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The influential impacts of international dynamic spillovers in forming investor preferences: a quantile-VAR and GDCC-GARCH perspective
Applied Economics
Abstract
This study investigates whether representative sectoral stock indices, gold, oil, Bitcoin, and wheat canmitigate risk and improve portfolio performance during normal times versus crises. The cutting-edgeQuantile Vector Autoregressive model and the Generalized Dynamic Conditional Correlations(Generalized-DCC) framework are adopted covering from 9 January 2017 until 30 August 2022.Econometric findings by the Q-VAR reveal that oil presents the strongest connection with commod-ities and stock indices and that Bitcoin and wheat despite their significant linkages with financialmarkets fail to act as safe havens. Moreover, GDCC-GARCH indicates that the returns of sectoralindices are weakly related but display powerful volatility co-movements. Gold serves efficiently asa hedger and oil follows and both act as better shelters during crises. Nevertheless, Bitcoin partlyabides by conventional markets in stressed periods. Notably, wheat reliably works as a hedger overallbut does not become a safe haven during crises.
... It means that organizations embrace environmentally responsible behaviors within their setup such as energy saving, waste reduction and recycling [39] . Employee engagement, training and development, and performance management are some of the methods used to achieve green HRM by managers across different organizations [40] . ...
... For example, organizations adopting green HRM practices were found to have low carbon emissions and waste generation when compared with those who did not adopt them. Another study conducted by Gazi et al. [38] also indicated that green HR activities were positively training, performance appraisal, compensation management, and employee relations, among others [39,40] . Additionally, some studies have underscored the importance of global human resource management for environmental sustainabil- ...
This study delves into Green Human Resource Management (GHRM) as well as its link with organizational sustainability. Through a review of literature, this research hopes to fathom the place of eco-sustainability in employee involvement and enhancing environmental responsibility via Green HRM. The main aim of this study is to thoroughly examine the literature on GHRM and its contribution to improving organizational sustainability through employee engagement and diminished environmental impact. The PRISMA guidelines were utilized in this conceptual review in order to investigate the impact that Green HRM has on the sustainability of organizations. The results show that Green HRM is vital for organizational sustainability since it reduces their ecological footprint, raises employee morale, and improves reputation. Additionally, the study emphasizes the significance of including Green HRM practices into organizational strategies and operations so as to achieve long-term sustainability. Findings indicate that through implementing green HRM practices organizations can record increased employee engagement levels, reduced turnover rates and improved customer satisfaction. Moreover, there are lower costs associated with green human resource management practices because of less waste and a better image for the brand. Nonetheless, some challenges and constraints towards change have been observed in relation to the implementation of these practices by companies. This study therefore gives an overview of how green HRM enhances organizational sustainability, thereby urging firms to adopt sustainable HR practices for successful operations in the future. These findings are important for human resource professionals, policymakers, or organizations who want to embed sustainability into their business strategy.
... Per prior studies (Dimitriadis et al., 2024a;Mazur & Polyzos, 2024), cryptos and NFTs were developed as innovative hedging instrument in response to the thriving liquidity tests facing the worldwide economic systems amid incessant chaos. Despite the supposed resilience of these assets, recent global events (pandemics, political tensions, conflicts, and policy uncertainty) have exposed their vulnerability, with numerous chaos and crashes being the consequence (Beneki et al., 2019;Biju & Thomas, 2023;Dimitriadis et al., 2024b;Sadorsky & Henriques, 2024;Woode et al., 2024a). For instance, the cryptos market faced a tragic meltdown in May 2022, unleashing an upheaval that echoed the foundations of these assets, with trails of bankruptcies and a loss in excess of $1.3 billion. ...
