Dynamic forecasting of monetary exchange rate models: Evidence from cointegration

The Frenkel-Bilson and Dornbusch-Frankel monetary exchange rate models are used to estimate the out-of-sample forecasting performance for the U.S. dollar/Canadian dollar exchange rate. By using Johansen's multivariate cointegration, up to three cointegrating vectors were found between the exchange rate and macroeconomic fundamentals. This means that there is a long-run relationship between the exchange rate and economic fundamentals. Based on error correction models, two monetary models outperform the random walk model at the three-, six-, and 12-month forecasting horizons. Therefore, monetary exchange rate models are still useful in forecasting exchange rates.     

The Taylor Rule,Wealth Effects and the Exchange Rate

In this study, we develop the Taylor rule and Taylor rule‐based exchange rate models that consider wealth effects as represented by both asset prices and asset wealth. Using data for Australia, Sweden, the UK and the USA, we find that effects of asset prices and wealth on the Taylor rule vary depending on the country and on the form that wealth takes. Out‐of‐sample forecasting capacities of the wealth‐augmented Taylor rule model and Taylor rule‐based exchange rate model outperform conventional models and random walk theories for these countries.     

Real exchange rate dynamics: Relative importance of Taylor‐rule fundamentals,monetary policy shocks,and risk‐premium shocks

We first show that the solution to the real exchange rate under the Taylor rule with interest rate smoothing can have two alternative representations—one based on a first‐order difference equation and the other based on a second‐order difference equation. Then, by comparing error terms from these two alternative representations and analyzing their second moments, we evaluate the relative importance of Taylor‐rule fundamentals, monetary policy shocks, and risk‐premium shocks in the dynamics of the real exchange rate. Empirical results suggest that the risk‐premium shock is the largest contributor to real exchange rate movements for all the countries examined, with the Taylor‐rule fundamentals and monetary policy shocks playing a limited role. These results are robust to various alternative sets of parameter values considered for the Taylor rule with interest rate smoothing.     

FORECASTING THE UK/US EXCHANGE RATE WITH DIVISIA MONETARY MODELS AND NEURAL NETWORKS

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative merits of the official Simple Sum and the weighted Divisia measures of money. Overall, there are four main findings. First, the majority of the models with fundamentals are able to beat the RW model in forecasting the UK/US exchange rate. Second, the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model. Third, taking into account structural breaks reveals that the Divisia aggregate performs better than its Simple Sum counterpart. Finally, Divisia‐based models provide more accurate forecasts than Simple Sum‐based models provided they are constructed within a nonlinear framework.     

Non‐linearities,Business Cycles and Exchange Rates

This paper conjoins the disparate empirical literatures on exchange rate models and monetary policy models, with special reference to the importance of output, inflation gaps and exchange rate targets. It focuses in on the dollar/euro exchange rate, and the differential results arising from using alternative measures of the output gap for the US and for the Euro area. A comparison of ‘in‐sample’ prediction against alternative models of exchange rates is also conducted. In addition to predictive power, I also assess the various models' plausibility as economic explanations for exchange rate movements, based on the conformity of coefficient estimates with priors. Taylor rule fundamentals appear to do as well, or better, than other models at the 1‐year horizon.     

Cointegration as an explanation for the Meese–Rogoff puzzle

Some economists suggest that the failure of exchange-rate models to outperform the random walk in exchange rate forecasting out of sample can be attributed to failure to take into account cointegration when it is present. We attempt to find out if cointegration matters for forecasting accuracy by examining the relation between the stationarity and size of the forecasting error. Results based on three macroeconomic models of exchange rates do not provide strong support for the proposition that cointegration matters for forecasting accuracy. The simulation results show that while stationary errors tend to be smaller than non-stationary errors, this is not a universal rule. Irrespective of the presence or absence of cointegration, none of the three models can outperform the random walk in out-of-sample forecasting, which means that cointegration cannot solve the Meese–Rogoff puzzle.     

Dissecting Exchange Rates and Fundamentals in the Modern Floating Era: The Role of Permanent and Transitory Shocks

In this paper, we apply a permanent–transitory decomposition method to analyze the link between nominal exchange rates and fundamentals in the modern floating era. The results suggest that transitory shocks dominate nominal exchange rate fluctuations, while permanent shocks dominate the variations in fundamentals. Therefore, the findings suggest that the nominal exchange rate should not be approximated by a pure random walk. Moreover, we find that unobserved fundamentals in the Taylor rule model can explain the transitory components in exchange rates.     

