Forecasting Commodity Prices: Looking for a Benchmark

Abstract

The random walk, no-change forecast is a customary benchmark in the literature on forecasting commodity prices. We challenge this custom by examining whether alternative models are more suited for this purpose. Based on a literature review and the results of two out-of-sample forecasting experiments, we draw two conclusions. First, in forecasting nominal commodity prices at shorter horizons, the random walk benchmark should be supplemented by futures-based forecasts. Second, in forecasting real commodity prices, the random walk benchmark should be supplemented, if not substituted, by forecasts from the local projection models. In both cases, the alternative benchmarks deliver forecasts of comparable and, in many cases, of superior accuracy.

A Statistical Model for Identifying Collusive Bidding Based on Historical Quotations

“…Among them, the arithmetic average price of each bidder is taken as the price benchmark, and the business score of each bidder is calculated according to the difference between the price and the benchmark price [14]. This method can avoid the risk of winning bids at low prices and defective products, and is the most commonly used business score calculation method for large-scale or important material procurement [15]. However, the large interest drives some bidders to form a community to raise the benchmark price and win the bid at a high price for illegal profit, thus increasing the risk of collusion bidding.…”

A Statistical Model for Identifying Collusive Bidding Based on Historical Quotations

“…The essence of collusive bidding is that bidders quote higher or lower than the reasonable price of the project [8,9], aiming to gain an advantage over other competitors and achieve higher profits [10,11]. As far as the tender is concerned, the collusive bidding will damage the interests of the tender [12]. Through the formal bidding process, the tender could have obtained high-quality services or products at a lower purchase price, but the collusive bidding behavior makes the high-quality bidders unable to win the bid.…”

A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling

“…So far, the existing literature has mostly focused on the relations between NG and other commodities or securities (see, for instance, [38][39][40][41][42][43][44][45][46][47]), as well as on modeling price volatility (e.g., [48][49][50][51][52][53][54][55]), demand and supply (e.g., [56][57][58][59][60][61][62][63][64]), spot prices (e.g., [65][66][67][68][69][70][71][72][73][74][75][76]) or futures prices of individual contracts (e.g., [77][78][79]). Relatively less attention has been paid to NG futures prices term structure modeling and forecasting and only a few studies have partly tackled the issues we are dealing with.…”