Responding to the fluctuations in the digital asset markets against the incessant turmoil and the limited studies on the role of varying uncertainty measures in driving these assets, we examine the predictive capacity of uncertainty (world uncertainty, geopolitical risk, crypto volatility, and crypto uncertainty) on the most capitalized and tradable non-fungible tokens (NFTs) (Bored Ape Yacht Club, Bored Ape Kernel Club, Cryptopunks, Doodles, Mutant Ape Yacht Club, and Puddy Penguins) from April 2021 to October 2024. The quantile regression results highlight an adverse (positive) nexus between NFTs and WUI and VCRIX (UCRP and GPR), with WUI being the most robust factor in driving NFTs. The blend of adverse (positive) influence suggests that the NFT market possesses a diverging capacity in terms of hedge and safe haven, with WUI being the most detrimental measure derailing NFT-based investors from such luxury. UCRP positively influences NFTs, while ETH and BTC fail in such capacity. GPR has a limited adverse (positive) influence on BAYC and DUD (MAYC), indicating that only the latter can hedge against GPR. These results support the quantile-on-quantile estimations, highlighting the adaptive and heterogeneous influence of the sample uncertainties on NFTs. The causality-in-quantile results highlighted the predictive capability of all the uncertainty measures, with WUI and UCRP providing the most robust results across most of the quantiles, with GPR and VCRIX possessing limited influence, emphasizing the robust nature of these uncertainty measures on NFTs. These results provide insights into investor heterogeneity and adaptive behavior and the limited potential for diversification within the NFT markets. The results also have implications for NFT-based investors and ventures, speculators, market regulators and policies, and risk management.
... This paper is connected with previous empirical studies that examine traditional and modern financial assets with emphasis on impacts during crises (Shahzad et al., 2019;Mariana et al., 2021;Bejaoui et al., 2023, Dimitriadis et al., 2024. The topic examined is tightly linked with the evolution of money and innovation in financial technology (Reiss, 2018;Kyriazis, 2020;Camera, 2024). ...
This paper aims to cast light on the dynamic linkages of traditional, modern and very modern forms of liquidity and ameliorate the level of knowledge about the feasibility of transition to a new frictionless digital economy with circulation of decentralized forms of private money. In order to accomplish this, the innovative Quantile-VAR methodology as in Cunado et al. (2023) is employed that makes possible to assess the dynamic total directional connectedness at lower and upper quantiles. This work builds on and contributes to earlier high-quality research such as in Koop et al. (1996), Pesaran and Shin (1998) and Yousaf et al. (2024). The variables used cover the major representatives of internationally-used fiat national currencies and the most prominent (in terms of market capitalization) representatives of major cryptocurrencies, Decentralized Finance and Non-Fungible Tokens digital assets during the Russia-Ukraine conflict as the most sophisticated forms of digital money were only embryonically adopted before this period.
This paper examines the dynamic interplay between the global geopolitical risk and eleven decentralized finance (DeFi) digital currencies during the inflationary burden caused by the Russia-Ukraine war episodes. Daily data spanning from 13 October 2021 to 29 October 2024 and the innovative Quantile-Vector Autoregressive (Q-VAR) methodology are employed for estimating the pairwise, joint and network linkages at the lower, middle and upper quantiles. High levels of geopolitical risk are more connected with bull markets of the DeFi assets and new war episodes strengthen this relation. Geopolitical tensions combined with high inflation lead to the GPR becoming major determinant of DeFi markets so contributing to the transition to the digital decentralized cashless financial system. Maker is the leading DeFi asset in this transition and constitutes a promising successor of fiat currencies that suffer from devaluation generated by conflicts.
This paper investigates the impact that the ‛green character’ of stocks generates on their market price of risk. The
study covers a spectrum of international large-cap stocks in influential sectors since 1 January 2013 up to 5 July
2022. Focusing on the asymmetric and shape features of price risk, econometric outcomes reveal that wellestablished
(Industrials) or highly profitable (Technology) sectors are less affected by the skewness/kurtosis
price of risk or the “environmental-friendly” features. On the contrary, sectors vulnerable to alterations in income
or wealth, such as Financials and Real Estate are more sizably influenced by negative asymmetry, kurtosis, and
the higher volatility in bear markets. The ‛green character’ lowers the market price of stocks, increases risk
premia due to higher probability for downwards movements and renders investors more risk-averse and
nevertheless, this impact only reflects the initial period of green innovation with high anti-polluting costs as the
green label could prove to be greatly beneficial in the future.
