Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets
Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover and asymmetric effects across and within the four markets, using three multivariate GARCH models, namely the constant conditional correlation (CCC), vector ARMA-GARCH (VARMA-GARCH) and vector ARMA-asymmetric GARCH (VARMA-AGARCH) models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecast conditional correlations between pairs of crude oil returns have both positive and negative trends. Moreover, the optimal hedge ratios and optimal portfolio weights of crude oil across different assets and market portfolios are evaluated in order to provide important policy implications for risk management in crude oil markets.
|Keywords||conditional correlation, crude oil prices, forward returns, futures returns, multivariate GARCH, spot returns, volatility spillovers|
|JEL||C22, Time-Series Models; Dynamic Quantile Regressions (jel), C32, Time-Series Models; Dynamic Quantile Regressions (jel), G17, Financial Forecasting (jel), G32, Financing Policy; Capital and Ownership Structure (jel)|
|Publisher||Erasmus School of Economics (ESE)|
Chang, C, McAleer, M.J, & Tansuchat, R. (2010). Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets (No. EI 2010-14). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–27). Erasmus School of Economics (ESE). Retrieved from http://hdl.handle.net/1765/18329