A Markov switching model of the conditional volatility of crude oil futures prices

Fong, Wai Mun; See, Kim Hock

doi:10.1016/s0140-9883(01)00087-1

Cited by 170 publications

(67 citation statements)

References 26 publications

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“…Wilson et al (1996) found that there were three major shifts in the volatility of world oil prices during the 1984-1992 period, attributed to the nature and magnitude of the exogenous shocks -OPEC policy changes, Iran-Iraq conflict, Gulf War and extreme weather conditions. Fong and See (2002) found that the volatility of oil prices can vary with market conditions. Sadorsky (2006) in examining the forecasting performance of GARCH and Threshold GARCH (TGARCH) type models in predicting volatility of daily oil prices concludes that no one model is the best predictor.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of the Forward Curve and Volatility of Energy Futures Prices

Alizadeh-Masoodian

Talley

2009

SSRN Journal

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The shapes of forward curves of energy commodities are believed to contain information on the volatility of futures prices for these commodities. The slope of the forward curve not only reflects temporal supply and demand conditions, but also the relationship between current and expected market conditions. However, no empirical investigation exists in the literature on whether utilising information on the slopes of the forward curves of energy commodities can improve one"s ability to capture the dynamics of the volatility of the futures prices of these commodities. The aim of this study is to undertake such an investigation. Daily energy futures prices traded on the New York Mercantile Exchange (NYMEX) over the period January 1997 to December 2006 are used to estimate the parameters of an augmented transition EGARCH model that allows for changes in the model"s parameters based on the forward curve. The forecasting performance of the model is compared to that of other models in predicting the volatility of energy futures prices over the period January 2007 to December 2008. The results provide strong support in favour of a convex relationship between the volatility of energy futures prices and the forward curve.

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Section: Introductionmentioning

confidence: 99%

Dynamics of the Forward Curve and Volatility of Energy Futures Prices

Alizadeh-Masoodian

Talley

2009

SSRN Journal

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“…They justify the use of such models and the regimes appear consistent with popular known exchange rate regimes in the world. Fong and See (2002) also demonstrate the validity of using a Markov regime switching model for volatilities in oil futures price series. Raymond and Rich (1997) use regime switching to study the role of oil prices in accounting for shifts in the mean of U.S. GDP and to predict the transition between low and high growth states.…”

Section: Methodsmentioning

confidence: 74%

Dividends, Momentum, and Macroeconomic Variables as Determinants of the US Equity Premium Across Economic Regimes

Malliaris

Bhar²

2011

Review of Behavioral Finance

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The equity premium of the S&P 500 Index is explained in this paper by several variables that can be grouped into fundamental, behavioral and macroeconomic factors. We hypothesize that the statistical significance of these variables changes across economic regimes. The three regimes we consider are the low, medium and high volatility regimes in contrast to previous studies that do not differentiate across economic regimes. Using the three-state Markov switching regime econometric methodology we confirm that the statistical significance of the independent variables representing fundamentals, macroeconomic conditions and a behavioral variable changes across economic regimes. Our findings offer an improved understanding of what moves the equity premium across economic regimes than what we can learn from single-equation estimation. Our results also confirm the significance of momentum as a behavioral variable across all economic regimes.Key Words: Excess stock returns, equity premium, dividends, macroeconomic variables, momentum, Markov regimes. JEL Classification: C22, E44, G12Current Revised Version: February10, 2011. The authors are very grateful to Professor Ravi Jagannathan for a detailed review of an earlier version of this paper that contributed to a substantial improvement of our hypotheses formulation and econometric methodology. We are also thankful to Professor John Doukas for insightful clarifications on the selection of the appropriate behavioral variable. The presentation of this paper in various seminars in the U.S. and Australia has encouraged the authors enrich both the methodology and exposition of the paper. A detailed and constructive referee report has helped the authors to refocus the scope of this research and to enrich the presentation. Special thanks are offered to the anonymous referee and the editor of the Review of Behavioral Finance. Resubmitted to the REVIEW OF BEHAVIORAL FINANCE. This is the latest master copy dated March 25, 2011.This article is Emerald Publishing Limited and permission has been granted for this version to appear in Loyola eCommons. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Publishing Limited. 2 Dividends, Momentum and Macroeconomic Variables as Determinants of the U.S. Equity Premium Across Economic Regimes AbstractThe equity premium of the S&P 500 Index is explained in this paper by several variables that can be grouped into fundamental, behavioral and macroeconomic factors. We hypothesize that the statistical significance of these variables changes across economic regimes. The three regimes we consider are the low, medium and high volatility regimes in contrast to previous studies that do not differentiate across economic regimes. Using the three-state Markov switching regime econometric methodology we confirm that the statistical significance of the independent variables representing fundamentals, macroeconomic conditions and a behavioral variable chang...

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“…For example, using an iterative cumulative sum-of-squares approach, Wilson et al (1996) document sudden changes in the unconditional variance in daily returns on one-month through six-month oil futures and relate these changes to exogenous shocks, such as unusual weather, political conflicts and changes in OPEC oil policies. Fong and See (2002) conclude that regime switching models provide a useful framework for studying factors behind the evolution of volatility and short-term volatility forecasts.…”

Section: Introductionmentioning

confidence: 93%

Market Efficiency of Oil Spot and Futures: A Mean-Variance and Stochastic Dominance Approach

McAleer

Wong

2010

SSRN Journal

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This paper examines the market efficiency of oil spot and futures prices by using both mean-variance (MV) and stochastic dominance (SD) approaches. Based on the West Texas Intermediate crude oil data for the sample period 1989-2008, we find no evidence of any MV and SD relationships between oil spot and futures indices. This infers that there is no arbitrage opportunity between these two markets, spot and futures do not dominate one another, investors are indifferent to investing in spot or futures, and the spot and futures oil markets are efficient and rational. The empirical findings are robust to each sub-period before and after the crises for different crises, and also to portfolio diversification.

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A Markov switching model of the conditional volatility of crude oil futures prices

Cited by 170 publications

References 26 publications

Dynamics of the Forward Curve and Volatility of Energy Futures Prices

Dynamics of the Forward Curve and Volatility of Energy Futures Prices

Dividends, Momentum, and Macroeconomic Variables as Determinants of the US Equity Premium Across Economic Regimes

Market Efficiency of Oil Spot and Futures: A Mean-Variance and Stochastic Dominance Approach

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