This study investigates the dynamic nexus that major international currencies (US dollar, Euro, Japanese yen)
exhibit with cryptocurrencies and highly innovative digital money (DeFi and NFT assets) during inflationary
periods such as the Russia-Ukraine conflict (from 14 December 2021 until 1 March 2024). The Quantile Vector
Autoregressive methodology as in Cunado et al. (2023) and daily data are adopted to investigate the net joint
extended dynamic connectedness and network connectedness at lower and upper quantiles. Conventional international
currencies act as hedgers against shocks while major cryptocurrencies are only modest generators
with Ripple being an influential absorber of effects. DeFi mainly serve for counteracting losses from conventional
investments in bear or bull markets and Maker is the most prominent generator of spillovers while NFTs mostly
rely on a few very strong leaders –Gala being by far the strongest- to have an impact, imitating Bitcoin in the
early cryptocurrency era.
The driving forces behind cryptoassets’ price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes. Fundamental components are believed to have a weak, if any, role in the price-formation process. This study examines five cryptoassets with different backgrounds, namely Bitcoin, Ethereum, Litecoin, XRP, and Dogecoin between 2016 and 2022. It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events. The findings show that the price and return dynamics of all the studied assets, except for Dogecoin, emerge from complex interactions between fundamental and speculative components, including episodes of price bifurcations. Bitcoin shows the strongest fundamentals, with on-chain activity and economic factors driving the fundamental part of the dynamics. Investor attention and off-chain activity drive the speculative component for all studied assets. Among the fundamental drivers, the analyzed cryptoassets present their coin-specific factors, which can be tracked to their protocol specifics and are economically sound.
The spreading COVID-19 outbreak has wreaked havoc on the world's financial system that raises an urgent need for the re-evaluation of the gold as safe haven for their money because of the unprecedented challenges faced by markets during this period. Therefore, the current study investigates whether different asset class volatility indices affect desirability of gold as a safe-haven commodity during COVID-19 pandemic. Long run and the short run relationship of gold prices with gold price volatility, oil price volatility, silver price volatility and COVID-19 (measured by the number of deaths due to COVID) has been analyzed in the current study by applying ARDL Bound testing cointegration and non linear ARDL approach on daily time series data ranging from January 2020 to Dec 2021. Findings of the study suggest that in the long run, oil price volatility and gold price volatility positively affect the gold prices, whereas the effect of silver price volatility on gold prices is negative in the long run. However in the short run, all the three indices negatively impact the gold prices. In contrast, the impact of COVID-19 is positive both in the short run and in the long run that proves the validity of gold as safe haven asset in the time of the deadly pandemic. The findings of this study have significant implications and offer investors with some indications to hedge their investments by considering the gold’s ability of safe haven during this era of pandemic.
We examine volatility connectedness of 11 sectoral indices in the US using daily data from January 01, 2013 to December 31, 2020. We employ the connectedness measures of Diebold and Yilmaz (2009, 2012, 2014), unveiling changes in sectoral connectedness and stylized facts regarding specific sectors during the COVID-19 pandemic. Among several results, we find extraordinary increase in total connectedness, from early stages of international spread to the end of July 2020; some relevant changes in the pairwise connections between sectors, especially among the originally stronger ones. However, in a total net connectedness perspective, there is little evidence of structural changes.
