by Krugman, 1991. Target zones and exchange rate dynamics, Q. J. Econ. 106 (3), 669–682. Moreover, we find weak or no evidence that relative output nonlinearly Granger causes the exchange rate. Thus, relative money is more important than relative output in explaining the nonlinearity in the exchange rate-fundamentals relationship.
We use a bivariate asymmetric GARCH model to examine patterns of across-market information flows for gold, platinum, and silver futures contracts traded in both the U.S. and Japanese markets. Our results indicate that pricing transmissions for these precious metals contracts are strong across the two markets, but information flows appear to lead from the U.S. market to the Japanese market in terms of returns. There are strong volatility spillover feedback effects across both markets, and their impacts appear to be comparable and similar. There is evidence that intraday pricing information transmission across the two precious metals futures markets is rapid, as offshore trading information can be absorbed in the domestic market within a trading day.
The present study investigates the linear and nonlinear causal linkages between daily spot and futures prices for maturities of one, two, three and four months of West Texas Intermediate (WTI) crude oil. The data cover two periods October 1991–October 1999 and November 1999–October 2007, with the latter being significantly more turbulent. Apart from the conventional linear Granger test we apply a new nonparametric test for nonlinear causality by Diks and Panchenko after controlling for cointegration. In addition to the traditional pairwise analysis, we test for causality while correcting for the effects of the other variables. To check if any of the observed causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VECM filtered residuals. Finally, we investigate the hypothesis of nonlinear non-causality after controlling for conditional heteroskedasticity in the data using a GARCH-BEKK model. Whilst the linear causal relationships disappear after VECM cointegration filtering, nonlinear causal linkages in some cases persist even after GARCH filtering in both periods. This indicates that spot and futures returns may exhibit asymmetric GARCH effects and/or statistically significant higher order conditional moments. Moreover, the results imply that if nonlinear effects are accounted for, neither market leads or lags the other consistently, videlicet the pattern of leads and lags changes over time.
We study the price-discovery process for a number of Chinese cross-listed stocks. For the stocks cross-listed on the New York Stock Exchange (NYSE) and the Stock Exchange of Hong Kong (SEHK), we find that the stock prices of these two exchanges are cointegrated and mutually adjusting, and that the SEHK makes more contributions than the NYSE to the price-discovery process. The SEHK contributions are 81.6% and 89.4%, computed from Gonzalo and Granger [Gonzalo, J., Granger, C., 1995. Estimation of common long-memory components in cointegrated systems. Journal of Business and Economics Statistics 13, 27–35] permanent–transitory (PT) and Hasbrouck [Hasbrouck, J., 1995. One security, many markets: Determining the contributions to price discovery. Journal of Finance 50, 1175–1119] information share (IS) models respectively.
We propose a method from the viewpoint of deterministic dynamical systems to investigate whether observed data follow a random walk (RW) and apply the method to several financial data. Our method is based on the previously proposed small-shuffle surrogate method. Hence, our method does not depend on the specific data distribution, although previously proposed methods depend on properties of the data distribution. The data we use are stock market (Standard & Poor's 500 in US market and Nikkei225 in Japanese market), exchange rate (British Pound/US dollar and Japanese Yen/US dollar), and commodity market (gold price and crude oil price). We found that these financial data are RW whose first differences are independently distributed random variables or time-varying random variables.
The relationship between energy consumption and economic growth is considered as an imperative issue in energy economics. Previous studies have ignored the nonlinear behavior which could be caused by structural breaks. In this study, both linear and nonlinear Granger causality tests are applied to examine the causal relationship between energy consumption and economic growth for a sample of Asian newly industrialized countries as well as the U.S. This study finds evidence supporting a neutrality hypothesis for the United States, Thailand, and South Korea. However, empirical evidence on Philippines and Singapore reveals a unidirectional causality running from economic growth to energy consumption while energy consumption may have affected economic growth for Taiwan, Hong Kong, Malaysia and Indonesia. Policy implications are also discussed.
The US dollar is frequently used as the invoicing currency of international crude oil trading. Hence, the fluctuation in US dollar exchange rate is believed to underlie the volatility of crude oil price and especially its forecasting accuracy. Using econometric techniques including cointegration, VAR model, ARCH type models and a newly proposed approach to test Granger causality in risk, three spillover effects are explored, i.e., mean spillover, volatility spillover and risk spillover. Using rigorous appraisal, analysis is made of the influence of US dollar exchange rate on the international crude oil price from the perspective of market trading and several findings have been obtained.
When homogeneous or closely-linked securities trade in multiple markets, it is often of interest to determine where price discovery (the incorporation of new information) occurs. This article suggests an econometric approach based on an implicit unobservable efficient price common to all markets. The information share associated with a particular market is defined as the proportional contribution of that market's innovations to the innovation in the common efficient price. Applied to quotes for the thirty Dow stocks, the technique suggests that the preponderance of the price discovery takes place at the New York Stock Exchange (a median 92.7 percent information share). Copyright 1995 by American Finance Association.
