Forecasting exchange rates in the presence of instabilities

Ribeiro, Pinho J. (2016) Forecasting exchange rates in the presence of instabilities. PhD thesis, University of Glasgow.

Abstract

Many exchange rate papers articulate the view that instabilities constitute a major impediment to exchange rate predictability. In this thesis we implement Bayesian and other techniques to account for such instabilities, and examine some of the main obstacles to exchange rate models' predictive ability. We first consider in Chapter 2 a time-varying parameter model in which fluctuations in exchange rates are related to short-term nominal interest rates ensuing from monetary policy rules, such as Taylor rules. Unlike the existing exchange rate studies, the parameters of our Taylor rules are allowed to change over time, in light of the widespread evidence of shifts in fundamentals - for example in the aftermath of the Global Financial Crisis. Focusing on quarterly data frequency from the crisis, we detect forecast improvements upon a random walk (RW) benchmark for at least half, and for as many as seven out of 10, of the currencies considered. Results are stronger when we allow the time-varying parameters of the Taylor rules to differ between countries.
In Chapter 3 we look closely at the role of time-variation in parameters and other sources of uncertainty in hindering exchange rate models' predictive power. We apply a Bayesian setup that incorporates the notion that the relevant set of exchange rate determinants and their corresponding coefficients, change over time. Using statistical and economic measures of performance, we first find that predictive models which allow for sudden, rather than smooth, changes in the coefficients yield significant forecast improvements and economic gains at horizons beyond 1-month. At shorter horizons, however, our methods fail to forecast better than the RW. And we identify uncertainty in coefficients' estimation and uncertainty about the precise degree of coefficients variability to incorporate in the models, as the main factors obstructing predictive ability.
Chapter 4 focus on the problem of the time-varying predictive ability of economic fundamentals for exchange rates. It uses bootstrap-based methods to uncover the time-specific conditioning information for predicting fluctuations in exchange rates. Employing several metrics for statistical and economic evaluation of forecasting performance, we find that our approach based on pre-selecting and validating fundamentals across bootstrap replications generates more accurate forecasts than the RW. The approach, known as bumping, robustly reveals parsimonious models with out-of-sample predictive power at 1-month horizon; and outperforms alternative methods, including Bayesian, bagging, and standard forecast combinations.
Chapter 5 exploits the predictive content of daily commodity prices for monthly commodity-currency exchange rates. It builds on the idea that the effect of daily commodity price fluctuations on commodity currencies is short-lived, and therefore harder to pin down at low frequencies. Using MIxed DAta Sampling (MIDAS) models, and Bayesian estimation methods to account for time-variation in predictive ability, the chapter demonstrates the usefulness of suitably exploiting such short-lived effects in improving exchange rate forecasts. It further shows that the usual low-frequency predictors, such as money supplies and interest rates differentials, typically receive little support from the data at monthly frequency, whereas MIDAS models featuring daily commodity prices are highly likely. The chapter also introduces the random walk Metropolis-Hastings technique as a new tool to estimate MIDAS regressions.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: PhD Thesis Examiners: Prof. Pasquale Della Corte and Prof. Ronald Mcdonald; PhD Viva Outcome: Outright Pass (Summa Cum Laude). The material in Chapter 2 has been published as Byrne, J.P., Korobilis, D. and Ribeiro, P.J. (2016). ''Exchange Rate Predictability in a Changing World.'' Journal of International Money and Finance, vol. 62, pp. 1-24. http://dx.doi.org/10.1016/j.jimonfin.2015.12.001 . All the remaining chapters (3,4,5), are currently under review for possible publication.
• Keywords: Exchange rate forecasting, time-varying parameters model, Bayesian dynamic linear models, Bayesian model averaging, Bayesian model selection, instabilities in exchange rate models, forecast combinations, economic evaluation of exchange rate models, statistical evaluation of exchange rate models, commodity prices and exchange rates, MIDAS model, bagging, bumping, bootstrap aggregation, Metropolis-Hastings algorithm, exponential almon lag polynomial, present-value exchange rate models, exchange rate predictability, Taylor rules.
• Subjects: H Social Sciences > H Social Sciences (General); H Social Sciences > HA Statistics; H Social Sciences > HG Finance
• Colleges/Schools: College of Social Sciences > School of Social and Political Sciences
• Supervisor's Name: Korobilis, Prof. Dimitris, Byrne, Prof. Joseph P. and Sermpinis, Dr. Georgios
• Date of Award: 2016
• Depositing User: Dr. Pinho Ribeiro
• Unique ID: glathesis:2016-7519
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 25 Aug 2016 15:24
• Last Modified: 13 Sep 2016 10:14
• URI: https://theses.gla.ac.uk/id/eprint/7519

Related URLs

• http://dx.doi.org/10.1016/j.jimonfin.201...
• http://dx.doi.org/10.2139/ssrn.2502586
• http://ssrn.com/abstract=2687064
• http://dx.doi.org/10.2139/ssrn.2396138