Error correction modelling and dynamic specifications as a conduit to outperforming the random walk in exchange rate forecasting

The proposition that dynamic exchange rate models can outperform the random walk in out-of-sample forecasting, in the sense that they produce lower mean square errors, is examined and disputed. By using several dynamic versions of three macroeconomic exchange rate models, it is demonstrated that dynamic specifications outperform the corresponding static models but improvement in the forecasting power may not be sufficient for the dynamic models to perform better than the random walk. The results are explained by suggesting that any dynamic specification or transformation of the static model leads to the introduction of a lagged dependent variable, which in effect is a random walk component. The analysis leads to the conclusion that it is implausible to aim at beating the random walk by augmenting a static model with a random walk component.     

A Rotated Dynamic Nelson‐Siegel Model

I show how to rotate the factor structure of the well‐known Dynamic Nelson‐Siegel yield‐curve model to enable direct parametrization of the short rate process. This makes it easy to calculate model‐implied term premia and to integrate macroeconomic variables into the model in a Taylor‐rule‐type fashion.     

Can Economists Forecast Exchange Rates? The Debate Re‐Visited: The Case of the USD/GBP Market

In the debate on forecasting exchange rates, critics claimed that traditional macroeconomic models could not outperform a random walk in post‐sample forecasts. Perceived deficiencies include inadequate allowance for simultaneity, and expectations hypotheses inconsistent with the structure of models employed. This paper re‐visits the debate, first to address critics' major concerns, and second because in the view of the present authors, the debate closed on an unduly pessimistic note. This paper develops a simultaneous, rational expectations model of the USD/GBP market, with functional relationships for hedgers, speculators and a spot rate equation. The model is estimated with data contemporaneous to the debate, including a period during which the US Commodity Futures Trading Commission did not collect data on traders' open positions. Using the results of post‐debate research on tests for stationarity with missing observations, the model, using only public information, outperforms a random walk in post‐sample forecasts of the spot rate. Recent microstructure models of the exchange rate based on order flow have re‐kindled the forecasting debate. The model developed here, however, is differentiated from these microstructure models, first because order flow utilises both public and private information, and second because the microstructure models do not directly address critics' concerns.     

Forecasting exchange rates: The multi-state Markov-switching model with smoothing

This paper presents an exchange rate forecasting model which combines the multi-state Markov-switching model with smoothing techniques. The model outperforms a random walk at short horizons and its superior forecastability appears to be robust over different sample spans. Our finding hinges on the fact that exchange rates tend to follow highly persistent trends and accordingly, the key to beating the random walk is to identify these trends. An attempt to link the trends in exchange rates to the underlying macroeconomic determinants further reveals that fundamentals-based linear models generally fail to capture the persistence in exchange rates and thus are incapable of outforecasting the random walk.     

House price to income ratio and fundamentals: Evidence on long‐horizon forecastability

This paper studies the relationship between the house price‐to‐income ratio (PIR) and economic fundamentals, and investigates the long‐horizon forecastability of the PIR. We first construct a small DSGE model to derive a dynamic expression of PIR, linking PIR to macroeconomic fundamentals and the stance of monetary policy. Based on the theoretically derived PIR, variance decomposition suggests that interest rate and real income growth appear to be the main sources for the deviations of PIR. Using the difference between actual PIR and the estimated fundamental PIR as the predictor, we find that both in‐sample and out‐of‐sample forecastability of the PIR over the future dynamics of PIR are significant.     

Forecasting the yield curve and the role of macroeconomic information in Turkey

In this study we investigate the yield curve forecasting performance of Dynamic Nelson–Siegel Model (DNS), affine term structure VAR model (ATSM VAR) and principal component model (PC) in Turkey. We also investigate the role of macroeconomic variables in forecasting the yield curve. We have reached numbers of important results: 1—Macroeconomic variables are very useful in forecasting the yield curve. 2—The forecasting performances of the models depend on the period under review. 3—Considering the structural break which associates with change in monetary policy leads models to produce better forecasts than the random walk. 4—The role of exchange rate should not be ruled out in forecasting the yield curve in an emerging market like Turkey.     

