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On the long run relationship between gold and silver prices A note
Abstract
This study examines the long ran trend between the prices of gold and silver futures contracts traded on the Tokyo Commodity Exchange and concludes that the stable relationship between gold and silver prices has disappeared in the 1990s. The underlying causes and implications of this finding are discussed.
... Silver also hold similar properties as that of gold because of its precious value (Ciner, 2001), being a symbol of virtues of fortune (Liu and Su, 2019), and is considered as a close substitute for gold (Lucey and Tully, 2006). Due to silver's miniature substitutability and high similarities with gold, both precious metals pursue arbitrage and low risk spread trading properties. ...
... Further, using monthly data during the period 1971 to 1990, Escribano and Granger (1998) investigate the long-run relationship between gold and silver prices and find cointegration between those two precious metal markets during the bubble and post-bubble periods. Similarly, Ciner (2001) probes the long-run relationship between gold and silver prices of the futures contracts traded on the Tokyo Commodity Exchange and shows a disappearance of the stable long run relationship between those prices during the 1990s (the disappearance is primarily due to the separation of markets). The daily closing data on gold and silver prices commences from 1992 through 1998. ...
... The past literature did not consider a dataset of this length. For instance, Ciner (2001) uses the daily closing prices of gold and silver future contracts covering a period from 1992 through 1998 and explores the long-run relationship. Lucey and Tully (2006) consider the Friday closing prices covering a period from 1978 to 2002 and find a weak relationship between gold and silver prices in a few sub-periods and a long-run stable cointegration relationship between these precious metals. ...
The objective of this paper is to examine the frequency domain connectedness among the returns series of crude oil, stock market index and four metal prices covering the period 1990M1-2017M3. To realize our objective, we have employed a recently introduced frequency domain spillover methods due to Barunik and Krehlik (2017). Furthermore, a network analysis is undertaken on the pairwise correlations and net directional matrix obtained from frequency domain spillover approach. In general, we find that the degree of connectedness decreases with the increase in the frequency. Particularly, with the lowest frequency i.e., 1 to 6 months, makes the largest contribution to total connectedness, followed by the frequency corresponding to more than 12 months, and 6 to 12 months, respectively. Our overall results suggest that titanium, platinum, gold and silver are the net contributors to volatility, while steel, crude oil, stock prices, and palladium are net receivers of volatility. These empirical findings are helpful in devising policies that avert contagion risk during period of economic rigidity and uncertainty.
... According to Ciner (2001), gold has also played an important role as a precious metal with significant portfolio diversification properties. Other research also points to the benefits of inclusion of gold holdings as leading to a more balanced portfolio (Aggarwal and Soenen, 1988;Johnson and Soenen, 1997;Ciner, 2001;Egan and Peters, 2001;Sherman, 1982;Davidson et al., 2003;Draper et al., 2006). ...
... According to Ciner (2001), gold has also played an important role as a precious metal with significant portfolio diversification properties. Other research also points to the benefits of inclusion of gold holdings as leading to a more balanced portfolio (Aggarwal and Soenen, 1988;Johnson and Soenen, 1997;Ciner, 2001;Egan and Peters, 2001;Sherman, 1982;Davidson et al., 2003;Draper et al., 2006). Although there is some evidence that in the gold market there is no strong relationship between risk and returns (Lucey & Tully, 2006b), the evidence presented here shows that the expected risk-return relationship does exist in the gold market. ...
... silver, along with gold has been used as currency for a long period in history (Baur & tran, 2014). Over the period of time silver has become the metal of various industries like, electronics, X-Ray and photography (Ciner, 2001). The prices of silver are determined by the market forces, like supply and demand. ...
... Burdekin, Mitchener & Weidenmier (2012) while examining Fisher (1920) the famous theory of 'stabilizing the dollar' reaffirmed that commodity (silver) based strategy to fix the prices is more effective. Given the different economic uses, the prices of gold and silver don't follow the same pattern in long run in Japanese Commodity Market (Ciner, 2001). However, in contrast tully & Lucey (2007), based on their 25 years data analysis, proved that despite of certain disintegration patches, there exists a long run unwavering relation between gold and silver prices. ...
The study was conducted with the view to test the impact of cash out flow from the Pakistan Prize Bond market to Gold and Silver markets as a result of the bonds’ draw. The data from July 2007 to June 2019 was selected, which included Returns of Gold and Silver Markets. The data was tested with Event Study and AR, AAR & CAR were calculated. The results of AR & CAR did not endorse both the hypotheses on the basis that significant readings were found both in pre and post draw dates, to rule out any impact of cash flow from Prize Bond market into Gold and Silver markets. Whereas, AAR results were insignificant as such, hence fortifying the inference of rejection of both the hypotheses. So, both the hypotheses of having different returns on prize bond draw dates in Gold and Silver markets were not supported. Though the results were incongruent with the hypotheses, yet study outcomes as such are significant enough to propose regulators to contemplate legislations to fetch more funds from this comparatively cheaper source through enhanced documentation, which ultimately would make the economy more compliant.
... Its distinct properties and traits render it a sought-after resource across diverse industries. Renowned for its remarkable electrical and thermal conductivity, silver frequently assumes a crucial role in the production of electronic devices within the manufacturing sector [1]. Furthermore, within the realm of medicine, the utilization of silver nanoparticles has wielded a substantial influence on the progression of treatments in the past few decades [2]. ...
This article presents a study on forecasting silver prices using the extreme gradient boosting (XGBoost) machine learning method with hyperparameter tuning. Silver, a valuable precious metal used in various industries and medicine, experiences significant price fluctuations. XGBoost, known for its computational efficiency and parallel processing capabilities, proves suitable for predicting silver prices. The research focuses on identifying optimal hyperparameter combinations to improve model performance. The study forecasts silver prices for the next six days, evaluating models based on mean absolute percentage error (MAPE) and root mean square error (RMSE). Model A (the best model based on MAPE value) suggests silver prices decline on the first and second days, rise on the third, decline again on the fourth, and stabilize with an increase on the fifth and sixth days. Model A achieves a MAPE of 5.98% and an RMSE of 1.6998, utilizing specific hyperparameters. Conversely, model B (the best model based on RMSE value) indicates a price decrease until the third day, followed by an upward trend until the sixth day. Model B achieves a MAPE of 6.06% and an RMSE of 1.6967, employing distinct hyperparameters. The study also compared the proposed models with several other ensemble models (CatBoost and random forest). The model comparison was carried out by incorporating 2 additional metrics (MAE and SI), and it was found that the proposed models exhibited the best performance. These findings provide valuable insights for forecasting silver prices using XGBoost.
