This chapter is concernedwith the statistical behavior of energy commodity prices. Aparticularly salient feature ofmany commoditymarkets is the unexpectedly rapid changes - or jumps - that result from the arrival of new information. Such a processwould contradict the viewthat energy commodity prices followa geometric Brownian motion (GBM) process (i.e. log returns are normally distributed). That is, assuming a GBMprocess for the data-generatingmechanismwould 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 ofChan and Maheu , 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.
|Original language||English (US)|
|Title of host publication||Handbook of Energy Finance|
|Subtitle of host publication||Theories, Practices and Simulations|
|Publisher||World Scientific Publishing Co.|
|Number of pages||15|
|State||Published - Jan 1 2020|
Bibliographical notePublisher Copyright:
© 2019 by World Scientific Publishing Co. Pte. Ltd.
- Energy commodity prices
- Jump diffusion