Random Walk Benchmark Research Articles

Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark

Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark

Are Global Factors Useful for Forecasting the Exchange Rate?

Abstract This paper applies a common factors analysis framework to an exchange rate data panel, in order to better understand the forces behind exchange rate dynamics and provide a set of variables for exchange rate forecasting. Results demonstrate the model’s ability to extract key information to under-stand the driving forces of exchange rate movements using the exchange rate Brazilian Real to U.S. dollar; in addition, the model also shows statistically significant improvement in terms of forecasting performance in relation to the random walk benchmark as well as to traditional macroeconomic models found in economics literature. JEL classification numbers: F31, F37, F47. Keywords: Exchange rate, Common factors, Forecasting.

A credit-based theory of the currency risk premium

This paper uncovers a novel component for exchange rate predictability based on the price difference between sovereign credit default swaps denominated in different currencies. This new forecasting variable – the credit-implied risk premium – captures the expected currency depreciation conditional on a severe but rare credit event. Using data for 16 Eurozone countries, we find that the credit-implied risk premium positively forecasts the dollar-euro exchange rate return at various horizons. Moreover, a currency strategy that exploits the informative content of our predictor generates substantial out-of-sample economic value against the naïve random walk benchmark.

Asymmetry and interdependence when evaluating U.S. Energy Information Administration forecasts

We evaluate US Energy Information Administration (EIA) forecasts of the world petroleum market, emphasising the importance of taking a multivariate perspective, considering asymmetric loss and allowing for time-variation. Forecasts for total demand, total supply, total stock withdrawals and the oil prices are biased, with biases that change over time and differ across variables. A loss function that takes into account asymmetry and interdependence can rationalise these biases. The implied asymmetric loss gives less weight to under-prediction of both demand and supply, while for oil prices, we document significant regime changes in the implied loss due to asymmetry. The EIA forecasts dominate a simple random walk benchmark when evaluated using symmetric and independent loss in the form of MSE statistical criteria. Yet, when allowing for asymmetry and interdependence that rationalise the EIA forecasts, the performance of the EIA forecasts worsens and is comparable to the random walk benchmark.

Exports and imports in Turkey: A regime‐switching approach

AbstractIn this study, we apply Markov regime‐switching models to various foreign trade variables with the aim of characterising the behaviour of the growth of exports and imports in Turkey. The results indicate that the forecasting performance of the proposed Markov regime‐switching models is much better than that of the random walk benchmark except for the growth of import values. However, for the growth of export volumes, the proposed Markov regime‐switching model always outperforms the random walk benchmark. Furthermore, we investigate whether the growth regimes of exports and imports can be associated with the growth regimes of world trade volumes, world trade prices and industrial production by introducing a probability score and comparing the regime probabilities. The results indicate that world trade prices are more closely related to import values than they are to export values except for low growth regime of export values. In fact, low growth regime of export values is strongly associated with low growth regime of world trade prices. On the contrary, normal growth regime of export volumes is closely related to high growth regime of world trade volumes. Moreover, world trade volumes and industrial production are more closely related to import values and import volumes than they are to export values in all regimes. Actually, low growth regimes of import values and import volumes are strongly associated with low growth regime of industrial production.

Do Divisia monetary aggregates help forecast exchange rates in a negative interest rate environment?

This paper contributes to the literature as the first work of its kind to examine the role and importance of Divisia monetary aggregates and concomitant User Cost Price indices as superior monetary policy forecasting tools in a negative interest rate environment. We compare the performance of Divisia monetary aggregates with traditional simple-sum aggregates in several theoretical models and in a Bayesian VAR to forecast the exchange rates between the euro, the dollar and yuan at various horizons using quarterly data. We evaluate their performance against that of a random walk using two criteria: Root Mean Square Error ratios and the Clark-West statistic. We find that, under a free-floating exchange regime, superior Divisia monetary aggregates outperform their simple sum counterparts and the benchmark random walk in a negative interest rate environment, consistently.

Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle

This paper tests the random walk hypothesis in the cryptocurrency market. Based on the well-known Meese–Rogoff puzzle, we evaluate whether cryptocurrency returns are predictable or not. For this purpose, we conduct in-sample and out-of-sample analyses to examine the forecasting power of our model built with autoregressive components and lagged returns of BITCOIN, compared with the random walk benchmark. To this end, we considered the 13 major cryptocurrencies between 2018 and 2022. Our results indicate that our models significantly outperform the random walk benchmark. In particular, cryptocurrencies tend to be far more persistent than regular exchange rates, and BITCOIN (BTC) seems to improve the predictive accuracy of our models for some cryptocurrencies. Furthermore, while the predictive performance is time varying, we find predictive ability in different regimes before and during the pandemic crisis. We think that these results are helpful to policymakers and investors because they open a new perspective on cryptocurrency investing strategies and regulations to improve financial stability.