... Gold, as one of the earliest forms of money, has been commonly considered as an effective hedge and safe haven for financial and other commodity assets against market turbulence (especially during market downturns). Extensive literature has shown a weak/negative relationship between gold and financial assets, revealing the investment sheltering role of gold as a hedging and/or safe haven (see, e.g., Baur & Lucey, 2010;Chua, Sick, & Woodward, 1990;Ciner, 2001;Ciner, Gurdgiev, & Lucey, 2013;Duan, Li, Urquhart, & Ye, 2021a;Dutta, Das, Jana, & Vo, 2020;Hillier, Draper, & Faff, 2006;Huang, Duan, & Mishra, 2021;Huang, Duan, & Urquhart, 2023;Kaul & Sapp, 2006;Reboredo, 2013;Upper, 2000). ...
... Owing to its risk reduction ability, inclusion of gold as a stabilizer for a portfolio has been well established (Ciner 2001). Investors commonly turn to gold to protect their investment during crises while keep a certain portion of gold within the portfolio to balance their portfolio and safeguard an unexpected event during normal periods. ...
To measure the diversification capability of Bitcoin, this study employs wavelet analysis to investigate the coherence of Bitcoin price with the equity markets of both the emerging and developed economies, considering the COVID-19 pandemic and the recent Russia-Ukraine war. The results based on the data from January 9, 2014 to May 31, 2022 reveal that compared with gold, Bitcoin consistently provides diversification opportunities with all six representative market indices examined, specifically under the normal market condition. In particular, for short-term horizons, Bitcoin shows favorably low correlation with each index for all years, whereas exception is observed for gold. In addition, diversification between Bitcoin and gold is demonstrated as well, mainly for short-term investments. However, the diversification benefit is conditional for both Bitcoin and gold under the recent pandemic and war crises. The findings remind investors and portfolio managers planning to incorporate Bitcoin into their portfolios as a diversification tool to be aware of the global geopolitical conditions and other uncertainty in considering their investment tools and durations.
... The study by Eryigit (2017) obtains that gold, palladium, and silver prices have various connections with energy prices. The studies by (Ciner, 2001;Sari et al., 2010) obtain long-run cointegrating associations between industrial metals, precious metals, and oil prices. The literature has adopted various models and methods to explore the metal and energy markets' nexus. ...
The prime objective of this article is to examine the policy-making role of metal markets, gold resources, and clean energy markets in the post-COVID-19 era and the Russia-Ukrainian military conflict. In doing so, we analyze the role of fossil fuels, clean energy, and metals markets, considering the military conflict in Ukraine in 2022. The study employs event study methodology (ESM), Total connectedness index (TCI), and network analyses. The results indicate that natural gas and clean energy prices are less affected by conflict in the aftermath of an invasion than traditional energy and metals markets. In addition, we observe an increase in the TCI in the energy markets during announcement days. The TCI of the metals market is greater than that of the energy market. According to network connectivity, the key asset class transmitters of the shock in Europe are the Geopolitical index (GPR), gold, and the clean energy stock index (ERIX). The U.S. markets are less affected by the situation in Ukraine. The average hedge suggests that the optimal hedge differs from one market to the next, with fossil fuels and renewable energy, respectively, being more hedge effective and reducing risk by an average of around 0.80 and 0.59, given their ability to function as a hedging instrument.
... This makes it an effective hedge against future dollar weakness. A numbers of studies also point to the benefits of the inclusion of gold holdings that leads to a more balanced portfolio (Johnson and Soenen, 1997;Aggarwal and Soenen, 1988;Sherman, 1982;Ciner, 2001;Egan and Peters, 2001;Draper et al., 2006). This article examines the monetary history of gold during Rashidun, Umayyad, Abbasid, Fatimid and Ottoman Caliphates. ...
For many centuries Islamic coins, such as the dīnār (pl. danānīr), have attracted the attention of historians and chroniclers. The initial appearance of Islamic coinage came as a response to economic requirements prevailing in the Middle East following the Arab invasion. The motivation behind the inception of Islamic coinage was in absolute harmony with the monetary traditions of the Near Eastern economy. Even though, the external features of the coinage might have been influenced by some socio-political factors, their intrinsic value was determined by the contemporary economic conditions. During periods 1870-1930 the world monetary system was mainly built on gold. However, with the establishment of Bretton Woods System in 1944, the world was set on the new monetary system involving a fixed exchange rate system. With the collapse of the Bretton Woods system in 1971 the price of gold increased to USD 38 per ounce. In April 1974, following the high volatility in monetary markets and oil crisis the price of gold increased 453% and reached USD 172 per ounce. As the U.S. economy increased in power, the I.M.F. diminished the monetary role of gold so that subsequently, gold was viewed as merely a precious metal commodity that was subject to a market value and changes in the price of gold. This article examines the monetary history of gold during Rashidun, Umayyad, Abbasid, Fatimid and Ottoman Caliphates. In particular, this study focuses on golds' performance during global financial crisis of 2007-2009 (GFC) and correlation analysis is conducted to measure the strength and direction of a linear relationship between gold and other selected asset classes as well as financial indicators such as real interest rates, inflation rates, silver price, Dow Jones Industrial Average, Trade Weighted U.S. Dollar Index, money supply and crude oil prices.
... Among these articles, Escribano and Granger [1] studied the long-term relationships of gold and silver by using cointegration techniques and although they found evidence of cointegration, they also found signs of separations. Ciner [4] found evidence that the longterm relationship between the gold and silver market has disappeared and they should be treated as separate markets and not used as a substitute in hedging strategies. Lycey and Tully [5], by studying their relationship during a period of 25 years ending in 2002, showed through the use of cointegration techniques that the long-term relationship of gold and silver is stable, although this relationship broke in 1990s. ...
Several studies estimate the volatility spillover effects between gold and silver returns, but none of them used the implied volatility to evaluate the long-term relationship between these two metal markets. Our paper aims to fill this gap in the existing literature. This paper investigates the long-term volatility transmission between gold and silver; by using GARCH and VAR modelling, it finds that the volatility transmission from gold to silver is unidirectional. Volatility strategies using options can be designed to take advantage of this especially in times where the volatility transmission is not captured by the markets. Additionally, the results appear to be useful for gaining better portfolio diversification benefits. Investors, for instance, could use the results of this study for making proper investment decisions during the period of economic down-turns or inflation surges.
... Also, little is known for the exporters of oil (Babatunde et al., 2013;Charfeddine & Barkat, 2020;Salisu & Isah, 2017), especially for the oil and gas stock prices. Furthermore, few studies have examined asymmetric oil price shocks on the oil and gas stock price, amidst the studies lack a consensus on findings of extant literature (Ciner, 2001;Jones & Kaul, 1996;Sadorsky, 1999). Evidence has shown that oil price impact could be asymmetric and nonlinear while most studies in the literature were based on the presumption that oil price impact on the stock market is linear. ...
