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Forecasting the volatility of crude oil futures using intraday data
Authors:Benoît Sévi
Institution:IPAG Business School, France; Aix-Marseille Université (Aix-Marseille School of Economics), CNRS & EHESS, France
Abstract:We use the information in intraday data to forecast the volatility of crude oil at a horizon of 1–66 days using a variety of models relying on the decomposition of realized variance in its positive or negative (semivariances) part and its continuous or discontinuous part (jumps). We show the importance of these decompositions in predictive (in-sample) regressions using a number of specifications. Nevertheless, an important empirical finding comes from an out-of-sample analysis which unambiguously shows the limited interest of considering these components. Overall, our results indicates that a simple autoregressive specification mimicking long memory and using past realized variances as predictors does not perform significantly worse than more sophisticated models which include the various components of realized variance.
Keywords:Volatility forecasting  Crude oil futures  Realized variance  Jumps  Realized semivariance
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