Hedging stock market prices with WTI, Gold, VIX and cryptocurrencies: a comparison between DCC, ADCC and GO-GARCH models
TL;DR: In this paper, the authors examined the dynamic correlations persistent between five cryptocurrencies, WTI, Gold, VIX and four stock markets (SP500, FTSE, NIKKEI and MSCIEM).
Abstract: In a first place, the present paper is designed to examine the dynamic correlations persistent between five cryptocurrencies, WTI, Gold, VIX and four stock markets (SP500, FTSE, NIKKEI and MSCIEM). In a second place, it investigates the relevant optimal hedging strategy.,Empirically, the authors examine how WTI, Gold, VIX and five cryptocurrencies can be applicable to hedge the four stock markets. Three variants of multivariate GARCH models (DCC, ADCC and GO-GARCH) are implemented to estimate dynamic optimal hedge ratios.,The reached findings prove that both of the Bitcoin and Gold turn out to display remarkable hedging commodity features, while the other assets appear to demonstrate a rather noticeable disposition to act as diversifiers. Moreover, the results show that the VIX turns out to stand as the most effectively appropriate instrument, fit for hedging the stock market indices various related refits. Furthermore, the results prove that the hedging strategy instrument was indifferent for FTSE and NIKKEI stock while for the American and emerging markets, the hedging strategy was reversed from the pre-cryptocurrency crash to the during cryptocurrency crash period.,The first paper's empirical contribution lies in analyzing emerging cross-hedge ratios with financial assets and compare hedging effectiveness within the period of crash and the period before Bitcoin crash as well as the sensitivity of results to refits choose to compare between short term hedging strategy and long-term one.
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TL;DR: In this article, the authors examined the relationship between the energy index, crude oil, gas prices, and financial assets (Gold, Bitcoin, and G7 stock indexes), especially during the coronavirus crisis.
Abstract: In this paper, we examine the relationship between the volatilities of the energy index, crude oil, gas prices, and financial assets (Gold, Bitcoin, and G7 stock indexes), especially during the coronavirus crisis. The study tests the presence of regime changes in the GARCH volatility dynamics of the G7 stock indexes, Bitcoin, Gold, and energy assets (energy index, oil, and gas) by using the Markov–Switching GARCH model. It estimates the dynamic correlation and volatility spillover between energy and financial assets, by using the multivariate MSGARCH models. The estimation results of the Markov-Switching-BEKK-GARCH prove the volatility spillover from energy assets to financial assets. For the high regime, the results indicate a high level of dynamic correlation between energy assets and stock indexes which proves the contagion effect of the COVID-19. On the contrary, the dynamic conditional correlation between energy assets and Gold prices decreased during the COVID-19 crisis. This paper makes an original contribution in identifying the contagion between energy and financial assets and indicates that Gold is a safe haven for all energy and financial assets during the COVID-19 crisis. However, Bitcoin cannot be considered as a safe haven during the COVID-19 pandemic when investing in energy assets (crude oil and gas).
15 citations
TL;DR: In this article , the authors examined the asymmetric relationship between six crypto-currencies and seven stock market prices (S&P500, CAC40, DAX30, NIKKEI, FTSE and FTSEMIB) accounting for the effects of Gold and WTI prices.
Abstract: Using the NARDL model for the period of pandemic COVID19, we examined the asymmetric relationship between six crypto-currencies (Bitcoin, Litecoin, Bitcoin gold, Dash, Maker, and Ehereum) and seven stock market prices (S&P500, CAC40, DAX30, NIKKEI, FTSE, FTSEMIB, and SPTSX) accounting for the effects of Gold and WTI prices. In the long run, our results revealed, in most cases, a positive asymmetric relationship between digital and financial assets, suggesting a weak safe haven role for crypto-currencies. The oil price (WTI) was also found to act as a diversifier. However, for, the results revealed, in most cases, a negative asymmetric relationship between the yellow metal and the different stock prices, suggesting that gold can act as a good hedging instrument or a safe haven against stock prices in the long run. On the other hand, in the short run, the results indicate that only Bitcoin, Litecoin, and Maker have an asymmetric effect on the chosen stock prices but the effect is positive in most cases. Moreover, gold can act as a hedge/safe-haven asset in the short run. Finally, while examining the dynamic response of stock prices to the negative and positive shocks of crypto-currencies, we concluded that the majority of stock prices respond more to the negative shocks of crypto-currencies than to the positive ones.
4 citations
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TL;DR: The authors examined the dependence of gold and benchmark bonds with ten stock markets including five large developed markets (eg USA, UK, Japan, Canada and Germany) and five Eurozone peripheral GIPSI countries (Greece, Ireland, Portugal, Spain and Ireland) stock markets.
Abstract: This paper examines the dependence of gold and benchmark bonds with ten stock markets including five larger developed markets (eg USA, UK, Japan, Canada and Germany) and five Eurozone peripheral GIPSI countries (Greece, Ireland, Portuguese, Spain and Ireland) stock markets We use a novel quantile-on-quantile (QQ) approach to construct the dependence estimates of the quantiles of gold and bond with the quantiles of stock markets The QQ approach, recently developed by Sim and Zhou (2015), captures the dependence between the entire distributions of financial assets and uncovers some nuance features of the relationship The empirical findings primarily show that gold is strong hedge and diversifier for the stock portfolio except when both the markets are under stress Further, the flight to safety phenomenon is short-lived because national benchmark bonds exhibit a positive dependence with their respective countries stock indices at various quantiles Unlike the existing literature, the QQ approach suggest that bonds act as safe havens for the stock portfolio but gold does not Our findings also suggest that dependence between stock-gold and stock-bond pairs is not uniform and this relationship is market state (eg bearish, mild bearish, optimistic or bullish) and country specific
4 citations
TL;DR: In this article , the authors collected and synthetized the existing knowledge on portfolio diversification, hedge, and safe-haven properties in cryptocurrency investments, and sampled 146 studies published in journals ranked in the Association of Business Schools 2021 journals list, considering all fields of knowledge.
