Time-varying Predictability in Crude Oil Markets: The Case of GCC Countries

This paper uses a time-varying parameter model with generalized autoregressive conditional heteros-cedasticity effects to examine the dynamic behavior of crude-oil prices for the period 1997-2008. Using data from four countries of the Gulf Cooperation Council, we find evidence of short-term pre-dictability in oil-price changes over time, except for several short sub-periods. However, the hypothe-sis of convergence towards weak-form informational efficiency is rejected for all markets. In addition, we explore the possibility of structural breaks in the time-paths of the estimated predictability indices and detect only one breakpoint, for the oil markets in Qatar and United Arab Emirates. Our empirical results therefore call for new empirical research to further gauge the predictability characteristics and the determinants of oil-price changes.

On the Information Content of Oil Future Prices

This paper deals with the efficiency of the Brent Crude oil future contracts and tests whether futures can be used to predict realized oil spot prices. Evidence suggests that future prices up to three-months contracts on Brent Crude are unbiased predictors of future spot prices but the explanation power is not high (around 20%). Furthermore, using cointegration techniques the unbiasedness hypothesis for future prices as predictors of realized spot prices could not be rejected. When the sample is divided into sub-periods, the absence of bias in futures prices is rejected.

Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries

The paper investigates the time-varying correlation between stock market prices and oil prices for oil-importing and oil-exporting countries. A DCC-GARCH-GJR approach is employed to test the above hypothesis based on data from six countries; Oil-exporting: Canada, Mexico, Brazil and Oil-importing: USA, Germany, Netherlands. The contemporaneous correlation results show that i) although time-varying correlation does not differ for oil-importing and oil-exporting economies, ii) the correlation increases positively (negatively) in respond to important aggregate demand-side (precautionary demand) oil price shocks, which are caused due to global business cycle’s fluctuations or world turmoil (i.e. wars). Supply-side oil price shocks do not influence the relationship of the two markets. The lagged correlation results show that oil prices exercise a negative effect in all stock markets, regardless the origin of the oil price shock. The only exception is the 2008 global financial crisis where the lagged oil prices exhibit a positive correlation with stock markets. Finally, we conclude that in periods of significant economic turmoil the oil market is not a safe haven for offering protection against stock market losses

The Volatility Spillover Effects and Optimal Hedging Strategy in the Corn Market

This article examines the volatility spillovers from energy market to corn market. Using a volatility spillover model from the finance literature, we found significant spillovers from energy market to corn cash and futures markets, and the spillover effects are time-varying. The business cycle proxied by crude oil prices is shown to affect the magnitude of spillover effects over time. Based on the strong informational linkage between energy market and corn market, a cross hedge strategy is proposed and its performance studied. The simulation outcomes show that compared to alternative strategies of no hedge, constant hedge, and GARCH hedge, the cross hedge does not yield superior risk-reduction performance.Volatility Spillover, GARCH, Optimal Hedge Ratio, Energy Price, Corn Price, Risk and Uncertainty,

Modelling Dynamic Conditional Correlations in WTI Oil Forward and Futures Returns

This paper estimates the dynamic conditional correlations in the returns on WTI oil one-month forward prices, and one-, three-, six-, and twelve-month futures prices, using recently developed multivariate conditional volatility models. The dynamic correlations enable a determination of whether the forward and various futures returns are substitutes or complements, which are crucial for deciding whether or not to hedge against unforeseen circumstances. The models are estimated using daily data on WTI oil forward and futures prices, and their associated returns, from 3 January 1985 to 16 January 2004. At the univariate level, the estimates are statistically significant, with the occasional asymmetric effect in which negative shocks have a greater impact on volatility than positive shocks. In all cases, both the short- and long-run persistence of shocks are statistically significant. Among the five returns, there are ten conditional correlations, with the highest estimate of constant conditional correlation being 0.975 between the volatilities of the three-month and six-month futures returns, and the lowest being 0.656 between the volatilities of the forward and twelve-month futures returns. The dynamic conditional correlations can vary dramatically, being negative in four of ten cases and being close to zero in another five cases. Only in the case of the dynamic volatilities of the three-month and six-month futures returns is the range of variation relatively narrow, namely (0.832, 0.996). Thus, in general, the dynamic volatilities in the returns in the WTI oil forward and future prices can be either independent or interdependent over time.Constant conditional correlations, Dynamic conditional correlations, Multivariate GARCH models, Forward prices and returns, Futures prices and returns, WTI oil prices

Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models

This paper investigates whether structural breaks and long memory are relevant features in modeling and forecasting the conditional volatility of oil spot and futures prices using three GARCH-type models, i.e., linear GARCH, GARCH with structural breaks and FIGARCH. By relying on a modified version of Inclan and Tiao (1994)'s iterated cumulative sum of squares (ICSS) algorithm, our results can be summarized as follows. First, we provide evidence of parameter instability in five out of twelve GARCH-based conditional volatility processes for energy prices. Second, long memory is effectively present in all the series considered and a FIGARCH model seems to better fit the data, but the degree of volatility persistence diminishes significantly after adjusting for structural breaks. Finally, the out-of-sample analysis shows that forecasting models accommodating for structural break characteristics of the data often outperform the commonly used short-memory linear volatility models. It is however worth noting that the long memory evidence found in the in-sample period is not strongly supported by the out-of-sample forecasting exercise.

Gasoline and crude oil prices: why the asymmetry?

Many consumers complain that gasoline and crude oil prices have an asymmetric relationship in which gasoline prices raise more quickly when crude oil prices are rising than they fall when crude oil prices are falling. Many also regard the asymmetry they observe as evidence of market power in the petroleum industry. Most previous research provides econometric evidence of the asymmetry, confirming at least part of what consumers suspect. In this article Stephen Brown and Mine Yucel extend the inquiry by examining the market conditions underlying the asymmetric relationship between gasoline and crude oil prices. They find the observed asymmetry is unlikely to be the result of monopoly power. The remaining explanations for the asymmetry suggest that policies to prevent an asymmetric relationship between gasoline and crude oil prices are likely to reduce economic efficiency.

The Impact of Oil Price Fluctuations on Stock Markets in Developed and Emerging Economies

This study examines the response of stock markets to oil price volatilities in Japan, Singapore, Korea and Malaysia by applying the generalized impulse response and variance decomposition analyses to the monthly data spanning 1986:01 – 2011:02. The results suggest that the reaction of stock markets to oil price shocks varies significantly across markets. Specifically, the stock market responds positively in Japan while negatively in Malaysia; the signal in Singapore and South Korea is unclear. We find that the stock market inefficiency, among others, appeared to have slowed the responses of the stock market to aggregate shocks such as oil price surges.oil price fluctuation, stock return, exchange rate, emerging market, VAR model