Forecasting exchange rates out-of-sample with panel methods and real-time data

Ince, Onur Ulas

doi:10.1016/j.jimonfin.2013.12.004

Cited by 50 publications

(32 citation statements)

References 43 publications

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“…8 The exchange rates are end-of-month nominal exchange rates from the Federal Reserve Bank of Saint Louis database. The exchange rate for Germany after 1998 is replaced with a synthetic Deutsche mark/dollar rate, which is calculated, as in Engel et al (2008) and Ince (2014), using the rate of depreciation of the dollar/Euro rate.…”

Section: Datamentioning

confidence: 99%

Taylor rule deviations and out-of-sample exchange rate predictability

Ince

Molodtsova

Papell

2016

Journal of International Money and Finance

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The Taylor rule has become the dominant model for academic evaluation of out-of-sample exchange rate predictability. Two versions of the Taylor rule model are the Taylor rule fundamentals model, where the variables that enter the Taylor rule are used to forecast exchange rate changes, and the Taylor rule differentials model, where a Taylor rule with postulated coefficients is used in the forecasting regression. We use data from 1973 to 2014 to evaluate short-run out-ofsample predictability for eight exchange rates vis-à-vis the U.S. dollar, and find strong evidence in favor of the Taylor rule fundamentals model alternative against the random walk null. The evidence of predictability is weaker with the Taylor rule differentials model, and still weaker with the traditional interest rate differential, purchasing power parity, and monetary models. The evidence of predictability for the fundamentals model is not related to deviations from the original Taylor rule for the U.S., but is related to deviations from a modified Taylor rule for the U.S. with a higher coefficient on the output gap. The evidence of predictability is also unrelated to deviations from Taylor rules for the foreign countries and adherence to the Taylor principle for the U.S. A B S T R A C T Keywords:Exchange rates Out-of-sample exchange rate predictability Taylor rulesThe Taylor rule has become the dominant model for academic evaluation of out-of-sample exchange rate predictability. Two versions of the Taylor rule model are the Taylor rule fundamentals model, where the variables that enter the Taylor rule are used to forecast exchange rate changes, and the Taylor rule differentials model, where a Taylor rule with postulated coefficients is used in the forecasting regression. We use data from 1973 to 2014 to evaluate short-run out-of-sample predictability for eight exchange rates vis-à-vis the U.S. dollar, and find strong evidence in favor of the Taylor rule fundamentals model alternative against the random walk null. The evidence of predictability is weaker with the Taylor rule differentials model, and still weaker with the traditional interest rate differential, purchasing power parity, and monetary models. The evidence of predictability for the fundamentals model is not related to deviations from the original Taylor rule for the U.S., but is related to deviations from a modified Taylor rule for the U.S. with a higher coefficient on the output gap.

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Section: Datamentioning

confidence: 99%

Taylor rule deviations and out-of-sample exchange rate predictability

Ince

Molodtsova

Papell

2016

Journal of International Money and Finance

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“…An important step forward in this strand of research was that these models can be now brought to the data via the use of advanced estimation techniques (An and Schorfheide, 2007). From an econometric perspective, recent writings have also convincingly presented evidence that models estimated with large panels of data are able to outperform the RW in exchange rate forecasting (Engel, 2013;Ince, 2014). This suggests that the dismal forecasting performance of exchange rate models can be partly attributed to estimation rather than mis-specification error.…”

Section: Introductionmentioning

confidence: 99%

Exchange rate forecasting with DSGE models

Zorzi

Kolasa

Rubaszek

2017

Journal of International Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe run a real exchange rate forecasting "horse race", which highlights that two principles hold. First, forecasts should not replicate the high volatility of exchange rates observed in sample. Second, models should exploit the mean reversion of the real exchange rate over long horizons. Abiding by these principles, an open-economy DSGE model performs well in real exchange rate forecasting. However, it fails to forecast nominal exchange rates better than the random walk. We find that the root cause is its inability to predict domestic and foreign inflation. This shortcoming leads us toward simpler ways to outperform the random walk. Non-technical summaryEconomic theory provides policymakers with clear guidance on how the competitiveness channel operates in the aftermath of a wide set of disturbances, such as monetary, productivity, risk premium or foreign shocks. However, there is a cloud hanging over this aspect of international economics, namely that these conjectures may have limited empirical significance, given the systematic failure of macro models to beat even the naïve random walk (RW) in exchange rate forecasting (the "exchange rate disconnect puzzle"). The question then naturally arises of whether international macro models are rich enough to be meaningful. Layers of complexity are typically added to improve their realism. For example, including in the features of the model the currency of trade invoicing may help the model to capture better the degree of exchange rate pass-through. Similarly, distinguishing the currency of denomination of asset and liabilities, may lead to a better description of the dynamics of external debt, which may be essential to better understand real exchange rate movements in emerging countries. On the other hand, imposing too many restrictions on the data generating process, either theoretically or in the estimation phase, may prove disadvantageous from a pure forecasting perspective given the higher number of estimated parameters. Every cloud has a silver lining, however. The exchange rate disconnect puzzle has spurred economists to look for new directions of research with success. Openeconomy dynamic stochastic general equilibrium (DSGE) models are clearly a major accomplishment from the theoretical perspective. The empirical literature has also shown why, by properly accounting for estimation error, exchange rate models may be better than we usually think. The consen...

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“…Authors (Kong et al 2015) investigated cloud enabled real time platform for trading. Author of paper (Ince 2014) researched that panel method and real time data increases the predictability of exchange rates. Kvietkauskiene (2013) investigated adequate portfolio strategies and monitored them by real time demo platform.…”

Section: Introductionmentioning

confidence: 99%

Impact of time zones on forecasting of exchange market based on distribution of expected values

Maknickienė

Maknickas

2016

9th International Scientific Conference “Business and Management 2016”

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Abstract. Forecasting of chaotic changes of exchange rates usually is based on historical data and depends on the choice of time intervals. This study seeks to develop new forecasting method based on data of different time zones. This paper demonstrates how the using of London and New York divisions of the trading day allows getting additional information from predicting exchange rates. This was modelled with the help of ensemble of EVOLINO for obtaining of predictions of the distribution of expected values. The obtained results show that double forecasts evaluation reveals a possible trend in the exchange market and enriches the choice of real-time trading strategies.

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Forecasting exchange rates out-of-sample with panel methods and real-time data

Cited by 50 publications

References 43 publications

Taylor rule deviations and out-of-sample exchange rate predictability

Taylor rule deviations and out-of-sample exchange rate predictability

Exchange rate forecasting with DSGE models

Impact of time zones on forecasting of exchange market based on distribution of expected values

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