Forecasting Disconnected Exchange Rates

Berge, Travis J.

doi:10.1002/jae.2350

Cited by 31 publications

(11 citation statements)

References 66 publications

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“…See, for example,Bai and Ng (2009);Khandani et al (2010);Berge (2014);Ng (2014).4 The subscript t h 1 indicates that many of the indicators included in the model search have a publication lag of 2-3 weeks. In the forecast experiment, the forecaster waits until each indicator is available, the third week of the month, then produces forecasts.…”

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confidence: 99%

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Berge

2015

Journal of Forecasting

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Four methods of model selection-equally weighted forecasts, Bayesian model-averaged forecasts, and two models produced by the machine-learning algorithm boosting-are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real-time forecast experiment explores the predictability of the 2001 and 2007 recessions.

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confidence: 99%

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Berge

2015

Journal of Forecasting

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“…Other techniques have been successfully used, including elastic net shrinkage (Li, Tsiakas, & Wang, ), gradient boosting (Berge, ), and model averaging/selection (Della Corte, Sarno, & Tsiakas, ; Della Corte & Tsiakas, ; Kouwenberg, Markiewicz, Verhoeks, & Zwinkels, ). All these approaches find sparsity to be an important modeling feature and, in particular, Kouwenberg et al () illustrate also the time‐varying relevance of regressors in a univariate framework.…”

Section: Relation To the Literaturementioning

confidence: 99%

Exchange rate predictability and dynamic Bayesian learning

Beckmann

Koop

Korobilis

et al. 2020

J of Applied Econometrics

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We consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross-section.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…predictive content of the fundamentals like IRD [Bacchetta and van Wincoop, 2013, Berge, 2014, Ismailov and Rossi, 2018, but also misspecifications of the models traditionally used to conduct these forecasts [Cheung et al, 2005, Rossi, 2013, Ismailov and Rossi, 2018.…”

Section: Introductionmentioning

confidence: 99%

Interest Rate Differentials and the Dynamic Asymmetry of Exchange Rates

Hambuckers

Ulm

2020

SSRN Journal

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In this paper, we revisit the predictive content of interest rates for daily exchange rate returns. The novelty of our approach is to take into account dependencies of higher orders by allowing for a time-varying asymmetry in the distribution of exchange rates. Using data on USD/EUR currency pair over the period 1999-2019, we find the dynamic asymmetry component to be significant and driven by interest rate differentials, but also by general uncertainty and past unexpected shocks. In line with recent currency crash theories, our study suggests that the larger the difference between interest rates, the more likely the high yield currency is to appreciate but also to experience currency crashes. To assess the economic significance of our results, we introduce a directional forecasting approach derived from our model. We show that a trading rule based on these forecasts provides better in-sample and out-of-sample economic performance compared to benchmark models.

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Forecasting Disconnected Exchange Rates

Cited by 31 publications

References 66 publications

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Exchange rate predictability and dynamic Bayesian learning

Interest Rate Differentials and the Dynamic Asymmetry of Exchange Rates

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