This study introduces volatility impulse response functions (VIRF) for dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) models. In addition, the implications with respect to network analysis—using the connectedness approach of Diebold and Y ιlmaz (Journal of Econometrics, 2014, 182(1), 119–134)—is discussed. The main advantages of this framework are (i) that the time‐varying dynamics do not underlie a rolling‐window approach and (ii) that it allows us to test whether the propagation mechanism is time varying or not. An empirical analysis on the volatility transmission mechanism across foreign exchange rate returns is illustrated. The results indicate that the Swiss franc and the euro are net transmitters of shocks, whereas the British pound and the Japanese yen are net volatility receivers of shocks. Finally, the findings suggest a high degree of comovement across European currencies, which has important portfolio and risk management implications.
Research in cryptofinance has continued to consider whether Bitcoin possesses a safe haven property as traditionally defined by its correlation with other assets during times of market stress. However, this neglects other attributes of assets that are important to investors during periods of crisis. Bitcoin is more volatile, less liquid, and costlier to transact (in terms of time and fees) than other assets (including gold - the traditional safe haven) even in normal market conditions. Until the market matures, it is therefore unlikely to be worthwhile considering Bitcoin as a safe haven.
We study the dynamics of stock market integration and its consequences during the recent financial crisis for twenty-three
developed and sixty emerging markets. We find that integration increased slightly for emerging markets but decreased for developed
countries during the crisis. Moreover, we argue that the high degree of integration propagated the crisis across the global
financial markets at the beginning of the crisis, but it had little effect during the crisis. We also find that integration
is mostly affected by financial openness, the institutional environment, and global financial uncertainty but that these determinants
vary slightly between emerging and developed markets.
In this article, we put forward a generalization of the Dynamic Conditional Correlation (DCC) Model of Engle (200210.
Engle , R. F. ( 2002 ). Dynamic conditional correlation – A simple class of multivariate generalized autoregressive conditional heteroskedasticity models . Journal of Business and Economic Statistics 20 : 339 – 350 . [Taylor & Francis Online], [Web of Science ®]View all references). Our model allows for asset-specific correlation sensitivities, which is useful in particular if one aims to summarize a large number of asset returns. We propose two estimation methods, one based on a full likelihood maximization, the other on individual correlation estimates. The resultant generalized DCC (GDCC) model is considered for daily data on 39 U.K. stock returns in the FTSE. We find convincing evidence that the GDCC model improves on the DCC model and also on the CCC model of Bollerslev (19903.
Bollerslev , T. ( 1990 ). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH approach . Review of Economics and Statistics 72 : 498 – 505 . [CrossRef], [Web of Science ®]View all references).
This paper investigates the presence of asymmetric conditional second moments in international equity and bond returns. The analysis is carried out through an asymmetric version of the Dynamic Conditional Correlation model of Engle (2002). Widespread evidence is found that national equity index return series show strong asymmetries in conditional volatility, while little evidence is seen that bond index returns exhibit this behaviour. However, both bonds and equities exhibit asymmetry in conditional correlation. Worldwide linkages in the dynamics of volatility and correlation are examined. It is also found that beginning in January 1999, with the introduction of the Euro, there is significant evidence of a structural break in correlation, although not in volatility. The introduction of a fixed exchange rate regime leads to near perfect correlation among bond returns within EMU countries. However, equity return correlation both within and outside the EMU also increases after January 1999. JEL Classification: F3; G1; C5.
This study uses time-frequency analysis to examine directional connectedness between US sector equity ETFs, oil, gold, stock market, and uncertainty factors over the short and long terms. Frequency-based spillover index and portfolio hedging methods are applied to develop the empirical results. In analysing directional connectedness, we find that the market's 30-day forward looking expectations of US stock market volatility (VIX) has the strongest effect on the US sector equity ETFs in both the short and long runs. This is followed by the 30-day forward looking expectations of oil price volatility (OVX). Of the uncertainty factors, US economic policy uncertainty has the smallest impact on sector ETFs. Oil has a stronger effect on sector ETFs than gold in the short and long runs. Spillovers between the sector ETFs, gold, oil, and uncertainty factors, are asymmetric for both short and long runs, stronger in the short term, and noticeably increase in times of financial turmoil and economic uncertainty. Portfolio hedging results show that oil is the most effective hedge for each sector ETF in both the short term and the long term. The highest hedging effectiveness is observed for the Consumer Staples ETF.