Overdependence on oil revenue has exposed the economy to shocks from oil price variations. In this paper, we investigated the relationship between oil price on the stock prices of oil and gas firms quoted in the Nigerian Stock Exchange market. In doing so, the ARDL and NARDL approach is applied to estimate quarterly data from 2009q1 to 2016q3. The result from the study showed that negative and positive oil price shocks have a positive long-run influence on stock prices (oil & gas). The evidence further showed that the long-run and short-run impact of an oil price increase on the oil and gas stock price index is similar to that of an oil price decrease. The oil and gas stock price responds positively to oil price shocks in the long run. Therefore, the portfolio managers, potential investors and policymakers in the oil-exporting countries should diversify investments to reduce exposure to risk and uncertainty that may arise to disruptions in the demand and supply for oil and gas or during periods of declining oil prices induced by negative global economic events.
... For example, Escribano and Granger (1998) compared the performance returns of gold and silver and found a strong simultaneous relationship in returns between them. This finding contradicts Ciner's (2001) results. Ciner studied gold and silver returns for future contracts traded in Tokyo from 1992 to 1998 and found no evidence of long-run linkages between the gold and silver price movements. ...
This paper investigates the price stability properties of precious metals during the 1997 Asian Financial Crisis, 2007–2008 Global Financial Crisis, and 2010 Eurozone Crisis. To analyse the interaction between precious metal prices and the US stock market stock performances, we use the ICSS algorithm along with the GARCH model to evaluate how the number of rapid changes in volatility of precious metals has been reduced. The results suggest gold is the most stable of the precious metals. However, silver, platinum, and palladium showed positive price correlation when the US Dow Jones market was unstable. These results imply that: (1) the correlation among stocks market returns has little to no significant impact on the price movement of precious metals, but the US Dow Jones has some influence on precious metal markets except gold, which means investors can reap this benefit from diversification; (2) investors can systematically increase their portfolio returns by going short with the gold investments with low price co-movement and long on silver, platinum, and palladium with high co-movement with stock prices.
... The effects of commodity price fluctuations on the macro-economy have been pioneered in Hamilton [62], and the connection with economic growth has been a fruitful thematic in the financial literature [18,21,24,25,27,40,74,108,112,114]. Despite the key informational role of commodity futures in addressing the monetary policy [9, 66] the exposure of commodity price fluctuations to macro risk has been hard to price [95] due to the strong inter-sectorial dependencies, first documented in Pindyck and Rotemberg [90] and Ciner [35]. A rich literature has then focused on the spillover effects between oil price and financial markets [72], and oil price and other commodities [10,39] such as precious metals [47, 93], agricultural [43, 84, 85], energy [49,92,102], and, more recently, the impact of climate related variables on the co-movements of commodity prices that affect the stability of the financial system [50]. ...
In this paper, we investigate the interconnections among and within the Energy, Agricultural, and Metal commodities, operating in a risk management framework with a twofold goal. First, we estimate the Value-at-Risk (VaR) employing GARCH and Markov-switching GARCH models with different error term distributions. The use of such models allows us to take into account well-known stylized facts shown in the time series of commodities as well as possible regime changes in their conditional variance dynamics. We rely on backtesting procedures to select the best model for each commodity. Second, we estimate the sparse Gaussian Graphical model of commodities exploiting the Graphical LASSO (GLASSO) methodology to detect the most relevant conditional dependence structure among and within the sectors. A novel feature of our framework is that GLASSO estimation is achieved exploring the precision matrix of the multivariate Gaussian distribution obtained using a Gaussian copula with marginals given by the residuals of the aforementioned selected models. We apply our approach to the sample of twenty-four series of commodity futures prices over the years 2005–2022. We find that Soybean Oil, Cotton, and Coffee represent the major sources of propagation of financial distress in commodity markets while Gold, Natural Gas UK, and Heating Oil are depicted as safe-haven commodities. The impact of Covid-19 is reflected in increased heterogeneity, as captured by the strongest relationships between commodities belonging to the same commodity sector and by weakened inter-sectorial connections. This finding suggests that connectedness does not always increase in response to crisis events.
... Eryigit (2017) proved that gold, silver and palladium prices have different correlations with energy prices. In addition, some studies have found the cointegration relationship between precious and industrial metals (Ciner, 2001;Sari et al., 2010). Most studies only considered the precious metal markets especially the gold market, without further studying the relationship between energy, industrial and previous metal markets. ...
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.
... While, results from Bampinas and Panagiotidis (2015) showed that silver can hedge inflation in the UK but not in the US. According to the results of Ciner (2001), stable relationship among gold and silver returns disappeared following 1990s, indicating that these assets may provide different hedging implications in managing risky portfolios. Moreover, Umar et al. (2019) reported that copper can hedge credit risk of US metal and mining industry better than other metals. ...
This paper examines the safe-haven role of copper, iron, gold, silver, and energy stocks for international equity markets during the COVID-19 pandemic. Specifically, the degree and structure of return dependence at different points of conditional return distributions are examined for the pre-COVID and post-COVID periods. The results show that copper is a weak safe-haven for the US equity market at the upper-tail of conditional distribution of cooper returns during the post-COVID period. Gold loses its hedge status during the post-COVID period while silver is a strong safe-haven against international equity markets at the upper-tail of conditional return distribution of silver. Further, iron pose weak safe-haven properties against international equity markets when iron returns are extremely positive. However, neither conventional nor green energy stocks act as safe-haven against international equity markets. Current results may provide guidance for risk management, portfolio management and policy decisions during the post-COVID-19 period.
... Following the inference 8 Given the historical correlation between gold and silver, the nonstationarity finding for silver prices can sound surprising while gold prices follow a stationary path. To clarify this point, we can recall that there are numerous studies on the co-movements of gold and silver (e.g., Ciner (2001); Escribano and Granger (1998); Lucey and Tully (2006); Soytas et al. (2009), Rossen (2015). Overall, results indicate that the connection between gold and silver prices depends on the time period under consideration, and they have become more separated over time. ...
The dynamics of metal prices are highly significant for worldwide economic activity due to metals being key intermediate inputs to industrial production and construction and treated as investment assets. In that sense, this study investigates the efficient market hypothesis, i.e., predictability of price patterns, for several non-renewable resources, namely copper, lead, tin, nickel, zinc, aluminum, gold, platinum, and silver. Our period covers 1980Q1–2019Q4, during which metal markets witnessed many extraordinary times due to market-specific and global factors. Accounting for the importance of the potential breaks in the analysis of stochastic properties of non-renewable resource prices, we utilize two different stationarity tests; one is designed to capture smooth breaks, and the other one is designed to detect abrupt changes in the prices. Our empirical results reveal that none of the prices, except silver, can be characterized by the efficient market hypothesis. They follow stationary and predictable patterns with structural changes related to market-specific and global economic events, though concerns on economic uncertainties appeared to be more effective on precious metals.
