Forecasting aluminum prices with commodity currencies

Abstract

In this paper we show that the exchange rates of some commodity exporter countries have the ability to predict the price of spot and future contracts of aluminum. This is shown with both insample and out-of-sample analyses. The theoretical underpinning of these results relies on the present-value model for exchange rate determination and on the tight connection between commodity prices and the currencies of commodity exporter countries. We show results using traditional statistical metrics of forecast accuracy: Mean Squared Prediction Error and Mean Directional Accuracy. We also show that the first principal component of our sample of exchange rates is a useful way to summarize the predictive information contained in our set of commodity currencies.

A Simple Out-of-Sample Test of Predictability against the Random Walk Benchmark

“…In this section, based on the commodity-currencies literature, we provide an empirical illustration in which we compare the SEP and the EP tests. Following [5], Chen, Rossi and Rogoff (2011) [26]; [7][8][9]; Pincheira, Hardy and Muñoz (2021) [27]; and [6], we evaluate the performance of five exchange rates from major commodity exporters (Australia, Canada, Chile, New Zealand and Norway) when predicting commodity prices. We consider five commodity series: West Texas Intermediate (WTI), heating oil, two-month WTI futures, copper and the London metal exchange index (LMEX).…”

“…Finally, Pincheira and Hardy (2021) [3] explain that part of the problem of beating the driftless random walk relies on its minimum magnitude: The zero forecast is so small that it can outperform a non-zero forecast in terms of Mean Squared Prediction Error despite having zero covariance with the predictand. In addition, driftless random walk is used as a common benchmark in the literature to compare more sophisticated models (see for instance, Meese and Rogoff (1983) [4], Chen, Rogoff and Rossi (2010) [5], [6], Pincheira and Hardy (2018, 2019 [7][8][9], ref. [2] and many others).…”

“A Bias Recognized Is a Bias Sterilized”: The Effects of a Bias in Forecast Evaluation

“…The data generating process (DGP) in Section 5.1 is designed to study the effects of a bias both in-sample and out of sample (e.g., the benchmark model is the DRW). The experiment in Section 5.2 is akin to Section 5.1, but this time we set our parameters to match the empirical specifications of [22][23][24] using the Chilean peso and lead prices. In these two experiments, we consider the MENC-NEW according to Theorem 2.…”

“…In this section, we use the same DGP as Section 5.1, but we set the parameters in our simulation to match the econometric setup of [22][23][24] (PH). Based on the presentvalue model for exchange rate determination (Campbell and Shiller (1987) [37] and Engel and West (2005) [38]), PH show that the Chilean exchange rate has the ability to predict base-metal prices.…”

“…A leading case in which (i) is relevant is the following: If the expected value of the target variable is not zero, and the benchmark is the zero-forecast from a driftless random walk (DRW), then by construction, both models are biased under the null hypothesis, and inference may be severely incorrect when using CM critical values. As the DRW is one of the most important benchmarks in the economic/financial forecasting literature, we think this is an important extension for CM work, and we focus on this leading case (Some empirical papers using the DRW as a benchmark and the ENC-NEW with CM critical values are Hardy (2019, 2021) [22,23], [24,25], to name a few. We emphasize, however, that these papers consider multiple benchmarks as the nested model (not exclusively the DRW).…”