星期四, 8月 22, 2013
Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks
Given that crude oil price is one of the key variables in forecasting macroeconomic aggregates, including real GDP and inflation, the forecasting of crude oil prices has become the focus of many economists and decision makers. The recent literature has already explored the forecasting ability of a number of predictors for the price of oil, including the oil futures price, oil inventories, the price of crack spread futures, the price of industrial raw materials (other than crude oil), the dollar exchange rate of major broad-based commodity exporters, U.S. and global macroeconomic aggregates, and expert survey forecasts.
In my recent paper, Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks (forthcoming in Economic Inquiry ), I propose a new leading indicator, namely, oil-sensitive stock price indices, to forecast the price of crude oil instead. There are several reasons for considering stock prices as predictors of spot crude oil prices. First, as global stock markets have become more integrated, stock prices should be a reliable leading indicator of boom and bust in the economy, respectively resulting in the increasing and decreasing demand for oil. We thus expect stock prices to predict oil prices well. Furthermore, Kilian and Vega(2011) show that unlike stock prices, the price of WTI crude oil does not respond significantly to macroeconomic news in the U.S. within either the day or the month. Hence, in response to the same macroeconomic news, we expect a lead–lag relationship between stock prices and oil prices. In particular, we consider oil-sensitive stock price indices, which may be more informative in tracing future changes in crude oil prices. Finally, stock prices appear superior as a leading indicator because timely stock price data are readily available for forecasting purposes. As stock prices are not subject to revision, the proposed predictor can be used in real-time data forecasts and even extended to consider price data at higher frequencies.
I uses monthly data from 1984:M10 to 2012:M8 to show that oil-sensitive stock price indices, particularly those in the energy sector, have strong power in predicting nominal and real crude oil prices at short horizons (one-month-ahead predictions), using both in- and out-of-sample tests. In particular, the forecasts based on oil-sensitive stock price indices are able to outperform significantly the no-change forecasts. For example, using the NYSE Arca (AMEX) oil index as a predictor, the one-month-ahead forecasts for nominal crude oil prices reduce the mean squared prediction error by between 22% (for the West Texas Intermediate oil price) and 28% (for the Dubai oil price). Moreover, we find that the directional forecast based the AMEX oil index is significantly better than a 50:50 coin toss. The novelty of this analysis is that it proposes a new and valuable predictor that both reflects timely market information and is readily available for forecasting the spot oil price.