Fundamentals and Exchange Rate Prediction Revisited

This paper measures latent fundamental exchange rates with independent component‐based rates constructed from a cross‐section of exchange rates and then uses their deviations from exchange rates to forecast. Empirical results indicate that the independent component‐based model and its Taylor rule and purchasing power parity augmented models are superior to the random walk in predicting exchange rates. These results are robust to several scenarios and are likely to be observed if the U.S. sources and the recursive scheme are applied. Our results reveal that information regarding the third moment of exchange rate changes is helpful to explain exchange rate movements.     

A new approach to forecasting exchange rates

Building on purchasing power parity theory, this paper proposes a new approach to forecasting exchange rates using the Big Mac data from The Economist magazine. Our approach is attractive in three aspects. Firstly, it uses easily-available Big Mac prices as input. These prices avoid several potential problems associated with broad price indexes, such as the consumer price index used in conventional PPP studies. Secondly, this approach provides real-time exchange-rate forecasts at any forecast horizon. These high-frequency forecasts could be appealing to those who want up-to-date exchange-rate forecasts. Finally, as our forecasts are obtained through a simulation procedure, estimation uncertainty is made explicit in our framework that provides the entire distribution of exchange rates, not just a single point estimate. Using exchange rates of six major currencies to illustrate the approach, we compare the Big Mac forecasts with those derived from a random walk and the CPI and find some support for our approach, especially at longer term horizons.     

Exchange rate expectations and the pricing of Chinese cross-listed stocks

I show that the price discounts of Chinese cross-listed stocks (American Depositary Receipts (ADRs) and H-shares) to their underlying A-shares indicate the expected yuan/US dollar exchange rate. The forecasting models reveal that ADR and H-share discounts predict exchange rate changes more accurately than the random walk and forward exchange rates, particularly at long forecast horizons. Using panel estimations, I find that ADR and H-share investors form their exchange rate expectations according to standard exchange rate theories such as the Harrod-Balassa-Samuelson effect, the risk of competitive devaluations, relative purchasing power parity, uncovered interest rate parity, and the risk of currency crisis.     

New evidence on nominal exchange rate predictability

The Meese–Rogoff puzzle, one of the well-known puzzles in international economics, concerns the weak relationship between nominal exchange rates and market fundamentals. The purpose of this paper is to show that market fundamentals do in fact matter in forecasting nominal exchange rates. In particular, we emphasize the importance of the Harrod–Balassa–Samuelson effect in modeling deviations from purchasing power parity. Based on the post-Bretton Woods period, we provide solid out-of-sample evidence that rejects the random walk forecast model at medium-term and long-term forecast horizons. We also find mild evidence for out-of-sample predictability of nominal exchange rates over the short term.     

Forecasting government bond yields with large Bayesian vector autoregressions

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. The optimal shrinkage is chosen by maximizing the Marginal Likelihood of the model. Focusing on the US, we provide an extensive study on the forecasting performance of the proposed model relative to most of the existing alternative specifications. While most of the existing evidence focuses on statistical measures of forecast accuracy, we also consider alternative measures based on trading schemes and portfolio allocation. We extensively check the robustness of our results, using different datasets and Monte Carlo simulations. We find that the proposed BVAR approach produces competitive forecasts, systematically more accurate than random walk forecasts, even though the gains are small.     

Exchange rates,innovations and forecasting

In this paper an ex-post forecasting experiment is performed on the basis of a version of the ‘news’ model of exchange rate determination. For several exchange rates the ‘news’ formulation of monetary exchange rate models leads to relatively accurate ex-post exchange rate forecasts at a number of forecasting horizons. For a majority of the exchange rates studied, however, the results do not compare favorably with those obtained from the naive random walk forecasting rule. Thus, the findings in this article provide mixed evidence with regard to a suggestion in the literature that the finding by Meese and Rogoff that structural models do not even outperform the random walk in an ex-post forecasting experiment, may be due to the fact that these models were not properly tested in a ‘news’ framework.     

