Exchange rate forecasting and the performance of currency portfolios

We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates: the euro versus the US dollar, the British pound, and the Japanese yen. We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error‐based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state‐of‐the‐art econometric models in currency portfolios. These benefits vary across investor preferences and prediction horizons but are rather similar across trading strategies.     

The Predictive Power of Survey‐Based Exchange Rate Forecasts: Is there a Role for Dispersion?

Previous research has shown that the consensus of individual exchange rate forecasts performs no better than many commonly used forecasting models in predicting future exchange rates. Studies on equity and bond markets have explored the effects of dispersion in forecasts on the predictive power of forecasts; however, no earlier paper has investigated such effects in the context of the foreign exchange market. This study explores the role of consensus forecast dispersion as a factor leading to bias and anchoring in exchange rate forecasts. Our analysis of five currency pairs reveals that consensus forecasts mostly appear to be unbiased predictors of exchange rates in the long run, but most are unable to pass tests for short‐run unbiasedness. In three of the five currencies examined it appears that forecasters should take greater account of reported forecast dispersion. Copyright © 2015 John Wiley & Sons, Ltd.     

Combining exchange rate forecasts: What is the optimal consensus measure?

In this paper a high-quality disaggregate database is utilized to examine whether individual forecasters produce efficient exchange rate predictions and also if the properties of the forecasts change when they are combined. The paper links a number of themes in the exchange rate literature and examines various methods of forecast combination. It is demonstrated, inter alia, that some forecasters are better than others, but that most are not as good as a naive no-change prediction. Combining forecasts adds to the accuracy of the predictions, but the gains mainly reflect the removal of systematic and unstable bias.     

Twisting the Dollar? On the Consistency of Short‐Run and Long‐Run Exchange Rate Expectations

We examine consistency properties of the exchange rate expectation formation process of short‐run and long‐run forecasts in the dollar/euro and yen/dollar market. Applying nonlinear consistency restrictions we show that in a simple expectation formation structure short‐run forecasts are indeed inconsistent with long‐run predictions. Moreover, we establish a ‘twist’ in the dollar/euro expectation formation process, i.e. market participants expect bandwagon effects in the short run, while they have stabilizing expectations in their long‐run forecasts. Applying a panel probit analysis we find that this twisting behavior is more likely to occur in periods of excess exchange rate volatility. Copyright © 2011 John Wiley & Sons, Ltd.     

Model instability in predictive exchange rate regressions

In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with transitions across regimes being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at home and in the USA. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time, and a model approach that takes this empirical evidence seriously yields more accurate density forecasts for most currency pairs considered.     

Testing the rationality of forecast revisions made by the IMF and the OECD

We investigate the rationality of forecast revisions made by the IMF and the OECD over the past three decades. We find that 60% of real‐GDP forecast series and 37% of GDP‐deflator forecast series are consistent with rationality. Forecast smoothing is found in real‐GDP forecasts. Copyright © 2006 John Wiley & Sons, Ltd.     

Are forecasters reluctant to revise their predictions? Some German evidence

People are reluctant to admit mistakes. This could also be true of economic forecasters. If revisions of past forecasts are costly, then it will become optimal for forecasters to only partially adjust a past forecast in the light of new information. The unwillingness to admit to the mistake in the old forecast generates a bias of the new forecast in the direction of the old forecast. We test this hypothesis for the joint predictions of the Association of German Economic Research Institutes over the last 35 years. We find some evidence for such a bias and compute the implied unwillingness to revise forecasts. Copyright © 2006 John Wiley & Sons, Ltd.     

Overreaction in Survey Exchange Rate Forecasts

We use survey data on five bilateral exchange rates to provide empirical evidence of the fact that professional forecasters of foreign exchange rates behave irrationally, in the specific sense that they respond inaccurately to available information in the market when forming their predictions. In particular, we find systematic biases in the forecasts resulting in the overreaction of analysts to past information contained in the exchange rate dynamics: forecasters change their prediction more than it would be rational on the basis of past realized changes. In addition, forecasters are heterogeneous in their irrationality: low performers in previous periods show a more pronounced overreaction effect. This can be read as an indication of perpetration of past errors and continued inability to learn from the past. In the second part of the paper, we exploit the novel structure of our dataset, which consists of survey data extracted from the Bloomberg platform and readily available to anyone. This feature allows us to consider their own and others' past forecasts as part of the information set that analysts use in making their predictions. By using past forecasts as proxies for relevant macroeconomic variables, we find evidence that analysts fail to correctly process not only the information contained in the spot rate past dynamics but also the information in this broader set. We see this as confirmation of the existence of inefficiency and heterogeneity between low and high performers also when full information is available. Copyright © 2014 John Wiley & Sons, Ltd.     