... Çoğu araştırma öncelikle Bitcoin'in yasal ve teknolojik yönlerine odaklanmış olsa da, Bitcoin'in diğer finansal, çeşitlendirme ve riskten korunma yönlerinin incelenmesi henüz yeterli düzeyde ilerlememiştir. Altın ve diğer varlıklar arasındaki ilişkiler üzerine ampirik literatür ve daha sonra altının bir çeşitlendirici ve bir riskten korunma aracı olma potansiyeli oldukça büyümüştür (Ciner 2001;Kaul ve Stephen 2006;Miyazaki ve Hamori 2014;Ciner ve diğerleri 2013;Reboredo 2013;Beckmann ve diğerleri 2015). Bu çalışmada, Bitcoin'in alternatif yatırım araçlarıyla bir arada portföy optimizasyon yaklaşımı kullanarak portföy çeşitlendirmesinde altın, hisse senedi, Bitcoin vb. ...
The study examines intraday price relationships between SP500 and Dow Jones Industrial (DJI) in
the 3 months following the V-type recovery from the stock market crash due to the pandemic.
Analysis with Momentum Threshold Value (MTAR) cointegration and error correction models
revealed the existence of an intraday non-linear asymmetric relationship in US stock markets. While
the MTAR model shows small decreases for positive ex values , it shows significant decreases for
negative ex values. Vector error correction model findings indicate the existence of an asymmetric
causality relationship from the SP500 index to the DJI index in the long run.
... The research suggested a positive relationship between gold price and silver price but that it was not stable. Ciner (2001) focused on silver and gold price on the Tokyo Commodity Exchange. The findings reported a disappearing relationship between the two assets highlighting that silver and gold are separate markets with different users. ...
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.
... Çoğu araştırma öncelikle Bitcoin'in yasal ve teknolojik yönlerine odaklanmış olsa da, Bitcoin'in diğer finansal, çeşitlendirme ve riskten korunma yönlerinin incelenmesi henüz yeterli düzeyde ilerlememiştir. Altın ve diğer varlıklar arasındaki ilişkiler üzerine ampirik literatür ve daha sonra altının bir çeşitlendirici ve bir riskten korunma aracı olma potansiyeli oldukça büyümüştür (Ciner 2001;Kaul ve Stephen 2006;Miyazaki ve Hamori 2014;Ciner ve diğerleri 2013;Reboredo 2013;Beckmann ve diğerleri 2015). Bu çalışmada, Bitcoin'in alternatif yatırım araçlarıyla bir arada portföy optimizasyon yaklaşımı kullanarak portföy çeşitlendirmesinde altın, hisse senedi, Bitcoin vb. ...
Bu çalışmanın temel amacı Küresel Belirsizlik Endeksi ile BRICS ülkelerinin borsaları arasındaki volatilite etkileşimini araştırmaktır. Buna bağlı olarak Ocak 2005 – Ekim 2020 dönemine ait aylık veriler kullanılarak BEKK-GARCH modeli çalıştırılmıştır. Yapılan analizler sonucunda Küresel Belirsizlik Endeksinden BRICS ülkelerinin borsalarının tamamına doğru volatilite etkileşimi olduğu görülmüştür. Fakat BRICS borsalarından sadece Hindistan ve Güney Afrika borsalarından Küresel Belirsizlik Endeksine doğru volatilite yayılımı gerçekleşmektedir. Sonuç olarak Küresel Belirsizlik Endeksi ile Brezilya, Rusya ve Çin borsaları arasında tek yönlü volatilite yayılımı gerçekleşmekte, fakat Hindistan ve Güney Afrika borsaları ile çift yönlü yayılım bulunmaktadır
... Çoğu araştırma öncelikle Bitcoin'in yasal ve teknolojik yönlerine odaklanmış olsa da, Bitcoin'in diğer finansal, çeşitlendirme ve riskten korunma yönlerinin incelenmesi henüz yeterli düzeyde ilerlememiştir. Altın ve diğer varlıklar arasındaki ilişkiler üzerine ampirik literatür ve daha sonra altının bir çeşitlendirici ve bir riskten korunma aracı olma potansiyeli oldukça büyümüştür (Ciner 2001;Kaul ve Stephen 2006;Miyazaki ve Hamori 2014;Ciner ve diğerleri 2013;Reboredo 2013;Beckmann ve diğerleri 2015). Bu çalışmada, Bitcoin'in alternatif yatırım araçlarıyla bir arada portföy optimizasyon yaklaşımı kullanarak portföy çeşitlendirmesinde altın, hisse senedi, Bitcoin vb. ...
We study the causal relationship between the risk appetite indices (RISE) and several financial indicators over the period 2008M01–2021M05 by employing the causality-in-quantiles technique and using weekly observations. The results suggest one-way causality-in-mean from the changes in CDS spreads and interest rates to all RISE indices in all quantiles. The findings, for example, show that the strength of the causality from CDS peaks in the bearish market but weakens as we move towards the medium and upper quantiles, that is, in the normal and bullish markets. However, the results of causality-in-variance exhibit unidirectional linkages from all RISE indices to bond yields at the normal and upper quantiles, implying no predictability of bond markets in the bearish markets. The findings yield significant implications in constructing monetary policy and measuring risk factors during different market conditions.
... Gold prices have a mammoth economic impact on the financial operation and economic areas (Ebrahim et al., 2014). Gold is a means of investment that can easily convert into monetary form and precious metal to make ornaments (Ciner, 2001). The investment in gold is viewed as a means of trade, inflation hedge, store of significant worth, and alternate for the stock market during the time of stock market inconvenience (Baur and Lucey, 2010). ...
... Our paper is linked to broad two strands of the literature. The first is research on financial modelling and risk management in relation to oil (the 'black gold'), which has traditionally been used for hedging, safe haven and diversification (see studies on oil and similar asset classes in Sherman, 1986;Chua et al., 1990;Upper, 2000;Ciner, 2001;Hillier et al., 2006;Kaul & Sapp, 2006). The second strand is the literature on risk management, in particular for energy and environmental derivatives, through the use of new technologies like blockchain and cryptocurrencies; our study examines Bitcoin, which is widely considered a modern alternative safe-haven asset. ...
In the context of the debate on cryptocurrencies as the ‘digital gold’, this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments.
... Consequently, when the other precious metals are used as a substitute for gold, there may be a co-movement among their prices. Ciner (2001) finds that the long-term stable relationship between gold and silver prices on the Tokyo market disappeared in the 1990s, giving way to separate developments. Lucey and Tully (2006) review Ciner's results, using a dynamic cointegration analysis, and show that this relationship has not really disappeared. ...
... Consequently, when the other precious metals are used as a substitute for gold, there may be a co-movement among their prices. Ciner (2001) finds that the long-term stable relationship between gold and silver prices on the Tokyo market disappeared in the 1990s, giving way to separate developments. Lucey and Tully (2006) review Ciner's results, using a dynamic cointegration analysis, and show that this relationship has not really disappeared. ...
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.
