In many commodity markets, the arrival of new information leads to unexpectedly rapid changes-or jumps-in commodity prices. Such arrivals suggest the assumption that log-return relatives are normally distributed may not hold. Combined with time-varying volatility, the possibility of jumps offers a potential explanation for fat tails in oil price returns. This article investigates the potential presence of jumps and time-varying volatility in the spot price of crude oil and in futures prices. The investigation is carried out over three data frequencies (Monthly, Weekly, Daily), which allows for an investigation of temporal properties. Employing likelihood ratio tests to compare among four stochastic data-generating processes, we find that that allowing for both jumps and timevarying volatility improves the model's ability to explain spot prices at each level of temporal aggregation; this combination also provides a statistically compelling improvement in model fit for futures prices at the Daily and Weekly level. At the monthly level, allowing for jumps does not provide a statistically significant increase in model fit after incorporating time-varying volatility into the model.
- Crude Oil prices
- Jump diffusion