Does economic policy uncertainty drive the dynamic spillover among traditional currencies and cryptocurrencies? The role of the COVID-19 pandemic (original) (raw)
Related papers
Resources Policy, 2020
This study examines the dynamic connectedness between three identified structural oil price shocks and the gold price based on the time-varying parameter vector autoregression (TVP-VAR) combined with the spillover's measures of Diebold and Yílmaz (2014). The three oil shocks are disentangled using the recent procedure of Ready (2018), which allows one to identify oil supply, oil demand, and oil risk shocks. Using daily data covering the period between January 2, 1997 and January 30, 2019, the results reveal a weak average of the total dynamic connectedness between the oil price shocks and the gold returns, manifesting sudden upsurges during turmoil periods. We also find that the oil supply shocks are the dominant transmitter of the time-varying spillovers from oil to the gold market, followed by the oil risk shocks, while the oil demand shocks are a net receiver of the spillovers from the gold market. Performing further analysis, we did not find any asymmetric patterns of dynamic connectedness between the oil shocks and the gold returns, regardless of the signs of the oil shocks. Besides, the results indicate a significant effect of the economic policy uncertainty on the connectedness between the oil shocks and the gold market in both the static and regime-switching framework. These findings have important implications for investors in regard to the hedging and safe-haven properties of gold against the identified oil shocks.
Connectedness of stock markets with gold and oil: New evidence from COVID-19 pandemic
Finance Research Letters, 2021
This paper sets out to explore the impact of COVID-19 pandemic on the dynamic connectedness among gold, oil and five leading stock markets by applying a new DCC-GARCH connectedness approach. We find stronger connectedness between these markets during the COVID-19 pandemic than in the pre-pandemic period. We also find that during this pandemic, gold is a receiver of shocks from the five stock markets, whereas the oil is a net transmitter of shocks.
Covid-19 and oil and gold price volatilities: Evidence from China market
Resources Policy
Gold and crude oil are the influential commodities of the stock markets and real economy of the world in financial crises as well as in COVID-19 periods. However literature mainly focused on the effects of these commodities' prices only, and the volatilities in the prices of these commodities altogether with the prices got little attention. To fill in a major research gap, our study intends to estimate the dynamic relationship between oil prices, gold prices, oil prices volatilities and gold prices volatilities on the stock market of China. Using daily data over the period from 2009 to 2021, the study applied Autoregressive Distributed Lag (ARDL) bound test approach for the purpose of empirical estimation. Moreover, Non linear ARDL and asymmetric Causality analysis has also been applied for more comprehensive asymmetric estimation. The findings of our study indicated that gold prices and oil prices negatively affect stock market of China in the long run. In terms of implied volatility index of these commodities, study finds negative impact of price volatility of oil but positive impact of the price volatility of gold on the country's stock market in the long run. However, in the short run, only oil price and gold prices have significant effect on the China's stock market. On the basis of our findings, we recommend the investors to make rational decisions in response to the uncertainties in these markets and should consider gold as a safe haven to hedge themselves in times of uncertainty. Policymakers should take appropriate actions and adopt proper mechanisms for dealing with the quick uncertainty flow of information from the oil to the stock market.
International Journal of Energy Economics and Policy, 2024
This paper investigates the safe haven property of Bitcoin and the main precious metals in a state of crisis. This study focuses mainly on two critical periods, namely the COVID-19 health crisis and the Russian-Ukraine conflict. To achieve this objective, we first use the DCC-GARCH model to study the dynamic correlation between the returns of oil and the main precious metals. Then, we use a bivariate specification and a Bayesian specification to estimate the time-varying coefficient vector autoregressive model. The results of this study indicate the existence of similarity between Gold and Bitcoin in hedging capabilities. In fact, both have been weak havens during the COVID-19 health crisis and strong havens during the Russian-Ukrainian war period. On the other hand, the results suggest that ruthenium and iridium yields are uncorrelated or negatively correlated with Brent yields. In this respect, investors are called upon to keep their treasury in the form of iridium and ruthenium during this period of war. Similarly, investors were required to invest in these two assets during the COVID-19 period.