... Later, Ma (1985) and Wahab, Cohn, & Lashgari (1994) found similar results. The findings of Koutsoyiannis (1983), Ciner (2001), and Escribano and Granger (1998) are in contrast to the long-term relationship Note. Price of 2 January, 2001, is taken as 100. ...
This paper discusses the case of strong path dependency in asset prices from the theoretical and empirical standpoints. Specifically, it demonstrates persistence of excess volatility in the gold spot price data that engenders excessive path dependence, whereas it is not the same with silver. For this study, we use the extreme value estimator proposed by Rogers and Satchell (1991) and the VRatio proposed by Maheswaran et al (2011). The data for the study is for the period from January, 2001 to December, 2016. We use multiple-days’ time horizons for examining the excess volatility with a better approximation of Brownian motion in the data. We capture the excess volatility in the gold data using the Binomial Markov Random Walk model. In this paper, we also utilize the Expected Lifetime Shortfall (ELS) ratio, as a measure of risk to test for the presence of mean reversion in asset prices. Using this ratio, one can observe that the strong mean-reverting characteristic in gold makes it a better investment choice than silver, in general, in the medium term.
... Choi and Hammoudeh (2010) confirm increasing correlations between spot markets of Brent oil, WTI oil, copper, gold, and silver. Specifically, Ciner (2001) finds no evidence of cointegration between gold and silver futures traded in Japan. Sensoy (2013) further documents that there is no significant impact on the volatility levels of gold and silver during the turbulent year of 2008. ...
We employ the R-vine copula approach to study the dependence structures between non-ferrous metal commodity futures on the London Metal Exchange, focusing on the comparison before and after structural breaks. We find that the center of the dependence structures between non-ferrous metal futures shifts from copper to zinc after the first structural break in 2008 and moves back to copper after the second structural break in 2014. Additionally, we document that non-ferrous metals experienced an increase in the level of integration and tail dependence between 2008 and 2014, while this increase is shown to cease after 2014. We further develop an R-vine copula-based method for forecasting Value-at-Risk, and the backtesting results show superior forecasting accuracy over the benchmark methods. Our study is useful for market participants seeking to enhance their risk management for non-ferrous metals.
... Furthermore, in terms of portfolio diversifications, Hammoudeh et al. (2009) found that, portfolio managers should include gold and silver as assets to a portfolio that also includes oil and copper or use hedges based on those nonprecious commodities. Their results complement those of Ciner (2001) who considers gold and silver as substitutes to hedge certain types of risk. Thus, oil traders should get their signals from both fundamentals of world supply and demand but also from the actions of central banks that channel their interest rate policies through credit markets that have linkages with many sectors of the economy and translate both in real growth and inflationary expectations. ...
Coronavirus (2019-nCoV) not only has an effect on human health but also on economic variables in countries around the world. Coronavirus has an effect on the price of black gold and on its volatility. The shock on all markets is already very strong. Volatility patterns in Brent crude oil simulation are examined during covid-19 crisis that significantly affected the oil market volatility. The selected crisis of coronavirus arose due to different triggers having diverse implications for oil returns volatility. Our findings indicate that model choice with data modeling is the same appropriate model EGARCH(0,2) with different parameters between pre-coronavirus and post-coronavirus. We find that oil prices are the most strongly and negatively influenced by the Coronavirus crisis. The downward movement post-covid-19 crisis is very noticeable in energy volatility. The return series, on the other hand, do not appear smooth, they rather appear volatile. We conduct a Monte Carlo simulation exercise during coronavirus crisis to investigate whether this decline is real or an artefact of the oil market. Our findings support the fact that the decline in oil prices volatility is an artefact of the covid-19 crisis.
Keywords: Oil Returns Conditional Volatility, Coronavirus Crisis, Univariate GARCH Models, Mean Equation, Variance Equation, Monte Carlo Simulation.
JEL Classifications: Q43, E44, C1, I15, C15
DOI: https://doi.org/10.32479/ijeep.10374
... Change in oil price is considered as a major source of business cycle and therefore stock prices fluctuations. In this context, Hamilton (1983), Mork (1989), Jones and Kaul (1996), Ciner (2001), Basher and Sadorsky (2006), Park and Ratti (2008), Aloui andJammazi (2009), Elder andSerletis (2010), Rahman and Serletis (2012), Fowowe (2013), Ajmi et al. (2014), Elian and Kisswani (2017) provide evidence for this argument. For example, Kang, Ratti, and Yoon (2015) shown that positive shocks to aggregate demand and to oil-market specific demand have negative effects on stock markets' return and volatility. ...
This paper investigates the relationship between international oil price and stock prices applying the time varying causality testing over the period of 2000 M1-2017 M3. The panel unit root and panel cointegration tests considering cross-section dependence are also employed. A time varying panel smooth transition vector error correction (TV-PSTRVEC) model is a developed and estimated for testing the presence of non-linear short-run and long-run causal-ity, and cointegrating relationship between stock and oil prices. The empirical findings indicate that short and long-run causalities between oil price and stock prices are time-dependent. Moreover, oil price cause stock prices in the long-run. In the short-run, neutral effect exists between oil price and stock prices. These two findings are evidence of a strong exogeneity of oil price in time-dependent regimes which is also supporting the recent arguments and empirical findings.
... Praktyka ta jest powszechnie stosowana na wielu segmentach rynku towarowego -m.in. na rynku miedzi.5 Negatywna korelacja zmian cen złota w stosunku do zmian cen akcji została pokazana w licznych pracach[Aggarwal i Soenen 1988, Johnson i Soenen 1997, Egan i Peters 2001, Ciner 2001. Z kolei zagadnienie korelacji stóp zwrotu cen akcji i stóp zwrotu na rynku srebra zostało opisane przez G.Brauer [1986]. ...
... policy, and dollar exchange rates as well as the geopolitical events such political events, military tensions, serious climate changes, natural catastrophes. Such these uncertainties enhance the volatility of crude oil market, leading to quick oil price changes and as a result to an increase in their inefficiency levels (Areal et al., 2015;Ciner, 2001;Fan et al., 2008;Hammoudeh et al., 2010;Kaufmann and Winters, 1989;Rockerbie, 1999). During COVID-19 pandemic outbreak, the speed of information transmission and the existence of profit opportunities augment significantly in these strategic commodity markets. ...
This paper examines the impacts of COVID-19 on the multifractality of gold and oil prices based on upward and downward trends. We apply the Asymmetric Multifractal Detrended Fluctuation Analysis (A-MF-DFA) approach to 15-min interval intraday data. The results show strong evidence of asymmetric multifractality that increases as the fractality scale increases. Moreover, multifractality is especially higher in the downside (upside) trend for Brent oil (gold), and this excess asymmetry has been more accentuated during the COVID-19 outbreak. Before the outbreak, the gold (oil) market was more inefficient during downward (upward) trends. During the COVID-19 outbreak period, we see that the results have changed. More precisely, we find that gold (oil) is more inefficient during upward (downward) trends. Gold and oil markets have been inefficient, particularly during the outbreak. The efficiency of gold and oil markets is sensitive to scales, market trends, and to the pandemic outbreak, highlighting the investor sentiment effect.
