Volatility Of Crude Oil Futures Research Articles

The determinants of volatility on the American crude oil futures market

AbstractThis article focuses on the volatility of crude oil futures prices on the New York Mercantile Exchange. It aims at examining whether this market creates excess volatility, which would not be observed in the absence of such a market. In order to reach this objective, price fluctuations are separated into two components: an information component that reflects a rational assessment of the information arrival, and an error component that represents the noise introduced by the trading process. We show that a significant part of the volatility recorded during exchange trading hours is caused by mispricing errors. In particular, this phenomenon affects the nearest futures contract.

Basis variations and regime shifts in the oil futures market

The conditional volatility of crude oil futures returns is modelled as a regime switching process. The model features transition probabilities that are functions of the basis. Consistent with the theory of storage, in volatile periods, an increase in backwardation is associated with an increase in the likellihood of switching to or remaining in the high-volatility state. Conditional on regimes, GARCH persistence is significantly reduced. Out-of-sample tests show that incorporating regime shifts improves the accuracy of short-term volatility forecasts.

A Markov switching model of the conditional volatility of crude oil futures prices

This paper examines the temporal behaviour of volatility of daily returns on crude oil futures using a generalised regime switching model that allows for abrupt changes in mean and variance, GARCH dynamics, basis-driven time-varying transition probabilities and conditional leptokurtosis. This flexible model enables us to capture many complex features of conditional volatility within a relatively parsimonious set-up. We show that regime shifts are clearly present in the data and dominate GARCH effects. Within the high volatility state, a negative basis is more likely to increase regime persistence than a positive basis, a finding which is consistent with previous empirical research on the theory of storage, e.g. Fama and French (1988a,b) and Ng and Pirrong (1994). The volatility regimes identified by our model correlate well with major events affecting supply and demand for oil. Out-of-sample tests indicate that the regime switching model performs noticeably better than non-switching models regardless of evaluation criteria. We conclude that regime switching models provide a useful framework for the financial historian interested in studying factors behind the evolution of volatility and to oil futures traders interested short-term volatility forecasts.