Evaluating forecasts from SETAR models of exchange rates

We consider the forecasting performance of two SETAR exchange rate models proposed by Kräger and Kugler [J. Int. Money Fin. 12 (1993) 195]. Assuming that the models are good approximations to the data generating process, we show that whether the non-linearities inherent in the data can be exploited to forecast better than a random walk depends on both how forecast accuracy is assessed and on the ‘state of nature’. Evaluation based on traditional measures, such as (root) mean squared forecast errors, may mask the superiority of the non-linear models. Generalized impulse response functions are also calculated as a means of portraying the asymmetric response to shocks implied by such models.     

The forecasting efficiency of the dynamic Nelson Siegel model on credit default swaps

This paper extends the Diebold–Li dynamic Nelson Siegel model to a new asset class, credit default swaps (CDSs). The similarities between the term structure of CDSs and the term structure of interest rates allow CDS curves to be modelled successfully using a parsimonious three factor model as first proposed by Nelson and Siegel (1987). CDSs and yield curves are modelled using the Diebold and Li (2006) dynamic interpretation of the Nelson Siegel model where the three factors are representative of the level, slope and curvature of the curve. Our results show that the CDS curve fits the data well and allows for the various shapes exhibited by the CDS data including steep, inverted and downward sloping curves. In addition to in sample fit of the modelled curve we explore the out of sample forecasting abilities of the model and using a univariate autoregressive model we forecast 1, 5 and 10 days ahead. Our results show that although the one day ahead forecast under performs the random walk, the 5 and 10 day forecast consistently outperforms the random walk for both yields and CDSs. This study reaffirms the ability of the Diebold–Li (2006) methodology to forecast yields and provides new evidence that this methodology is efficacious when applied to CDS spreads.     

Structural models of exchange rate determination

This study compares the forecasting accuracy of state space techniques based on the monetary models of exchange rate with univariate and random walk models for four countries. It is found that these structural models outperform ARIMA and random walk models for all four countries. A state space vector that contains variables based on the monetary model easily outperforms random walk as well as ARIMA models for France, Germany, UK, and Japan during the sample period of this study.     

Forecasting the Term Structure of South African Government Bond Yields

This article uses the parsimonious dynamic Nelson–Siegel model to fit the yields of South African government bonds. We find that the dynamic Nelson–Siegel model has good fitting abilities for all maturities. We further forecast the term structure by seven different dynamic Nelson–Siegel models with time series models. We find that the DNS–VAR–GARCH model is useful for forecasting the short-term rates, the DNS–VAR best predicts the medium-term rates, and the DNS–RW best predicts the long-term rates. In addition, the dynamic Nelson–Siegel models provide better forecasts of yield data than a random walk model, especially for the 12-month forecasting horizons.     

Investment intensity of currencies and the random walk hypothesis: Cross-currency evidence

This paper studies the cross-currency and temporal variations in the random walk behavior in exchange rates. We characterize currencies with relatively large investment flows as investment intensive and conjecture that the more investment intensive a currency is, the closer its exchange rate adheres to random walk. Using 29 floating bilateral USD exchange rates, we find that the higher the investment intensity, the less likely it is to reject random walk and the smaller the deviation from random walk is. However, the effect of investment intensity is non-monotonic. Application of threshold models shows that after investment intensity reaches the estimated thresholds, the level of investment intensity has no further effect on the deviation from random walk. These findings help reconcile the previous conflicting results on the random walk in exchange rates by focusing on the effect of cross-currency and temporal variations in investment intensity.     

Nonlinear exchange rate predictability

We study whether the nonlinear behavior of the real exchange rate can help us account for the lack of predictability of the nominal exchange rate. We construct a smooth nonlinear error-correction model that allows us to test the hypotheses of nonlinear predictability of the nominal exchange rate and nonlinear behavior on the real exchange rate in the context of a fully specified cointegrated system. Using a panel of 19 countries and three numeraires, we find evidence of nonlinear predictability of the nominal exchange rate and of nonlinear mean reversion of the real exchange rate. Out-of-sample Theil’s U -statistics show a higher forecast precision of the nonlinear model than the one obtained with a random walk specification. Although the robustness of the out-of-sample results over different forecast windows is somewhat limited, we are able to obtain significant predictability gains—from a parsimonious structural model with PPP fundamentals—even at short-run horizons.     

Forecasting the price of gold using dynamic model averaging

We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas City Fed's financial stress index and the U.S. economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons. Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme.     

