A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting

In existing researches, the investigations of oil price volatility are always performed based on daily data and squared daily return is always taken as the proxy of actual volatility. However, it is widely accepted that the popular realized volatility (RV) based on high frequency data is a more robust measure of actual volatility than squared return. Due to this motivation, we investigate dynamics of daily volatility of Shanghai fuel oil futures prices employing 5-minute high frequency data. First, using a nonparametric method, we find that RV displays strong long-range dependence and recent financial crisis can cause a lower degree of long-range dependence. Second, we model daily volatility using RV models and GARCH-class models. Our results indicate that RV models for intraday data overwhelmingly outperform GARCH-class models for daily data in forecasting fuel oil price volatility, regardless the proxy of actual volatility. Finally, we investigate the major source of such volatile prices and found that trader activity has major contribution to fierce variations of fuel oil prices.

[1]  P. Hansen.  A Test for Superior Predictive Ability , 2005 .

[2]  James D. Hamilton.  What is an Oil Shock? , 2000 .

[3]  H. Mohammadi,et al.  International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models , 2010 .

[4]  L. Kilian,et al.  The Impact of Oil Price Shocks on the U.S. Stock Market , 2007 .

[5]  Yu Wei,et al.  Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective , 2010 .

[6]  Rania Jammazi,et al.  The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach , 2009 .

[7]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[8]  C. Granger,et al.  A long memory property of stock market returns and a new model , 1993 .

[9]  R. Engle,et al.  A Permanent and Transitory Component Model of Stock Return Volatility , 1993 .

[10]  Fulvio Corsi,et al.  A Simple Approximate Long-Memory Model of Realized Volatility , 2008 .

[11]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[12]  N. Shephard,et al.  Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics , 2001 .

[13]  T. Bollerslev,et al.  ANSWERING THE SKEPTICS: YES, STANDARD VOLATILITY MODELS DO PROVIDE ACCURATE FORECASTS* , 1998 .

[14]  L. Kilian.  Not All Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market , 2006 .

[15]  F. Diebold,et al.  The distribution of realized stock return volatility , 2001 .

[16]  Yiu Kuen Tse,et al.  The conditional heteroscedasticity of the yen-dollar exchange rate , 1998 .

[17]  Joseph P. Romano,et al.  The stationary bootstrap , 1994 .

[18]  Yudong Wang,et al.  What can we learn from the history of gasoline crack spreads?: Long memory, structural breaks and modeling implications , 2012 .

[19]  N. Nomikos,et al.  Forecasting petroleum futures markets volatility: The role of regimes and market conditions , 2011 .

[20]  James D. Hamilton.  Oil and the Macroeconomy since World War II , 1983, Journal of Political Economy.

[21]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[22]  Yu Wei,et al.  Forecasting crude oil market volatility: Further evidence using GARCH-class models , 2010 .

[23]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[24]  Yudong Wang,et al.  Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis , 2010 .

[25]  H. Iemoto.  Modelling the persistence of conditional variances , 1986 .

[26]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[27]  R. Baillie,et al.  Fractionally integrated generalized autoregressive conditional heteroskedasticity , 1996 .

[28]  Jose Alvarez-Ramirez,et al.  Short-term predictability of crude oil markets: A detrended fluctuation analysis approach , 2008 .

[29]  H E Stanley,et al.  Analysis of clusters formed by the moving average of a long-range correlated time series. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Wei-Xing Zhou,et al.  Detrending moving average algorithm for multifractals. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[32]  Daniel B. Nelson.  CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[33]  Seong-Min Yoon,et al.  Forecasting volatility of crude oil markets , 2009 .

[34]  Chin Wen Cheong,et al.  Modeling and forecasting crude oil markets using ARCH-type models , 2009 .

[35]  B. M. Tabak,et al.  Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility☆ , 2007 .

[36]  Perry Sadorsky,et al.  Modeling and forecasting petroleum futures volatility , 2006 .

[37]  James Davidson,et al.  Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model , 2004 .

[38]  Jui-Cheng Hung,et al.  Estimation of value-at-risk for energy commodities via fat-tailed GARCH models , 2008 .

[39]  Minh-Vuong Vo,et al.  Regime-switching stochastic volatility: Evidence from the crude oil market , 2009 .

[40]  J. Alvarez,et al.  Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern , 2010 .

[41]  Yu Wei,et al.  Can GARCH-class models capture long memory in WTI crude oil markets? , 2011 .

[42]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .