Forecasting the Crude Oil Price with Extreme Values

Abstract

Extreme values are usually given special attention. Using a decomposition-based vector autoregressive (VAR) model, this paper investigates the additional information of extreme values for forecasting the crude oil price. Empirical studies performed on the WTI spot crude oil price over year 1986-2013 are positive: decomposition-based VAR model produces significant both in-sample and out-of-sample forecast. Different evaluation tests are used and the results unanimously report the dominance of decomposition-based VAR over both efficient market model and ARIMA model. These findings are important as they hint that forecasts can be improved if high-low extreme information is properly used. An even more interesting finding is that the predictability of the crude oil price is asymmetric: crude oil price is more predictable in recession than in expansion. This finding is of great significance as it means there is information friction in the oil market especially when the oil price is in recession.