Traditional finance theory posits a positive risk–return relation, but empirical evidence is inconclusive. Retail investor sentiment has long been viewed as a distorting factor, while more recently institutional investor sentiment is thought to play a role. We examine the separate and joint impacts of retail and institutional investor sentiments on the risk‐return relation. We find, at both market and firm levels, the risk‐return relation is more likely to be distorted by the two investor‐type sentiments jointly, rather than separately. We further find a cross‐sectional pattern, with the risk‐return relation being more sensitive to investor sentiment for stocks with specific characteristics.
The COVID-19 pandemic has led to an unprecedented shift of consumption from services to goods. We study this demand reallocation in a multi-sector model featuring sticky prices, input-output linkages, and labor reallocation costs. Reallocation costs hamper the increase in the supply of goods, causing inflationary pressures. These pressures are amplified by the fact that goods prices are more flexible than services prices. We estimate the model allowing for demand reallocation, sectoral productivity, and aggregate labor supply shocks. The demand reallocation shock explains a large portion of the rise in U.S. inflation in the aftermath of the pandemic.
Purpose
This study investigate the return and volatility spillover among agricultural commodities and emerging stock markets during various crises, including the COVID-19 pandemic and the Russian-Ukrainian war.
Design/methodology/approach
This return and volatility spillover is estimated using Diebold and Yilmaz (2012, 2014) approach.
Findings
The results reveal the weak connectedness between agricultural commodities and emerging stock markets. Corn and sugar are the highest and lowest transmitters, respectively, whereas soya bean and coffee are the largest and smallest recipients of spillover over time. Most equity indices are the net recipient except for India, China, Indonesia, Argentina and Mexico, during the entire sample period. Most commodities are net transmitters of volatility spillover except coffee and soya bean. At the same time, major equity indices are the net recipient of the volatility spillover except for India, Indonesia, China, Argentina, Malaysia and Korea. In addition, the return and volatility spillover increase during various crises like the COVID-19 pandemic and the Russian-Ukrainian war, but the major increase in spillovers occurs during the COVID-19 pandemic.
Practical implications
The empirical results show a weak relationship between agricultural commodities and emerging stock markets which is helpful for investors and portfolio managers in the construction and reallocation of their portfolios under different periods, most notably under COVID-19 and the Russian-Ukrainian war.
Originality/value
It is an original paper.
This paper investigates the non-linear causal nexus at lower, medium, and upper quantiles that cryptocurrencies can generate as a hedging mechanism for international benchmark indices, as represented by the MSCI World index and its sectoral sub-indices. Significant causality-in-mean between each of the assets examined is detected at lower quantiles only in a small number of sectors, whereas all analysed assets are found to trigger important non-linear causal impacts on the volatility of the selected benchmark indices, particularly during periods denoted to possess significant asset price fluctuations. From a financial perspective, this result suggests that cryptocurrencies are developing to possess a very similar ability to that of long-standing traditional financial assets, serving as a hedging mechanism against extreme fluctuations within investor portfolios that include MSCI constituents during a variety of very different market conditions.