... The volatile nature of the prices of these metals can impact the price movements of the whole commodity markets (Gokmenoglu and Fazlollahi, 2015). (Ciner, 2001) argues that gold and silver must be pondered as separate markets and should not be considered as close substitutes to hedge against similar types of risk. Therefore, in this study, we focus not only on gold but also on silver. ...
In this paper, we investigate the volatility spillover effect among the global commodity futures (including both energy and metal futures; global stock markets (covering both Developed and Emerging Markets); the US bond market and the US Dollar index by employing the dynamic connectedness approach of (Diebold and Yilmaz, 2012, 2014) based on the time-varying parameter vector autoregressive (TVP-VAR) model and using daily data for the period from January 3, 1992 to December 27, 2019. Our results indicate a moderate connectedness among the volatilities changing over time and approaching its peak level during 2007/08 global financial crises. In addition, we determine the optimal hedge ratios and portfolio weights for the commodity investors and portfolio managers. Our results indicate that for the equity market volatility investors, the highest hedging effectiveness can be reached by taking short positions in energy futures (such as natural gas), on the other hand for both the US bond and US Dollar volatility investors it can be reached by taking short positions in metal futures (such as gold).
This article explores the dynamic interdependencies, returns and volatility spillovers among oil, gold, foreign exchange and equity markets. It also compares how the leading and most active commodity markets, forex and equity markets reacted to each other during the period of the global economic crisis and COVID-19 pandemic. The time-varying relationship between oil and gold, oil and forex and gold and exchange rate is predominantly positive, whereas it is negative for oil and stock price, gold and stock price and exchange rate and stock price. This continues to hold even in the crisis period, except for the oil and stock price relationship during the COVID-19 crisis. However, the degrees of relationship significantly high during both crises. The returns and volatility spillovers obtained from forecasted error variance decomposition based on vector autoregression show that the spillovers among these four markets remained low for the whole period but drastically increased during both crises. Compared to the return spillover, the volatility spillover is strong in the crisis period and was too high during the COVID-19 crisis. The rolling-sample spillover analysis shows that both return and volatility spillovers significantly vary across different periods, and the volatility spillovers are predominant.
JEL Codes: C32, F31, G15, Q40
The precious metal gold has been used as money for many centuries till the breakdown of the “Breeton Woods” agreement. In addition to its use as jewelry and electrical components, gold is now used as an asset investment and a hedging strategy against inflation, this leads us to consider the gold market as a financial market .The price of gold has known small changes over the period: 1833 to 1973 (before the breakdown of Breeton Woods agreement) ,from 20,65 $ per ounce in 1833 to 42 $ per ounce in 1971.After 1973 , gold has fluctuated , it was about 384 $ per ounce in 1982 and 283 $ per ounce in 1999. The gold price has risen dramatically since 2001 from its low level of 250 $ per ounce, then it reached a peak in 2011 with a level of 1700 per ounce.The main objective of this paper is to focus on the gold market and try to analyze empirically the trend of gold price and the causes behind its fluctuations. The paper provides first the literature review highlighting the factors that affect the gold price and its trend. Second, using a Multivariate-GARCH model, we search for the effect of the different economic indicators on the movements of the gold price and if they are a real cause beyond its volatility. Finally, a concluding section is provided to compare the results with the existent literature.
The interaction among financial and precious metals in various markets is a furthermost interesting matter for investors. The fluctuation in prices in one market may influence the price index of the other market. Gold prices have widespread economic effects on different financial activities. These effects are directly specious in all financial decisions adopted by investors. This study aims to check whether bank stock return (SRB) and insurance stock return (SRI) have significant effects on the gold price (GOP). We employ the Autoregressive Distributed Lag co-integration to test the long-run association between the SRB, SRI, and GOP. The study reveals that the SRB and SRI have no long-run effect on the gold price. Only one unidirectional causal relationship exists between stock returns in the bank sector and gold prices; results designate that a one percent increase in stock returns in banks would lead to a decrease of 984% in the gold price.
This dissertation focuses on three main themes related to precious metals markets with particular attention to: (1) the dynamics and causality between spot and forward precious metals, (2) the investment properties of precious metals as hedge, safe haven and diversification assets for G-7 equity markets and, (3) the impact of economic policy uncertainty (EPU) on the dependence between precious metals and BRICS equity markets. We first examine, in the first chapter, the dependence structure and the Granger causality in distribution (GCD) between spot and future returns of precious metals (gold, silver, and platinum) via copula modelling. This study considers the evidence on real precious metals returns from Jan 2, 2002 to Jan 13, 2017. Throughout literature, the use of copula in precious metals markets is still limited. Indeed, unlike linear methods, using a copula-based approach has several attractive advantages. Using static and dynamic copulas, we find that the dependence between the spot and the future returns of precious metals is relatively strong and time varying with a strong tail dependence for all pairs. Then, applying independence test based on the empirical copula, we detect a unidirectional GCD from future to spot precious metals market during normal times. This result means that past information from the future returns improves forecasts of spot returns. However, the causal relationship seems to be bidirectional in the case of gold and platinum during crisis periods. Our findings are important to investors for investigating hedging strategies since the efficacy of a hedging strategy is dependent on the price discovery mechanism. Hence, they should take the above findings into consideration to make optimal decisions, especially during periods of marked instability. The second chapter discusses the hedge, safe haven, and diversification properties of precious metals—namely gold, silver, and platinum—for the G-7 stock markets. Therefore, this study proposes a multivariate vine copula-based GARCH model to assess the hedge and safe haven properties of precious metals and a Bivariate Value at Risk-based copula (BiVaR) measure to analyse the diversification potential of precious metals. Our empirical results suggest that gold is the strongest hedge and safe haven asset in almost all the G-7 stock markets. Whereas silver and platinum results show that they may act as weak hedge assets. Results of safe haven analyses show that silver bears the potential of a strong safe haven role only for Germany’s and Italy’s stock markets; however, platinum provides a weak safe haven role for most developed stock markets. Finally, precious metals appear as interesting assets for diversifying a portfolio for G-7 stock markets investors. Overall, our findings provide noteworthy practical implication for investors. The last chapter investigates the impact of the Economic Policy Uncertainty (EPU) on the quantile dependence between precious metals and BRICS stock markets. Applying the cross-quantilogram approach, our results lead to the following findings. First, we provide evidence that gold is a perfect hedge in Russia and India. While silver and platinum may be seen as hedge assets only in the China stock market. Adding to that, over the entire sample period, we find that extreme negative stock market returns were followed by extreme positive gold returns for all stock markets except for Brazil and China. Hence, gold is not a safe haven in these stock markets. However, silver is a safe haven only in China stock market and platinum is a safe haven in China and South Africa stock markets. Second, the estimation results of the partial cross-quantilogram (PCQ) reveal that the dependence structure across quantiles is found to be consistent, even after controlling for EPU.