Abstract: Our study collected and synthetized the existing knowledge on portfolio diversification, hedge, and safe-haven properties in cryptocurrency investments. We sampled 146 studies published in journals ranked in the Association of Business Schools 2021 journals list, considering all fields of knowledge, and elaborated a systematic literature review along with a bibliometric analysis. Our results indicate a fast-growing literature evidencing cryptocurrencies’ ability to hedge against stocks, fiat currencies, geopolitical risks, and Economic Policy Uncertainty (EPU) risk; also, that cryptocurrencies present diversification and safe-haven properties; that stablecoins reveal unstable peg with the US dollar; that uncertainty is a determinant for cryptocurrency returns. Additionally, we show that investors should consider Gold, along with the European carbon market, CBOE Bitcoin futures, and crude oil to hedge against unexpected movements in the cryptocurrency market.
4 citations
TL;DR: In this article , the authors investigated the risk-hedging effects of Bitcoin and Gold in the stock markets of the G7 countries and found that Bitcoin provides stronger short-term risk hedging compared to gold during the COVID-19 and Russo-Ukrainian War periods.
3 citations
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TL;DR: In this article, a modified GARCH-M model was used to find a negative relation between conditional expected monthly return and conditional variance of monthly return, using seasonal patterns in volatility and nominal interest rates to predict conditional variance.
Abstract: We find support for a negative relation between conditional expected monthly return and conditional variance of monthly return, using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, we also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility. THE TRADEOFF BETWEEN RISK and return has long been an important topic in asset valuation research. Most of this research has examined the tradeoff between risk and return among different securities within a given time period. The intertemporal relation between risk and return has been examined by several authors-Fama and Schwert (1977), French, Schwert, and Stambaugh (1987), Harvey (1989), Campbell and Hentschel (1992), Nelson (1991), and Chan, Karolyi, and Stulz (1992), to name a few. This paper extends that research.
7,837 citations
TL;DR: In this article, a new class of multivariate models called dynamic conditional correlation models is proposed, which have the flexibility of univariate generalized autoregressive conditional heteroskedasticity (GARCH) models coupled with parsimonious parametric models for the correlations.
Abstract: 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.
5,695 citations
TL;DR: This paper proposed a new generalized autoregressive conditionally heteroskedastic (GARCH) process, the asymmetric generalized dynamic conditional correlation (AG-DCC) model, which allows for series-specific news impact and smoothing parameters and permits conditional asymmetries in correlation dynamics.
Abstract: This paper proposes a new generalized autoregressive conditionally heteroskedastic (GARCH) process, the asymmetric generalized dynamic conditional correlation (AG-DCC) model The AG-DCC process extends previous specifications along two dimensions: it allows for series-specific news impact and smoothing parameters and permits conditional asymmetries in correlation dynamics The AG-DCC specification is well suited to examine correlation dynamics among different asset classes and investigate the presence of asymmetric responses in conditional variances and correlations to negative returns We employ the AG-DCC model to analyze the behavior of international equities and government bonds While equity returns show strong evidence of asymmetries in conditional volatility, little is found for bond returns However, both equities and bonds exhibit asymmetries in conditional correlations, with equities responding stronger than bonds to joint bad news The article also finds that, during periods of financial turmoil, equity market volatilities show important linkages, and conditional equity correlations among regional groups increase dramatically Furthermore, in January 1999 with the introduction of the euro, we document significant evidence of a structural break in correlation although not in
1,733 citations
TL;DR: The authors compare the restrictions imposed by the four most popular multivariate GARCH models, and introduce a set of robust conditional moment tests to detect misspecification, and demonstrate that the choice of a multivariate volatility model can lead to substantially different conclusions in any application that involves forecasting dynamic covariance matrices (like estimating the optimal hedge ratio or deriving the risk minimizing portfolio).
Abstract: Existing time-varying covariance models usually impose strong restrictions on how past shocks affect the forecasted covariance matrix. In this article we compare the restrictions imposed by the four most popular multivariate GARCH models, and introduce a set of robust conditional moment tests to detect misspecification. We demonstrate that the choice of a multivariate volatility model can lead to substantially different conclusions in any application that involves forecasting dynamic covariance matrices (like estimating the optimal hedge ratio or deriving the risk minimizing portfolio). We therefore introduce a general model which nests these four models and their natural 'asymmetric' extensions. The new model is applied to study the dynamic relation between large and small firm returns. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.
1,310 citations
TL;DR: In this article, a bivariate error correction model with a GARCH error structure was proposed to estimate the risk-minimizing futures hedge ratios for several currencies and a dynamic hedging strategy was proposed in which the potential risk reduction is more than enough to offset the transactions costs for most investors.
Abstract: Most research on hedging has disregarded both the long-run cointegrating relationship between financial assets and the dynamic nature of the distributions of the assets. This study argues that neglecting these affects the hedging performance of the existing models and proposes an alternative model that accounts for both of them. Using a bivariate error correction model with a GARCH error structure, the risk-minimizing futures hedge ratios for several currencies are estimated. Both within-sample comparisons and out-of-sample comparisons reveal that the proposed model provides greater risk reduction than the conventional models. Furthermore, a dynamic hedging strategy is proposed in which the potential risk reduction is more than enough to offset the transactions costs for most investors.
1,182 citations