Why is it so difficult to outperform the random walk in exchange rate forecasting?

A simulation exercise is used to demonstrate the difficulty to outperform the random walk in exchange rate forecasting if forecasting accuracy is judged by the Root Mean Square Error (RMSE) or similar criteria that depend on the magnitude of the forecasting error. It is shown that, as the exchange rate volatility rises, the RMSE of the model rises faster than that of the random walk. While the literature considers this finding to be a puzzle that casts a big shadow of doubt on the soundness of international monetary economics, the results show that failure to outperform the random walk, in both in-sample and out-of-sample forecasting, should be the rule rather than the exception. However, the results do not imply that the random walk is unbeatable, because it can be easily outperformed if forecasting accuracy is judged according to criteria such as direction accuracy and profitability.     

Cointegration,structural breaks and monetary fundamentals of the Dollar/Yen Exchange

This paper reexamines the empirical performance of monetary exchange rate models for the dollar/yen exchange rate. We focus on the character of a potential long-run relationship between exchange rates and fundamentals. Using monthly data from 1976:01 to 2007:12 this paper applies a novel time-varying coefficient approach which allows a distinction between breaks in the cointegration vector and instabilities in the adjustment coefficients. We are able to show that most of the observed breakpoints can be traced back to major policy changes or specific economic developments. Our findings also show that macroeconomic fundamentals do matter for the U.S. dollar/Japanese yen exchange rate, but in different ways over different periods of time.     

Should monetary policy respond to asset price misalignments?

This paper analyses the relationship between monetary policy and asset prices using a structural rational expectations open economy model that allows for the effect of asset prices and exchange rates on aggregate demand. We assume that asset prices and exchange rates follow a partial adjustment mechanism whereas they are positively affected by past changes, thus allowing for ‘momentum trading’, while at the same time we allow for reversion towards fundamentals. We then conduct stochastic simulations using two alternative monetary policy rules, inflation-forecast targeting and the standard Taylor rule. The results indicate that, under both rules, interest rate setting that takes into account asset price misalignments leads to lower overall macroeconomic volatility, as measured by the postulated loss function of the central bank.     

Professional forecasts of interest rates and exchange rates: Evidence from the Wall Street Journal’s panel of economists

We analyze economists’ forecasts of interest rates and exchange rates from the Wall Street Journal. We find that a majority of economists produced unbiased forecasts but that none predicted directions of changes more accurately than chance. Most economists’ forecast accuracy is statistically indistinguishable from a random walk model in forecasting the Treasury bill rate, but many are significantly worse in forecasting the Treasury bond rate and the exchange rate. We also find systematic forecast heterogeneity, support for strategic models predicting the industry employing the economist matters, and evidence that economists deviate less from the consensus as they age.     

The accuracy of interest rate forecasts in the Asia-Pacific region: opportunities for portfolio management

We analysed interest rate forecasts from Australia, China, Hong Kong, India, Indonesia, Malaysia, New Zealand, Singapore, South Korea, Taiwan and Thailand. We assessed 532 forecast time series with a total of 85,264 individual interest rate forecasts. To do so, we carried out a comparison to naïve forecasts and investigated the forecast time series for topically orientated trend adjustments. In addition, we deployed the sign accuracy test and the unbiasedness test. The results are very sobering in part: 95.9% of all forecast time series are characterized by the phenomenon of topically orientated trend adjustments, and 99.4% of all forecast time series proved to be biased. Only a small proportion of the forecast time series (3.6%) reflected the future interest rate trend significantly more precisely than a naïve forecast. However, at the same time some of the results of the study are surprisingly positive. The sign accuracy test revealed that 48.3% of all forecast time series predict the interest rate trend significantly better than a random walk forecast.     

A Comparison of Alternative Exchange Rate Forecasting Models*

This paper compares a number of structural and times-series models on the basis of their accuracy in forecasting the A ustralian-US dollar exchange rate out of sample. Purchasing power parity, forward exchange theory, static and dynamic specifications of both the flexible price and sticky price monetary models, and univariate ARIMA models are considered in the paper. Exchange rate forecasts are generated at horizons of one to four quarters. In contrast to overseas results which support the view that the exchange rate follows a random walk, several models in this study are found to generate forecasts superior to the random walk model.