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.