Resources Policy, 2016
This paper investigated volatility persistence and returns spillovers between oil and gold markets using daily historical data from 1986 to 2015 partitioned into periods before the global crisis and after the crisis. The log-returns, absolute and squared log-returns series of these asset prices were used as proxy variables to investigate volatility persistence using the fractional persistence approach. The Constant Conditional Correlation (CCC) modelling framework was applied to investigate the spillover effects between the asset returns. The volatility in the gold market was found to be less than that at the oil market before and after the crisis periods. The returns spillover effect was bidirectional before the crisis period, while it was unidirectional from gold to oil market after the crisis. The fact that there was no returns spillover running from oil to gold after the crisis suggested a measure of optimum allocation weights and hedge ratio. The results obtained are of practical implications for portfolio managers and decision managers in these two ways: gold market should be used as a hedge against oil price inflationary shocks; and the volatility at the oil market can be used to determine the behaviour of gold market.
Eurasian Economic Review, 2021
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).
Commodity dynamism in the COVID-19 crisis: Are gold, oil, and stock commodity prices, symmetrical?
Resources Policy
The current research intends to examine the commodities' dynamism connection with stock prices under the COVID-19 crisis. DCC-GARCH modeling was applied to the data of Asian economies, including China, India, Sri Lanka, Bangladesh, and Pakistan to achieve the study objectives. The study's results indicated a significant connection between gold prices with stock prices and oil prices for all Asian stock markets. The results of the study constructs were symmetrical. In general, the connection grows with the frequency. The lowest frequency months contributed the most to the total relationship, followed by more than 12 months. Overall, gold and oil prices influence the Asian stock markets. These research findings can avoid contagion in times of economic uncertainty. This study also suggested policy implications for better decision-making of key stakeholders. Dynamic coefficient values were about 0.8 of β2 because nations' internal markets were more closely linked. There are also dynamic relationship factors between crude oil and foreign currency markets, where the correlations in India and China have always been around 0.
A note on oil price shocks and the forecastability of gold realized volatility
Applied Economics Letters, 2020
We examine the predictive power of disentangled oil price shocks over gold market volatility via the heterogeneous autoregressive realized volatility (HAR-RV) model. Our in-and out-of-sample tests show that combining the information from both oil supply and demand shocks with the innovations associated with financial market risks improves the forecast accuracy of realized volatility of gold. While financial risk shocks are important on their own, including oil price shocks in the model provides additional forecasting power in out-of-sample tests. Compared to the benchmark HAR-RV model, the extended model with all the three shocks included outperforms, in a statistically significant manner, all other variants of the HAR-RV framework for short-, medium, and long-run forecasting horizons. The findings highlight the predictive power of cross-market information in commodities and suggest that disentangling supply-and demand-related factors associated with price shocks could help improve the accuracy of forecasting models.
Resources Policy
Extant literature establishes co-movements among commodity (metal and oil) prices; whereas oil price/shocks aggregate, as a lone predictor, has relative predictability for most financial assets. We assess the predictability of Baumeister and Hamilton's (2019) decomposed oil shocks (economic activity shocks, oil consumption demand shocks, oil inventory demand shocks, and oil supply shocks) for conditional volatilities of prominently traded precious metals (gold, palladium, platinum, and silver) using GARCH-MIDAS-X framework. The asymmetric effect of decomposed oil shocks on precious metals' volatilities is examined. The DCC-MIDAS framework allows to investigate the conditional correlations and volatility between oil and precious metal prices. Results show that precious metals exhibit hedging potentials against oil demand and supply shocks, with heterogeneity observed in the precious metal-oil shocks nexus. Asymmetry is evident in the responses of metals' volatility to oil shocks. DCC-MIDAS results reveal significant dynamic correlations between oil prices and precious metals (except for platinum). Our results are robust (sensitive) to precious metals (oil shocks) proxies. The findings are insightful for commodity market stakeholders.
Mathematics 2022, 10, 4035., 2022
The fluctuations in oil have strong implications on many financial assets not to mention its relationship with gold prices, exchange rates, stock markets, and investor sentiment. Recent evidence suggests nonlinear contagion among the factors stated above with bivariate or trivariate settings and a throughout investigation of contagion and causality links by taking especially nonlinearity into consideration deserves special importance for the relevant literature. For this purpose, the paper explores the Markov switching generalized autoregressive conditional heteroskedasticity copula (MS-GARCH—copula) and MS-GARCH-copula-causality method and its statistical properties. The methods incorporate regime switching and causality analyses in addition to modeling nonlinearity in conditional volatility. For a sample covering daily observations for 4 January 2000–13 March 2020, the empirical findings revealed that: i. the incorporation of MS type nonlinearity to copula analysis provides important information, ii. the new method helps in the determination of regime-dependent tail dependence among oil, VIX, gold, exchange rates, and BIST stock market returns, in addition to determining the direction of causality in those regimes, iii. important policy implications are derived with the proposed methods given the distinction between high and low volatility regimes leads to different solutions on the direction of causality.