The spillover effect is a significant factor impacting the volatility of commodity prices. Unlike earlier studies, this research uses the rolling window-based Quantile VAR (QVAR) model to describe the conditional volatility spillover between energy, biofuel and agricultural commodity markets. Since the magnitude of connectedness and spillover effects may switch between bearish and bullish market states over time, a QVAR model is a relatively realistic and appropriate approach to capture the connectedness as compared to the mean-based approaches of Diebold and Yilmaz (DY; 2009, 2012, & 2014) which are mostly used in the literature. To this end, we employ volatility estimates by using the realized variance advanced by Parkinson (1980). Specifically, we investigate the time-varying volatility spillovers and connectedness among agricultural markets (wheat, corn, sugar, soyabean, coffee, and cotton), energy markets (gasoline, crude oil, natural gas) and biofuel (ethanol) markets from January 12, 2012 to May 10, 2021. By comparing our empirical analysis with results from the DY spillover model, we establish that connectedness is stronger in the left and right quantiles than those in the mean and median of the conditional distribution, emphasizing the importance of systematic risk spillovers during extreme market movements. Furthermore, results find that volatility spillovers and connectedness in the right tail is higher than in the left tail. In particular, we document significant volatility spillovers from agricultural markets to energy markets during extreme markets conditions and observe the dominance of agricultural markets over energy markets. To ascertain the impact of COVID-19 on the volatility of markets examined, we divide our sample into sub-samples and observe significant variation in the level of volatility spillovers and connectedness across the markets before and during the outbreak of COVID-19. Finally, some useful implications are summarized for investors' portfolios and risk avoidance.
Our research examines connectedness landscape and the dynamic spillovers of volatility and returns in the network comprising eleven agricultural commodities and US shadow short rate in a quality of inflation indicator, observed in monetary policymaking. We use daily data from November 2000 to May 2021. We document monetary policy as a net transmitter of spillover for both return and volatility of agricultural commodities. We find significant volatility connectedness among agricultural commodities. The connectedness is shown to be of a time-varying nature, exhibiting considerable increases during periods of market turmoil. The results of this paper provide relevant insights into the interrelations of US monetary policy and agricultural commodity prices, being, hence, potentially useful for commodity investors, brokers and dealers, as well as for market regulators designing policy solutions for financial stability enhancements.
This study compares the dynamic spillover effects of gold and Bitcoin prices on the oil and stock market during the COVID-19 pandemic via time-varying parameter vector autoregression. Both time-varying and time-point results indicate that gold is a safe haven for oil and stock markets during the COVID-19 pandemic. However, unlike gold, Bitcoin's response is the opposite, rejecting the safe haven property. Further analysis shows that the safe-haven effects of gold on the stock market become stronger when the pandemic critically spreads.
Is clean energy a safe haven for cryptocurrencies, or vice versa? In this paper, we investigate the hedge and safe haven property of a wide range of clean energy indices against two distinct types of cryptocurrencies based on their energy consumption levels, termed “dirty” and “clean”. Statistical evidence shows that clean energy is not a direct hedge for either of types. However, it serves as at least a weak safe haven for both in extreme bearish markets. Moreover, clean energy is more likely to be a safe haven for dirty cryptocurrencies than clean cryptocurrencies during increased uncertainty. We further study the spillover patterns among clean energy, cryptocurrency, stock, and gold markets. Weak connectedness is found between clean energy and cryptocurrencies which implies the potential use of clean energy as a hedge and diversification tool for cryptocurrencies in the future.
This paper investigates the decline in performance of the carry strategy after the Global Financial Crisis (GFC) from a risk-based asset pricing perspective. We put forward and test four hypothesis that attribute the performance deterioration to under-diversification, a decline in systematic risk, downside risk, or crash risk, respectively. Overall, we attribute the decline in carry performance can be largely attributed to the disappearance of the downside risk in the post-crisis carry trade portfolio, rather than under-diversification, changing exposure to known risk factors, or decreased crash risks in currencies.
We investigate 1-year interest rate swaps on USD, EUR, JPY and GBP between 2005 and 2020 utilising a quantile connectedness model. This approach allows for a nuanced investigation of connectedness and adds to understanding the monetary policy transmission mechanism within a highly integrated international financial system. Substantial interest rate changes (in either direction) matter for connectedness in financial markets. The results also indicate which currency drives developments depending on the direction of the change in interest rates. The full implementation and replication code — based on R, is available at: https://github.com/GabauerDavid/ConnectednessApproach.