This paper investigates the long memory properties of the prices series of two major precious metals (gold and silver) and six non-precious metals (aluminium, copper, lead, zinc, tin and nickel) by using a fractional integration modelling framework, while controlling for structural breaks and non-linearities. We use daily data in a range from November 01, 2007 to March 20, 2020. From the results, Copper and Tin exhibit a very small degree of mean reversion. However, if autocorrelation is permitted, the unit root null hypothesis cannot be rejected in any of the series. We also account for structural breaks and non-linearities. We observe that all the series under investigation are exposed to multiple breaks. Results obtained under white noise errors shows some evidence of mean reversion for silver aluminium, copper, tin and zinc in some of the subsamples. However, under the assumption of Bloomfield autocorrelated errors the confidence intervals are so wide that we cannot confirm this evidence in any single case.
COVID – 19 is not only impact on heath of the society but also influence the wealth of the country. Any disaster uncertainty affect directly or indirectly to the Economy. The main purpose behind this study is to know the relationship between Gold, Silver, Crude Oil and Exchange rate during Covid – 19. This research is based on Covid – 19 during the 1st Lockdown from April - 2020 to March – 2021. This study shows that there is correlation between Gold, Silver and Crude oil. There is an interdependence between Silver, Crude Oil and Exchange rate.
This article proposes a test to determine if two price series that each contain an explosive autoregressive regime consistent with the presence of a bubble are related in the sense that a linear combination of them is integrated of order zero. We refer to such a phenomenon as ‘co‐explosive behaviour’, and propose a test based on a stationarity testing framework. The test allows the explosive episode in one series to lead (or lag) that in the other by a number of time periods. We establish the asymptotic properties of the test statistic and propose a wild bootstrap procedure for obtaining critical values that are robust to heteroskedasticity. Simulations show that the proposed test has good finite sample size and power performance. An empirical application to detect whether co‐explosive behaviour exists among a set of precious and non‐ferrous metals is presented.
The study investigates long-run and short-run cointegrating relationship between stock market returns, fear index (VIX), brent crude oil prices and growth in deaths due to the COVID-19 pandemic for BRIC countries using daily data from 23 January 2020 to 24 August 2020 using Autoregressive Distributed Lag (ARDL) model. CUSUM test and serial correlation test estimates point towards the robustness of the model used. The evidence reveals that for India and Brazil, with the outbreak of COVID-19, decrease in crude oil prices and increase in volatility index, the stock returns started declining in the short run, but the impact has declined and the stock returns have regained in the long run. For China, due to the outbreak of COVID-19 and increase in fear index, stock returns declined in the short run, but the Chinese economy has recovered well due to a strong industrial and services sector. For Russia, increase in deaths due to COVID and decline in oil prices has impacted the stock returns in the long- and short run. Due to a decline of 53% for crude oil prices from January 2020 to May 2020, the Russian economy would face the consequences in the long run as well. The results suggest that though BRIC countries were impacted by growth in COVID-19 deaths, but the recovery trajectory and stability has resumed for all countries except Russia. Results of Granger Causality indicate a bidirectional causality between VIX and stock returns for the Indian market.
Using one-minute intraday data and wavelet decomposition of stochastic processes we obtain realised VCOV matrices with and without price discontinuities in the U.S. Treasuries and precious metals futures. Our work provides determinants of co-jumps in gold, silver and U.S. Treasuries across the yield curve and empirically demonstrates impact of price discontinues on hypothetical investor through realised correlations, hedging effectiveness ratios and several portfolio settings. We find that co-jumps in gold and silver have similar monetary characteristics to co-jumps in gold or silver with U.S. Treasuries futures. We further unpack investor choices between precious metals and U.S. bonds under the presence of high-frequency risks. We show that behaviour puzzle of simultaneous demand for safety and quality during market turmoils disappears if investors are seeking maximum diversification. We also find that runs to safety do not offer statistically significant improvements in diversification benefits unlike runs to short-term quality. Other results uncover higher investments to gold due to the shifts in the U.S. yield curve and potential gains in realised hedging effectiveness for the end of the yield curve investors through asymmetry in co-jumps of gold and U.S. Treasuries during periods of extreme market volatility such as beginning of the COVID-19 pandemic.
This study discuss the relation between gold demands and employees' salaries. It also examines the linkages between the board of director's remuneration and gold demands. We investigate the association between two forms of gold, i.e., gold jewellery and gold bars & coins between both categories of incomes. There are two sources of income, one of them is from organized sources of income, and another is from unorganized sources of income. However, approximately 92% of the workforce falls under the unorganized sectors, and 8% fall under the organized sectors. Our study is based on the only income of corporate sectors (i.e. formal sectors) which fall under the organized income group. In this study, the demand function is investigated in terms of corporate incomes and the price of the gold. This study provides analytical and empirical linkages between gold demands and corporate income during 2010–2019. It is an unbalanced panel data with 386,312 quarterly board of directors' remuneration from 2424 listed firms. We take quarterly employees' salaries paid by 2223 listed firms to their employees. We test the unit root test to check the stationarity of data. This paper uses the Two-Stage Least Squares Estimation and Error Correction Models to test the research hypotheses. We find a positive relation between gold jewellery and income and no significant association between the gold bars & coins and income.
This study aims to evaluate the links among gold price, oil price, exchange rate andinterest rate in Pakistan. All these channels are interconnected and have impact onmonetary policy of the country. Monthly data ranging from 1995-01 to 2016-12 is usedfor the analysis based on VAR Model. Exchange rate depreciations are responded bytight monetary policy actions, which seem to have a significant effect on exchange ratestabilization process and raise gold price. Changes in oil prices at global level stronglyaffect the nexus in Pakistan. Monetary policy managers are suggested to take changes ingold prices as indicators of short-run fluctuations in Pakistan economy. The studycontributes in two ways. Firstly, as a case study of Pakistan, it analyzes the role of goldmarket in response to changes in exchange rate and world oil prices. Secondly, the studylinks up monetary policy decisions to the nexus of gold price-oil price-exchange rate.Findings of the study may be useful for monetary policy makers, academia, and goldindustry alike.
This paper examines and compares financial market reaction and recovery of four broad classes of financial assets – equity indexes, precious metals, 10-year benchmark bonds and cryptocurrencies, to the COVID-19 pandemic. The data set comprises daily observations of close prices in the selected markets from 17-04-2018 to 20-06-2021. Using the Yang and Zhao (2020) and Koenker and Xiao (2004) quantile unit-root tests for return persistence, we find heterogeneity in reactions and recovery patterns not only across asset classes, but also within them. Specifically, we find strong potential for mean reversion in equity markets even at high levels of shocks. While gold offers limited mean reversion, platinum shows very strong resistance to the COVID. Government bonds show small declines in value to the COVID in addition to high persistence. Cryptocurrencies, as a group, turn out to be the riskiest in the long-term, with more than a 50% decline in value coupled with high degrees of persistence. Our results raise questions as to the safe haven characteristics of the newly-popular Bitcoin. Our findings are useful for policy makers and investors through a better understanding of differences in the potential for mean reversion provided by different asset classes.