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

We show that a straightforward modification of a trading-based test for predictability displays interesting advantages over the Excess Profitability (EP) test proposed by Anatolyev and Gerco when testing the Driftless Random Walk Hypothesis. Our statistic is called the Straightforward Excess Profitability (SEP) test, and it avoids the calculation of a term that under the null of no predictability should be zero but in practice may be sizable. In addition, our test does not require the strong assumption of independence used to derive the EP test. We claim that dependence is the rule and not the exception. We show via Monte Carlo simulations that the SEP test outperforms the EP test in terms of size and power. Finally, we illustrate the use of our test in an empirical application within the context of the commodity-currencies literature.

Returns, volatility and the cryptocurrency bubble of 2017–18

Research on cryptocurrencies has focused on price and volatility formation in isolation, however knowledge about their interdependence is important for risk management and asset allocation. We investigate the existence and nature of such a relationship in four commonly traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple, during the cryptocurrency bubble of 2017–18. Using a generalized asset pricing model, we find evidence of a risk premium effect in Litecoin and Ripple during the boom of 2017, and that adverse news effects were an important driver of the cryptocurrency crash of 2018 in all four cryptocurrencies. In an out-of-sample forecasting exercise, we find that allowing for stochastic volatility and a heavy tailed distribution provides more accurate return and volatility forecasts compared to a random walk benchmark. This suggests that cryptocurrency markets were not weak-form efficient during this period.

Common factors and the dynamics of industrial metal prices. A forecasting perspective

This study aims to analyze the suitability of factor models in describing the dynamics of real prices for four main non-ferrous industrial metals: aluminium, copper, nickel and zinc. For that purpose, using an extensive dataset of monthly time series covering the years 1980–2019, we extract four different common factors explaining commodity prices, exchange rates, financial and macroeconomic indicators. Next, we examine these factors as potential predictors of the movements of four metal prices with the use of two model classes: direct forecasts (DF) and factor augmented vector autoregressions (VAR). We show that for three out of four metals (aluminium, nickel and zinc) VAR models provide relatively good point and density forecasts, outperforming the random walk benchmark as well as DF models.

Forecasting Commodity Prices: Looking for a Benchmark

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.

Gasoline price predictability: insights from consumer vehicle‐buying assessments

AbstractThis study employs the random walk forecasts of US retail gasoline prices for 2001–2020 in order to investigate the potential usefulness of consumer vehicle‐buying assessments for predicting gasoline prices. We show that the random walk forecasts are free of systematic bias and imply symmetric loss, but they do not fully contain the information on consumer vehicle‐buying assessments. For instance, the random walk forecast errors fail to be orthogonal to the difference of opinion of consumers about whether the next 12 months or so will be a good time or a bad time to buy a new vehicle, and to the difference of opinion of consumers about vehicle prices (but not interest rates). In addition, the random walk forecast errors fail to be orthogonal to the change in consumer opinion about gasoline prices, fuel efficiency and poor selections (quality). Such findings remain unchanged when we use the ‘weakly’ rational forecasts of gasoline prices instead of the random walk benchmark. Put together, our results provide new insights into the potential usefulness of consumer vehicle‐buying assessments for predicting gasoline prices. We recommend forecasters pay special attention to such assessments when forecasting retail gasoline prices, since they can potentially help produce forecasts that are superior to both the random walk and ‘weakly’ rational forecasts.

Forecasting energy commodity prices: A large global dataset sparse approach

This paper focuses on forecasting quarterly nominal global energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information on this large database, we apply dynamic factor models based on a penalized maximum likelihood approach that allows to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selected loadings across variables. When the model is extended to predict energy commodity prices up to four periods ahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter ahead for all energy commodities and up to 4 quarters ahead for gas prices. Our model also provides superior forecasts than machine learning techniques, such as elastic net, LASSO and random forest, applied to the same database.

Oil prices and experts’ forecasts of seven exchange rates

ABSTRACT Research indicates that survey-based forecasts of exchange rates fail to be rational. In addition, the literature points to oil prices as a key factor in determining/predicting exchange rates. We investigate these two research topics by focusing on Blue Chip multi-month forecasts of seven exchange rates for 1999–2015. Our investigation utilizes the recent forecast evaluation method that allows for the possibility of asymmetric loss. We show that Blue Chip forecasts, while free of systematic bias, cannot beat the random walk benchmark, and, at some forecast horizons, the errors fail to be orthogonal to changes in oil prices. We further show that, for four out of seven exchange rates, the change in oil prices explains the information content difference between the Blue Chip and random walk forecasts. Put together, our findings suggest that the inaccuracy of Blue Chip forecasts may be in part due to an inefficient use of oil price information available at the time of the forecast and, thus, forecasters should pay special attention to oil price movements when forecasting exchange rates.

