Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks

Feng Ma, Yin Liao, Yaojie Zhang, Yang Cao

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Oil markets are subject to extreme shocks (e.g. Iraq’s invasion of Kuwait), causing the oil market price exhibits extreme movements, called jumps (or spikes). These jumps pose challenges on oil market volatility forecasting using conventional volatility dynamic models (e.g. GARCH model) This paper characterizes dynamics of jumps in oil market price using high frequency data from three perspectives: the probability (or intensity) of jump occurrence, the sign (e.g. positive or negative) of jumps, and the concurrence with stock market jumps. And then, the paper exploits predictive ability of these jump-related information for oil market volatility forecasting under the mixed data sampling (MIDAS) modeling framework. Our empirical results show that augmenting standard MIDAS model using the three jump-related information significantly improves the accuracy of oil market volatility forecasting. The jump intensity and negative jump size are particularly useful for predicting future oil volatility. These results are widely consistent across a variety of robustness tests. This work provides new insights on how to forecast oil market volatility in the presence of extreme shocks.
Original languageEnglish
Pages (from-to)40-55
Number of pages16
JournalJournal of Empirical Finance
Volume52
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes

Keywords

  • Oil market
  • Volatility forecasting
  • Jump intensity
  • Signed jumps
  • Cojumps

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