Following the seminal paper on gold and silver prices by Escribano and Granger (1998), a growing number of empirical studies have studied the possible existence of a stable and cointegrating relationship between gold and silver prices. This paper examines the long-run equilibrium relationship between gold and silver prices in the emerging market of India using annual data for the sample period 1978–1979 to 2018–2019. We find robust evidence of a stable cointegrating relationship between gold and silver prices in India. The estimation of cointegrating relationship using six estimators provides robust evidence of a positive impact of gold prices on silver prices in the long-run. The results indicate that silver prices are quite responsive to gold prices in both the long-run and short-run in India. Our findings provide robust evidence of a stable long-run relationship between gold and silver prices in India. The results have important implications for predicting returns, hedging against similar risks, understanding the role of two commodities in portfolio diversification, and the nature of two markets over time.
The present paper investigates the long-run relationships between daily prices, stocks and fear gauges of gold and silver by employing an updated fractional cointegrating framework, that is, the Fractional Cointegrating Vector Autoregression (FCVAR). The initial unit root tests results indicate that the series are I(d)s with values of d around 1 in all cases, and these are homogenous in the paired cointegrating series. Evidence of cointegration is found in the three pairs (prices, stocks and market gauge indices), while these cointegrations are only time-varying in the case of market gauge indices for the commodities. The fact that cointegration exists in prices and stocks of gold and silver implies the possibility that gold and silver prices and stocks can interchangeably be used to access the performances of the commodity markets, with the recommendation that the two commodities are not to be traded in the same portfolio.
This paper analyzes the dynamics of U.S. lithium mining companies, the lithium industry and West Texas Intermediate (WTI) crude oil prices using a Fractional Cointegration Vector AutoRegressive model (FCVAR model) and a Continuous Wavelet Transform (CWT) for its resolution. The results indicate evidence of a negative relationship between FMC Corp with Albermale and SQM stock prices. These results are similar if we analyze the risk based on the beta term structure of each company. Analyzing the fractional differencing parameter for the stock prices and their logs, we observe that they are very persistent, and there are no long-term deviations in the stock prices. The same happens when analyzing the beta term structure. Based on Continuous Wavelet Transform (CWT) methods, our results show that lithium mining companies and the lithium industry are weakly correlated with WTI crude oil prices at higher frequencies (short-run) and persist through the sample period. At lower frequencies (long-term) the time series reached a high level of dependence between late 2012 to mid 2016, concluding that the lithium mining companies and the lithium industry reflect and foreshadow the responsiveness of the WTI crude oil prices during the period mentioned above.
This paper deals with the analysis of silver prices and the influence of solar energy on its behaviour. For this purpose, the analysis uses long memory methods based on fractional integration and cointegration. The results indicate that the two variables are very persistent, though any long run equilibrium relationship between them is not observed. Nevertheless, the results illustrate some short-run negative effects from solar energy capacity on silver prices.
This paper investigates the dependence structure among nominal crude oil (WTI), gold, and specific U.S. dollar against four major currencies (Euro, British Pound, Japanese Yen and Canadian Dollar) on a daily basis over the last decade. In order to capture the tail dependence between commodity market and USD exchange rates, we apply both bivariate zero tail and tail copulas, combined with the AR-GARCH marginal distribution for gold, oil and exchange rates daily returns. The primary findings are as follows. Firstly, based on the concordance and correlation coefficient, we find that there is a positive correlation between gold and crude oil prices, and a negative dependence between gold and currencies as well as oil and currencies. Secondly, the crude oil price can be viewed as a short term indicator in the exchange rates movement; the crude oil price also can be viewed as a short term descend indicator of gold price, while the gold price is an short term rise indicator of oil price. Thirdly, small degree of conditional extreme tail dependence for all considered pairs are observed. Our results provide useful information in portfolio diversification, asset allocation and risk management for investors and researchers.
This paper compares several methods (ordinary least squares, nonlinear least squares, maximum likelihood in an error correction model, principal components, and canonical correlations) of estimating cointegrating vectors. Although all of them are superconsistent, an empirical example shows that the estimates can vary significantly. The paper examines the asymptotic distribution of the estimators resulting from these methods, and shows that maximum likelihood in a fully specified error correction model (Johansen's approach) has clearly better properties than the other estimators. A Monte Carlo study indicates that finite sample properties are consistent with the asymptotic results. This is so even when the errors are non-Gaussian or when the dynamics are unknown.
This paper analyzes the long-run relationship between gold and silver prices. The three main questions addressed are: the influence of a large bubble from 1979:9 to 1980:3 on the cointegration relationship, the extent to which by including error correction terms in a nonlinear way we can beat the random walk model out-of sample and, the existence of a strong simultaneous relationship between the rates of return of gold and silver. Different efficient single equation estimation techniques are required for each of the three questions and this is explained within a simple bivariate cointegrating system. With monthly data from 1971 to 1990, it is found that cointegration could have occurred during some periods and specially during the bubble and post-bubble period. However, dummy variables for the intercept of the long-run relationships are needed during the full sample. For the price of gold the nonlinear models perform better than the random walk in-sample and out-of sample. In-sample nonlinear models for the price of silver perform better than the random walk but this predictive capacity is lost out-of sample, mainly due to the structural change that occurs (reduction) in the variance of the out-of sample models. The in-sample and out-of sample predictive capacity of the nonlinear models is reduced when the variables are in logs. Clear and strong evidence is found for a simultaneous relationship between the rates of return of gold and silver. In the three type of relationships that we have analyzed between the prices of gold and silver, the dependence is less out-of sample, possibly meaning that the two markets are becoming separated.
* Department of Statistics and Econometrics, Universidad Carlos III de Madrid
This paper contains the likelihood analysis of vector autoregressive models allowing for cointegration. The author derives the likelihood ratio test for cointegrating rank and finds it asymptotic distribution. He shows that the maximum likelihood estimator of the cointegrating relations can be found by reduced rank regression and derives the likelihood ratio test of structural hypotheses about these relations. The author shows that the asymptotic distribution of the maximum likelihood estimator is mixed Gaussian, allowing inference for hypotheses on the cointegrating relation to be conducted using the Chi(" squared") distribution. Copyright 1991 by The Econometric Society.
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.
The outlook for and inter-relationship between gold and silver
- M A Simon
Simon, M. A. (1996). The outlook for and inter-relationship between gold and silver. CPM Group Market Report,
www.cpmgroup.com/msimon_auvsag.html.