This paper examines the asymmetric return spillovers between crude oil futures, gold futures and ten sector stock markets of China. The results show using the spillover index of Diebold and Yilmaz (2012, 2014) time-varying asymmetry spillovers among commodity and the ten sectors. Industrials and consumer discretionary sectors are the largest contributor and receiver of spillovers in the system. In addition, basic materials sector is a net contributor of spillovers whereas oil futures, gold futures and the remaining sectors are net receiver of spillovers. Furthermore, the bad return spillovers dominate the good return spillovers. The asymmetry spillovers are influenced by the global financial and European crises (GFC & ESDC), oil price crash and global health crisis (COVID-19 outbreak). Equity investors benefit from adding gold and oil to their individual equity markets. Moreover, the hedging is sensitive to the GFC & ESDC, oil price crash, and COVID-19 outbreak. Finally, the highest hedging effectiveness occurs during COVID-19 spread for the case of oil futures. The result is similar for gold under only good spillovers and it is highest during recovery period under bad spillovers.
This study examines the safe haven prowess of gold against some exogenous shocks due to the COVID-19 pandemic. We further make a comparison of our findings with those obtained for the period before it. Our results confirm the potential of gold market to serve as a safe haven during the pandemic albeit with a higher effectiveness before the pandemic. Further results suggest that gold consistently offers better safe haven properties than the US stocks as well as other precious metals like Silver, Palladium and Platinum regardless of the period. Finally, we find that the predictive model that accounts for uncertainties outperforms the benchmark model that ignores the same both for the in- and out-of-sample forecast analyses.
This paper surveys the academic literature concerning the formation of pricing bubbles in digital currency markets. Studies indicate that several bubble phases have taken place in Bitcoin prices, mostly during the years 2013 and 2017. Other digital currencies of primary importance, such as Ethereum and Litecoin, also exhibit several bubble phases. The Augmented Dickey Fuller (ADF) as well as the Log-Periodic Power Law (LPPL) methodology are the most frequently employed techniques for bubble detection and measurement. Based on much academic research, Bitcoin appears to have been in a bubble-phase since June 2015, while Ethereum, NEM, Stellar, Ripple, Litecoin and Dash have been denoted as possessing bubble-like characteristics since September 2015. However, this latter group possess little academic evidence supporting the presence of bubbles since early 2018. An overall perspective is provided based on a robust bibliography based on large deviations of market quotes from fundamental values that can serve as a guide to policymakers, academics and investors.
We compare the performance of safe-haven assets during the Global Financial Crisis (GFC) and COVID-19 pandemic. First, regarding the GFC, we find, intermediate (weak) safe haven evidence for US dollar, Swiss franc and T-bonds (Gold, Silver and T-bills). Second, with regard
to COVID, we find gold is very risky in some settings, while silver has become extremely risky. Collectively, our findings suggest that the character of safe-haven assets has changed between the crises. Therefore, investors should exercise extreme care when investing in
potential safe-haven assets during times of market stress.
After the financial liberalization in the emerging economies, their stock markets have grown very rapidly in terms of value and volumes. However, a sharp increase in the prices of strategic commodities like oil and gold can have negative effects on the macroeconomics of the emerging economies and their stock markets. This study analyses the dynamic relationship between oil, gold and stock market returns in Turkey. It particularly investigates volatility spillover from oil and gold to the Borsa Istanbul Stock Exchange Index after the global financial crises. Movement of the BIST index with the international oil and gold prices are examined by using different versions of the SVAR-DCC-GARCH framework. The bootstrap causality test which accounts for the non-normal distribution of errors is utilized to specify the SVAR model. Our results support the presence of time-varying co-movement and volatility spillover from gold and oil to the Turkish stock market. Volatilities are high, and gold has stronger impact on the stock market than oil; therefore, gold cannot be used as a safe haven against volatility risk. The results imply that Turkey needs dynamic macroeconomic policies to manage the spillover effects of volatility after the global crisis.
