Forecasts of the real price of oil revisited: Do they beat the random walk?

Futures Prices are Useful Predictors of the Spot Price of Crude Oil

“…The authors argue that for real prices, the no-change forecast implied by the random walk model should be based on the real end-of-month price rather than the standard real monthly average price. Ellwanger and Snudden (2021) further document that the performance of the alternative no-change forecast often dominates that of the conventional no-change forecasts, especially at shorter forecast horizons. The results (row "No Change, EoM") show that this is indeed the case for the real price of WTI crude oil.…”

“…While historically, futures-based forecasts have proven to perform quite differently across different commodities (Chinn and Coibion, 2014), there is reason to believe that the insights of our paper apply more broadly. First, as suggested by Ellwanger and Snudden (2021), the gains from using end-of-period prices instead of average should apply to any persistent series, including the prices of other commodities. Second, many futures markets of other assets have experienced a similar process of financialization as the oil futures markets, which could, in principle, have contributed to similar changes in the predictive content of futures in these markets.…”

“…This section compares the effectiveness of the futures-based forecast to that of other model-based and model-free forecasts. Specifically, we consider the autoregressive and vector autoregressive models of Baumeister and Kilian the judgement-based EIA forecasts, and the real end-of-month no-change forecast proposed by Ellwanger and Snudden (2021).…”

“…Finally, Table 7 also displays the accuracy of the alternative no-change forecast recently proposed by Ellwanger and Snudden (2021). The authors argue that for real prices, the no-change forecast implied by the random walk model should be based on the real end-of-month price rather than the standard real monthly average price.…”

“…The use of end-of-period prices is preferable because averaging changes the underlying data process (Rossana and Seater, 1995), which leads to a mechanical loss of information that is particularly relevant for forecasting persistent processes (Wei, 1978;Lütkepohl, 1984). Ellwanger and Snudden (2021) propose a parsimonious solution to the information loss via period-end price sampling (PEPS). They show that real end-ofmonth spot prices can improve the forecast accuracy of no-change forecasts and of model-based forecasts by preserving the informational content of the last available price.…”