Predicting Interest Rates Using Shrinkage Methods, Real-Time Diffusion Indexes, and Model Combinations

In the context of predicting the term structure of interest rates, we explore the marginal predictive content of real-time diffusion indexes extracted from a “data rich” real-time dataset, when used in dynamic Nelson-Siegel (NS) models of the variety discussed in Diebold and Li (DNS: 2007) and Svensson (NSS: 1994). We find that the indexes have significant predictive content for sample periods ranging from 2001 through 2010. Additionally, DNS and NSS type models that include these indexes are the mean square forecast error (MSFE) “best” performers, when compared with various other econometric specifications, for the same period. In our top ranked models, diffusion indexes are sometimes optimally constructed used un-targeted principal component analysis (PCA), and sometimes using targeted PCA in conjunction with elastic net and least absolute shrinkage operators. The news is not all good for models utilizing indexes constructed from real-time datasets, however. In particular, after 2010 relatively few “data-rich” prediction models “beat” a random walk benchmark. Also, forecast combinations that utilize models that exclude real-time diffusion indexes yield the lowest overall MSFEs, dominating all other combination and individual models, across all sample periods, forecast horizons and bond maturities. Two key conclusions from our analysis are the following. First, fully revised data may have an important confounding effect upon results obtained when instead carrying out real-time prediction experiments. Second, real-time diffusion indexes matter when comparing the predictive performance of individual models, indicating the presence of unspanned macroeconomic risks in the term structure of interest rates. However, there appear to be two different ways to “capture” these unspanned risks. One is to use data rich real-time diffusion indexes, and another is to simply combine predictions from many non-data rich models.

The discontinuation of the EUR/CHF minimum exchange rate: information from option-implied break probabilities

Abstract We derive risk-neutral probability densities for future euro/Swiss franc exchange rates as implied by option prices. We find that the credibility of the Swiss franc floor decreased somewhat as the spot exchange rate approached the lower bound of 1.20 CHF per euro. We also compare the forecasting performance of a random walk benchmark model with an error-correction model (ECM) augmented with option-implied break probabilities of breaching the currency floor. We find some evidence that the augmented ECM has an informational advantage over the random walk when using one-month break probabilities. But we find that one-month option-implied densities cannot predict the entire range of exchange rate realizations.

Distributional Forests for Short-Term Density Predictions of U.S. Inflation

Recent work by Medeiros et al. (2019, Journal of Business & Economic Statistics) shows that point forecasts of the random forest machine learning algorithm systematically outperform well-established benchmarks at predicting U.S. inflation. This article extends their work from point to density forecasts derived from large-scale datasets by means of the novel distributional forest algorithm of Schlosser et al. (2019, The Annals of Applied Statistics), which allows us to simultaneously account for locally evolving means, time-varying volatilities, nonlinearities as well as heavy-tailed densities – all of which constitute stylized characteristics highlighted in the recent literature. We show that distributional forests typically achieve superior point and density predictions over random walk, auto-regressive and stochastic volatility benchmarks.

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.

Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models

We use 1982–2014 data on the US riskless yield curve to show that regime switching dynamics in Nelson-Siegel factor models extended to encompass variables that summarize the state of monetary policy lead to superior predictive accuracy. Such spread in forecasting power turns out to be statistically significant even controlling for parameter uncertainty and sample variation. Exploiting regimes, we obtain evidence that the increase in predictive accuracy is stronger during the Great Financial Crisis, when monetary policy underwent a significant, sudden shift. Although more caution applies in comparisons to a naïve random walk benchmark over a few sub-samples and when transaction costs are accounted for, we report that the increase in predictive power owed to the combination of regimes and of variables that capture the stance of unconventional monetary policies is tradeable. We devise and test butterfly strategies that exploit the forecasts from the models and obtain evidence of risk-adjusted profits both per se and in comparisons to simpler models.

A Credit-Based Theory of the Currency Risk Premium

This paper extends Kremens and Martin (2019) and uncovers a novel component for exchange rate predictability based on the price difference between sovereign credit default swaps denominated in different currencies. This new forecasting variable - the credit-implied risk premium - captures the expected currency depreciation conditional on a severe but rare credit event. Using data for 16 Eurozone countries, we find that the credit-implied risk premium positively forecasts the dollar-euro exchange rate return at various horizons. Moreover, a currency strategy that exploits the informative content of our predictor generates substantial out-of-sample economic value against the naive random walk benchmark.