The use of encompassing tests for forecast combinations

This paper proposes an algorithm that uses forecast encompassing tests for combining forecasts when there are a large number of forecasts that might enter the combination. The algorithm excludes a forecast from the combination if it is encompassed by another forecast. To assess the usefulness of this approach, an extensive empirical analysis is undertaken using a US macroeconomic dataset. The results are encouraging; the algorithm forecasts outperform benchmark model forecasts, in a mean square error (MSE) sense, in a majority of cases. The paper also compares the empirical performance of different approaches to forecast combination, and provides a rule‐of‐thumb cut‐off point for the thick‐modeling approach. Copyright © 2009 John Wiley & Sons, Ltd.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

Non‐linear forecasts of stock returns

Following recent non‐linear extensions of the present‐value model, this paper examines the out‐of‐sample forecast performance of two parametric and two non‐parametric nonlinear models of stock returns. The parametric models include the standard regime switching and the Markov regime switching, whereas the non‐parametric are the nearest‐neighbour and the artificial neural network models. We focused on the US stock market using annual observations spanning the period 1872–1999. Evaluation of forecasts was based on two criteria, namely forecast accuracy and forecast encompassing. In terms of accuracy, the Markov and the artificial neural network models produce at least as accurate forecasts as the other models. In terms of encompassing, the Markov model outperforms all the others. Overall, both criteria suggest that the Markov regime switching model is the most preferable non‐linear empirical extension of the present‐value model for out‐of‐sample stock return forecasting. Copyright © 2003 John Wiley & Sons, Ltd.     

The maximum and minimum of primary forecasts

The purpose of this paper is to suggest that the maximum (or minimum) of a number of primary forecasts may make a valuable addition to the forecasting accuracy of a combination of forecasts. Such forecasts are readily computable. Theoretical results are presented for two unbiased forecasts with correlated normally distributed errors, showing that the maximum (minimum) of two forecasts can have a smaller error variance than either of the primary forecasts and the forecast error can have low correlation with the primary errors. Empirical results are obtained for two different sets of forecasts available in the literature, and it is observed that a combination forecast including the maximum and/or minimum has attractive forecasting properties.     

A comparison of temperature density forecasts from GARCH and atmospheric models

Density forecasts for weather variables are useful for the many industries exposed to weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. The distribution of the scenarios can be used as a density forecast, which is needed for pricing weather derivatives. We consider one to 10‐day‐ahead density forecasts provided by temperature ensemble predictions. More specifically, we evaluate forecasts of the mean and quantiles of the density. The mean of the ensemble scenarios is the most accurate forecast for the mean of the density. We use quantile regression to debias the quantiles of the distribution of the ensemble scenarios. The resultant quantile forecasts compare favourably with those from a GARCH model. These results indicate the strong potential for the use of ensemble prediction in temperature density forecasting. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecast Evaluation of Nonlinear Models: The Case of Long‐Span Real Exchange Rates

This paper deals with the nonlinear modeling and forecasting of the dollar–sterling and franc–sterling real exchange rates using long spans of data. Our contribution is threefold. First, we provide significant evidence of smooth transition dynamics in the series by employing a battery of recently developed in‐sample statistical tests. Second, we investigate the small‐sample properties of several evaluation measures for comparing recursive forecasts when one of the competing models is nonlinear. Finally, we run a forecasting race for the post‐Bretton Woods era between the nonlinear real exchange rate model, the random walk, and the linear autoregressive model. The nonlinear model outperforms all rival models in the dollar–sterling case but cannot beat the linear autoregressive in the franc–sterling. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluating forecasts: a look at aggregate bias and accuracy measures

