Long memory in commodity futures volatility: A wavelet perspective

Hyun Joung Jin

45 citations0/0 files storedJournal of Futures Markets

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Financial Risk and Volatility Modeling Market Dynamics and Volatility Complex Systems and Time Series Analysis

Abstract

Abstract The authors reexamine the volatility of agricultural commodity futures for evidence of fractional integration, providing new empirical results and extending the extant literature in important dimensions. First, they utilize two relatively new estimators based on wavelets, which are generally superior to, for example, the popular estimator by J. Geweke and S. Porter‐Hudak (GPH; 1983) and exact maximum likelihood estimators (MLEs) on the basis of mean squared error (MSE). Second, they provide simulations to contrast their point estimates with those obtained by a fractionally integrated GARCH (generalized autoregressive conditional heteroscedasticity) model. Third, they conduct a wavelet coef.cient decomposition of futures volatility. They .nd that futures volatilities display the self‐similarity property consistent with long memory and that futures volatilities exhibit persistent long memory with .nite unconditional variance. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:411–437, 2007

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References

1992Testing the null hypothesis of stationarity against the alternative of a unit root 1980AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING 1983THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS 1981Fractional Differencing 1996Fractionally integrated generalized autoregressive conditional heteroskedasticity 2015Proof that Properly Anticipated Prices Fluctuate Randomly 1993Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures 1996Modeling and pricing long memory in stock market volatility

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