The connectedness between crude oil and financial markets: Evidence from implied volatility indices (original) (raw)
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Resources Policy, 2020
This paper investigates the dynamic correlation and risk transmission between the oil market and the U.S. stock market, using the respective implied volatility indices published by the Chicago Board Options Exchange. The results indicate that, first, there is a significant positive time-varying correlation between oil and stock implied volatility returns. Second, during the global financial crisis, the correlation between oil and stock markets increases significantly. Third, there is a significant bidirectional implied volatility spillover between the oil and stock markets. Insights gleaned from the findings in this study could project energy and monetary policy implications. Monetary and/or energy policy changes could impact the predicted linkage mechanism between these two markets, which can be further leveraged to forecast the market's future volatility.
Energy, 2013
a b s t r a c t OVX (Crude oil volatility index), as a measure of oil market uncertainty and new volatility derivatives published by CBOE (Chicago Board Options Exchange) during the 2008 global financial crisis, provides a direct prediction of the market's expectation for future 30-day crude oil price volatility. This paper investigates the short-and long-term cross-market uncertainty transmission implied by OVX and other important volatility indices which are VIX (stock market volatility index), EVZ (euro/dollar exchange rate volatility index) and GVZ (gold price volatility index). The results indicate that there are no strong long-run equilibrium relationships among these volatility indices, which indirectly verify the effectiveness of crossmarket volatility portfolio strategy for risk hedge. Furthermore, OVX is significantly influenced by other ones, which indicates that investors' volatility expectation in the oil market become more sensitive to uncertainty shocks from other markets when the global economic situation is extremely unstable. Finally, impacts of interior and exterior uncertainty shocks on OVX are found to be positive and transient. And the significant short-term uncertainty transmission between oil and other major markets has been confirmed.
Volatility Spillover from Oil to Food and Agricultural Raw Material Markets
Modern Economy, 2011
The upward movement in oil and food prices in the 2000s has attracted interest in the information transmission mechanism between the two markets. This paper investigates the volatility spillover between oil, food consumption item, and agricultural raw material price indexes for the period January 1980 to April 2008. The results of the Cheung-Ng procedure show that variation in oil prices does not Granger cause the variance in food and agricultural raw material prices. Since there is no volatility spillover from oil markets to food and agricultural raw material markets, investors can benefit from risk diversification. However, there is bi-directional spillover between agricultural raw material and food markets.
Spillover and Drivers of Uncertainty among Oil and Commodity Markets
Mathematics
The paper aims to examine the spillover of uncertainty among commodity markets using Diebold–Yilmaz approach based on forecast error variance decomposition. Next, causal impact of global factors as drivers of uncertainty transmission between oil and other commodity markets is analyzed. Our analysis suggests that oil is a net transmitter to other commodity uncertainties, and this transmission significantly increased during the global financial crisis of 2008–2009. The use of linear and nonlinear causality tests indicates that the global factors have a causal effect on the overall connectedness, and especially on the spillovers from oil to other commodity uncertainties. Further segregation of transmissions between oil to individual commodity markets indicates that stock market implied volatility, risk spread, and economic policy uncertainty are the influential drivers of connectedness among commodity markets.
Keywords: Volatility spillover Oil market Stock markets Oil-importing and oil-exporting countries Portfolio and hedging implications Symmetric and asymmetric DCC-GARCH modelsJEL classification: F65 G11 A B S T R A C T This study analyses the volatility spillover between the oil market and the stock market of oil-importing and oil-exporting countries using daily data over the period from January 2010 to December 2016. The study also explores the portfolio and hedging implications based on dynamic conditional correlation (DCC) and corrected DCC (cDCC) GARCH models. For the analysis, we have used symmetric and asymmetric versions of DCC and cDCC models. Specifically, in the symmetric version of DCC and cDCC, the estimations are based on GARCH (1,1), and in the asymmetric version of DCC and cDDC, the estimations are based on GJR-GARCH (1,1), FIGARCH (1,1) and FIEGARCH (1,1) models, and for each case, we have explored the portfolio and hedging implications. Overall, the evidence indicates that oil-importing countries are severely affected by lagged oil price shocks, and there is less evidence of interdependence between stock markets for both oil-importing and oil-exporting countries. Further, we find that the lagged volatility in the oil market and stock market has a statistically significant impact on the current volatility in its respective markets. The results from the asymmetric analysis show that the magnitudes of the negative shocks are higher than those of the positive shocks. The overall results from portfolio optimization reveal that investors in oil-exporting countries should hold more oil assets in the portfolio to hedge the risk.
Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness
Energy Economics, 2020
Building on the increased interest in oil prices and other financial assets, this paper examines the dynamic conditional correlations among their implied volatility indices. We then proceed to the examination of the optimal hedging strategies and optimal portfolio weights for implied volatility portfolios between oil and fourteen asset volatilities, which belong to four different asset classes (stocks, commodities, exchange rates and macroeconomic conditions). The results suggest that the oil price implied volatility index (OVX) is highly correlated with the US and emerging stock market volatility indices, whereas the lowest correlations are observed with the implied volatilities of gold and the Euro/dollar exchange rate. Hedge ratios indicate that VIX is the least useful implied volatility index to hedge against oil implied volatility. Finally, we show that investors can benefit substantially by adjusting their portfolios based on the dynamic weights and hedge ratios obtained from the dynamic conditional correlation models, although a trade-off exists between the level of risk reduction and portfolio profitability.
Testing the volatility spillover between crude oil price and the U.S. stock market returns
Management Science Letters, 2019
The study aims to examine the volatility transmission between the West Texas Intermediate (WTI) crude oil price returns and the U.S. stock market (S&P500 index) returns for the period 2006-2016. In the empirical analyses, univariate GARCH and multivariate GARCH (BEKK-GARCH) models are employed to investigate potential volatility spillover effect of crude oil price returns on the S&P500 index returns or vice versa. The results of GARCH methods reveal that (i) volatility spill-over effect of S&P500 index returns on the crude oil returns is more significant than the volatility spillover effect of crude oil on the S&P500 index returns by using univariate GARCH model; and (ii) there is a one way volatility spillover effect that runs from S&P500 index returns to crude oil returns when we apply multivariate BEKK-GARCH model. These findings have implications for investors and oil-stock portfolio holders for their portfolio decisions in order to manage their risks on their international investments. Further, crude oil investment participants should consider the changes in U.S. stock market index returns in order to predict the expected volatility in the crude oil returns.