Order Flow and the Monetary Model of Exchange Rates: Evidence from a Novel Data Set

We propose an exchange rate model that is a hybrid of the conventional specification with monetary fundamentals and the Evans–Lyons microstructure approach. We estimate a model augmented with order flow variables, using a unique data set: almost 100 monthly observations on interdealer order flow on dollar/euro and dollar/yen. The augmented macroeconomic, or “hybrid,” model exhibits greater in‐sample stability and out of sample forecasting improvement vis‐à‐vis the basic macroeconomic and random walk specifications.     

The impact of uncertainty on professional exchange rate forecasts

This paper analyzes the role of uncertainty on both exchange rate expectations and forecast errors of professionals for four major currencies based on survey data provided by FX4casts. We consider economic policy, macroeconomic, and financial uncertainty as well as disagreement among CPI inflation forecasters to account for different dimensions of uncertainty. Based on a Bayesian VAR approach, we observe that uncertainty effects on forecast errors of professionals turn out to be more significant compared to the adjustment of exchange rate expectations. Our findings are robust to different forecasting horizons and point to an unpredictable link between exchange rates and fundamentals. Furthermore, we illustrate the importance of considering common unpredictable components for a large number of variables. We also focus on the post-crisis period and the relationship between uncertainty and disagreement among exchange rate forecasters and identify a strong relationship between them.     

Forecasting the yield curve with linear factor models

In this work we compare the interest rate forecasting performance of a broad class of linear models. The models are estimated through a MCMC procedure with data from the US and Brazilian markets. We show that a simple parametric specification has the best predictive power, but it does not outperform the random walk. We also find that macroeconomic variables and no-arbitrage conditions have little effect to improve the out-of-sample fit, while a financial variable (Stock Index) increases the forecasting accuracy.     

Can Affine Term Structure Models Help Us Predict Exchange Rates?

This paper proposes an arbitrage-free model to extract the information that the term structure of forward premia contains for forecasting future spot exchange rates. Using monthly data on four U.S. dollar bilateral exchange rates, we find evidence that this model provides statistically better forecasts than those produced by a random walk for the British pound and Canadian dollar exchange rates. Negative results for the German mark/Euro and Swiss franc are explained by a rejection of the restrictions imposed by the term structure model.     

Heterogeneity of agents and exchange rate dynamics: Evidence from the EMS

We develop and estimate a dynamic heterogeneous agent model for the EMS period. Our empirical results suggest that the existence of heterogeneous interacting agents is indeed a possible explanation for the dynamics of exchange rates during the EMS. We find strong evidence of heterogeneous boundedly rational beliefs, and the fact that agents switch between these beliefs. Moreover, we show that the dynamic heterogeneous agent model outperforms the random walk and the static heterogeneous agents’ model in out-of-sample forecasting in the large majority of country-horizon combinations.     

Generating currency trading rules from the term structure of forward foreign exchange premia

The quality of an exchange rate forecasting model has typically been judged relative to a random-walk in terms of out-of-sample forecast errors. The difficulty of outperforming this benchmark is well documented, although Clarida and Taylor have demonstrated how the random walk can be beaten in this metric by exploiting information embedded within the term structure of forward exchange rate premia. But this achievement does not guarantee success within an investment context. We therefore assess whether the Clarida-Taylor framework can be used to generate significant trading profits in combination with an acceptable degree of risk in a realistic investment portfolio context.     

Why Do Analysts use a Zero Forecast for Other Comprehensive Income?

Accounting theory and accounting researchers stress the importance of clean surplus accounting and comprehensive income to corporate valuation. However, casual observation suggests that sell-side equity analysts routinely ignore other comprehensive income (OCI) in their forecasts and instead focus on forecasting earnings (before OCI). Using a sample of analyst reports, I first confirm that analysts normally omit forecasts of OCI or comprehensive income from their reports, consistent with analysts forecasting OCI as zero. I then predict and find that a zero forecast for OCI generally produces lower forecasting errors than alternative time-series models, such as a random walk or AR(1) model, suggesting a rational reason why analysts take this approach. Finally, I predict and find that although analysts’ point forecasts of future OCI are usually zero, their implied cost of equity estimates are consistent with analysts forecasting a positive variance for OCI.