This letter explores the behavior of conditional correlations among main cryptocurrencies, stock and bond indices, and gold, using a generalized DCC class model. From a portfolio management point of view, asset correlation is a key metric in order to construct efficient portfolios. We find that: (i) correlations among cryptocurrencies are positive, albeit varying across time; (ii) correlations with Monero are more stable across time; (iii) correlations between cryptocurrencies and traditional financial assets are negligible.
Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two-step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.
We assess the role of gold as a safe haven or hedge against the US dollar (USD) using copulas to characterize average and extreme market dependence between gold and the USD. For a wide set of currencies, our empirical evidence revealed (1) positive and significant average dependence between gold and USD depreciation, consistent with the fact that gold can act as hedge against USD rate movements, and (2) symmetric tail dependence between gold and USD exchange rates, indicating that gold can act as an effective safe haven against extreme USD rate movements. We evaluate the implications for mixed gold-currency portfolios, finding evidence of diversification benefits and downside risk reduction that confirms the usefulness of gold in currency portfolio risk management.
This paper develops a model of the social and economic interaction of speculators in a securities or foreign exchange market. Both chartist and fundamentalist strategies are pursued by traders. The formalization of chartists behavior combines elements of mimetic contagion and trend chasing leading to waves of optimism or pessimism. Furthermore, changes of strategies from chartist to fundamentalist behavior and vice versa occur because speculators compare the performance of both strategies. The dynamic system under study encompasses the time development of the distribution of attitudes among traders as well as price adjustment. Chaotic attractors are found within a broad range of parameter values. The distributions of returns derived from chaotic trajectories of the model share important characteristics of empirical data: they exhibit high peaks around the mean as well as fat tails (leptokurtosis) and become less leptokurtotic under time aggregation.
This study shows the influence of investor sentiment on the market's mean–variance tradeoff. We find that the stock market's expected excess return is positively related to the market's conditional variance in low-sentiment periods but unrelated to variance in high-sentiment periods. These findings are consistent with sentiment traders who, during the high-sentiment periods, undermine an otherwise positive mean–variance tradeoff. We also find that the negative correlation between returns and contemporaneous volatility innovations is much stronger in the low-sentiment periods. The latter result is consistent with the stronger positive ex ante relation during such periods.
The aim of this paper is to examine the role of gold in the global financial system. We test the hypothesis that gold represents a safe haven against stocks of major emerging and developing countries. A descriptive and econometric analysis for a sample spanning a 30Â year period from 1979 to 2009 shows that gold is both a hedge and a safe haven for major European stock markets and the US but not for Australia, Canada, Japan and large emerging markets such as the BRIC countries. We also distinguish between a weak and strong form of the safe haven and argue that gold may act as a stabilizing force for the financial system by reducing losses in the face of extreme negative market shocks. Looking at specific crisis periods, we find that gold was a strong safe haven for most developed markets during the peak of the recent financial crisis.
This paper addresses two questions. First, we investigate whether gold is a hedge against stocks and/or bonds and second, we investigate whether gold is a safe haven for investors if either stocks or bonds fall. A safe haven is defined as a security that loses none of its value in case of a market crash. This is counterpoised against a hedge, defined as a security that does not co-move with stocks or bonds on average. We study constant and time-varying relationships between stocks, bonds and gold in order to investigate the existence of a hedge and a safe haven. The empirical analysis examines US, UK and German stock and bond prices and returns and their relationship with the Gold price. We find that (i) Gold is a hedge against stocks, (ii) Gold is a safe haven in extreme stock market conditions and (iii) Gold is a safe haven for stocks only for 15 trading days after an extreme shock occurred.
Global Financial Crisis
Global Financial Crisis." Journal of International Financial
Markets, Institutions and Money 76:101460. https://doi.org/
10.1016/j.intfin.2021.101460.