The paper examines the performance of four multivariate volatility models, namely CCC, VARMA-GARCH, DCC and BEKK, for the crude oil spot and futures returns of two major benchmark international crude oil markets, Brent and WTI, to calculate optimal portfolio weights and optimal hedge ratios, and to suggest a crude oil hedge strategy. The empirical results show that the optimal portfolio weights of all multivariate volatility models for Brent suggest holding futures in larger proportions than spot. For WTI, however, DCC and BEKK suggest holding crude oil futures to spot, but CCC and VARMA-GARCH suggest holding crude oil spot to futures. In addition, the calculated optimal hedge ratios (OHRs) from each multivariate conditional volatility model give the time-varying hedge ratios, and recommend to short in crude oil futures with a high proportion of one dollar long in crude oil spot. Finally, the hedging effectiveness indicates that DCC (BEKK) is the best (worst) model for OHR calculation in terms of reducing the variance of the portfolio.
This paper examines for the first time the existence of psychological barriers in a variety of daily and intra-day gold price series. This paper uses a number of statistical procedures and presents evidence of psychological barriers in gold prices. We document that prices in round numbers act as barriers with important effects on the conditional mean and variance of the gold price series around psychological barriers.
The relationship between cointegration and error correction models, first suggested by Granger, is here extended and used to develop estimation procedures, tests, and empirical examples. A vector of time series is said to be cointegrated with cointegrating vector a if each element is stationary only after differencing while linear combinations a8xt are themselves stationary. A representation theorem connects the moving average , autoregressive, and error correction representations for cointegrated systems. A simple but asymptotically efficient two-step estimator is proposed and applied. Tests for cointegration are suggested and examined by Monte Carlo simulation. A series of examples are presented. Copyright 1987 by The Econometric Society.
This paper investigates the dynamic association between daily stock index returns and percentage trading volume changes. To proceed with this, linear and nonlinear Granger causality tests are applied to the Karachi Stock Exchange (KSE) data. The analysis covers the span of about 5 years with 1266 daily observations. The same methodology is employed for two non-overlapping sub-periods to examine the robustness of the results. As well as for positive and negative stock returns samples.Unidirectional linear Granger causality from stock returns to trading volume is observed for the entire sample period and for both the sub-periods as well. The null hypothesis of linear Granger noncausality from percentage volume changes to stock returns is rejected only in optimal lag length for the second sub-period. Regarding nonlinear Granger causality, the modified Baek and Brock's test [Baek, E., & Brock, W. (1992a). A general test for nonlinear Granger causality: Bivariate model. Working paper, Lowa State University and University of Wisconsin, Madison] for nonlinear Granger causality provides evidence of significant unidirectional nonlinear Granger causality from percentage volume changes to stock returns in both the sub-periods for all the common lag lengths used but not for vice versa. Finally, the analysis suggests that the linear Granger causality from volume change to stock price change depends on the direction of the stock price movement. The investigation exposed that volume has significant nonlinear explanatory power for stock returns in general, whereas stock returns have linear explanatory power for trading volume.
This paper examines the theoretical and empirical relationships between the major exchange rates and the price of gold using forecast error data. Among other things, it is found that, since the dissolution of the Bretton Woods international monetary system, floating exchange rates among the major currencies have been a major source of price instability in the world gold market and, as the world gold market now seems to be dominated by the US dollar bloc, appreciations or depreciations of that dollar would have strong effects on the price of gold in other currencies. The results of this study are rather different from those obtained in an earlier study of the same subject.
The first sections of the paper show that causality tests can be relevant when considering the effectiveness of a control mechanism, rejecting some results in earlier papers. The relevance comes from a more careful consideration of what information is available to whom and when, compared to previous work. The final section of the paper extends this analysis to cointegrated variables, when causality is a necessary consequence.
This paper examines the theoretical relationship between the major exchange rates and the prices of internationally-traded commodities. In the empirical section, the case of gold is analyzed using forecast error data. Among other things, it is found that, since the dissolution of the Bretton Woods International monetary system, floating exchange rates among the major currencies have been a major source of price instability in the world gold market and, as the world gold market is dominated by the European currency bloc, appreciations or depreciations of European currencies have strong effects on the price of gold in other currencies.
Linear and nonlinear Granger causality tests are used to examine the dynamic relation between daily Dow Jones stock returns and percentage changes in New York Stock Exchange trading volume. The authors find evidence of significant bidirectional nonlinear causality between returns and volume. They also examine whether the nonlinear causality from volume to returns can be explained by volume serving as a proxy for information flow in the stochastic process generating stock return variance as suggested by P. Clark's (1973) latent common-factor model. After controlling for volatility persistence in returns, the authors continue to find evidence of nonlinear causality from volume to returns. Copyright 1994 by American Finance Association.
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