## Abstract

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 [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.

Original language | English (US) |
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Title of host publication | Handbook of Energy Finance |

Subtitle of host publication | Theories, Practices and Simulations |

Publisher | World Scientific Publishing Co. |

Pages | 215-229 |

Number of pages | 15 |

ISBN (Electronic) | 9789813278387 |

ISBN (Print) | 9789813278370 |

DOIs | |

State | Published - Jan 1 2020 |

## Keywords

- ARJI
- Energy commodity prices
- GARCH
- Jump diffusion