In this paper an investigation is made of the properties and use of two aggregate measures of forecast bias and accuracy. These are metrics used in business to calculate aggregate forecasting performance for a family (group) of products. We find that the aggregate measures are not particularly informative if some of the one‐step‐ahead forecasts are biased. This is likely to be the case in practice if frequently employed forecasting methods are used to generate a large number of individual forecasts. In the paper, examples are constructed to illustrate some potential problems in the use of the metrics. We propose a simple graphical display of forecast bias and accuracy to supplement the information yielded by the accuracy measures. This support includes relevant boxplots of measures of individual forecasting success. This tool is simple but helpful as the graphic display has the potential to indicate forecast deterioration that can be masked by one or both of the aggregate metrics. The procedures are illustrated with data representing sales of food items. Copyright © 2005 John Wiley & Sons, Ltd.     

The performance of non‐linear exchange rate models: a forecasting comparison

In recent years there has been a considerable development in modelling non‐linearities and asymmetries in economic and financial variables. The aim of the current paper is to compare the forecasting performance of different models for the returns of three of the most traded exchange rates in terms of the US dollar, namely the French franc (FF/$), the German mark (DM/$) and the Japanese yen (Y/$). The relative performance of non‐linear models of the SETAR, STAR and GARCH types is contrasted with their linear counterparts. The results show that if attention is restricted to mean square forecast errors, the performance of the models, when distinguishable, tends to favour the linear models. The forecast performance of the models is evaluated also conditional on the regime at the forecast origin and on density forecasts. This analysis produces more evidence of forecasting gains from non‐linear models. Copyright © 2002 John Wiley & Sons, Ltd.     

Forecasting the dollar/euro exchange rate: are international parities useful?

In this paper we assess the empirical relevance of an expectations version of purchasing power parity in forecasting the dollar/euro exchange rate. This version is based on the differential of inflation expectations derived from inflation‐indexed bonds for the euro area and the USA. Using the longest daily data for both the dollar/euro exchange rate and for the inflation expectations, our results suggest that, with few exceptions, our predictors behave significantly better than a random walk in forecasts up to five days, both in terms of prediction errors and in directional forecasts. Copyright © 2005 John Wiley & Sons, Ltd.     

Reconfigurable combined forecasts in a non-stationary inflationary environment

This paper presents a composite method for non-stationary economic forecasts, incorporating reconfigurable exponential trend extraction and linear combinations of parabolic and spectral estimators for short-term prediction. The method automatically identifies the points of time series misalignment induced by sharp environmental changes. An application to the problem of hard currency exchange rate prediction in Russia is presented.     

The importance of time‐varying volatility and country interactions in forecasting economic activity

This paper examines the relative importance of allowing for time‐varying volatility and country interactions in a forecast model of economic activity. Allowing for these issues is done by augmenting autoregressive models of growth with cross‐country weighted averages of growth and the generalized autoregressive conditional heteroskedasticity framework. The forecasts are evaluated using statistical criteria through point and density forecasts, and an economic criterion based on forecasting recessions. The results show that, compared to an autoregressive model, both components improve forecast ability in terms of point and density forecasts, especially one‐period‐ahead forecasts, but that the forecast ability is not stable over time. The random walk model, however, still dominates in terms of forecasting recessions.     

An assessment of the economic value of non-linear foreign exchange rate forecasts

Recent research suggests that non-linear methods cannot improve the point forecasts of high-frequency exchange rates. These studies have been using standard forecasting criteria such as smallest mean squared error (MSE) and smallest mean absolute error (MAE). It is, however, premature to conclude from this evidence that non-linear forecasts of high-frequency financial returns are economically or statistically insignificant. We prove a proposition which implies that the standard forecasting criteria are not necessarily particularly suited for assessment of the economic value of predictions of non-linear processes where the predicted value and the prediction error may not be independently distributed. Adopting a simple non-linear forecasting procedure to 15 daily exchange rate series we find that although, when compared to simple random walk forecasts, all the non-linear forecasts give a higher MSE and MAE, when applied in a simple trading strategy these forecasts result in a higher mean return. It is also shown that the ranking of portfolio payoffs based on forecasts from a random walk, and linear and non-linear models, is closely related to a non-parametric test of market timing.