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Jumps in Energy Commodity Markets

This chapter is concerned with the statistical behavior of energy commodity prices. A particularly salient feature of many commodity markets is the unexpectedly rapid changes — or jumps — that result from the arrival of new information. Such a process would contradict the view that energy commodity prices follow a geometric Brownian motion (GBM) process (i.e. log returns are normally distributed). That is, assuming a GBM process for the data-generating mechanism would be insufficient to capture the true dynamics of energy commodity markets. The discontinuous arrival of information necessitates a stochastic process that incorporates this feature, and as such, Jump processes have become an important tool in the analysis of energy markets. While such models allow for multiple jumps in a period, the jump intensity is assumed to be constant over time — a questionable assumption given the dynamics of such energy markets. The autoregressive conditional jump intensity (ARJI) model of Chan and Maheu [2002],which allows for a time-varying jump intensity, is applied to important energy commodity markets. The results indicate the importance of incorporating time-varying jump intensities in energy markets.