Point, Interval and Density Forecasts of Exchange Rates with Time Varying Parameter Models

Abstract

Summary.We explore whether modelling parameter time variation improves the point, interval and density forecasts of nine major exchange rates vis-à-vis the US dollar over the period 1976-2015. We find that modelling parameter time variation is needed for an accurate calibration of forecast confidence intervals and is better suited at long horizons and in high volatility periods. The biggest forecast improvements are obtained by modelling time variation in the volatilities of the innovations, rather than in the slope parameters. We do not find evidence that parameter time variation helps to unravel exchange rate predictability by macroeconomic fundamentals. However, an economic evaluation of the various forecast models reveals that controlling for parameter time variation and macroeconomic fundamentals leads to higher portfolios returns, and to higher utility values for investors.

Exchange rate predictability and dynamic Bayesian learning

“…Carriero, Kapetanios, and Marcellino () work with a large Bayesian VAR involving a cross‐section of exchange rates and find forecast improvements from considering dynamic comovements of exchange rates. Abbate and Marcellino () extend Carriero et al () by allowing for, among other things, time‐varying coefficients and volatilities, and find the latter to be particularly useful in improving forecast performance. These considerations suggest that working with VARs with time‐varying volatilities is potentially important and our modeling approach does so.…”

“…The literature has also explored the implications of exchange rate predictability (or a lack thereof) for an investor wishing to build an investment portfolio involving various exchange rates; see, for instance, Abhyankar, Sarno, and Valente (), Della Corte et al (), Kouwenberg et al (), and Abbate and Marcellino ().…”

Exchange rate forecasting and the performance of currency portfolios

“…This research question links our work to the large literature on the statistical and economic evaluation of exchange rate forecasts (see Abbate & Marcellino, 2018;Della Corte, Sarno, & Tsiakas, 2009;Rossi, 2013) and provides a novel evaluation context that goes beyond the existing methods based on forecast errors and directional change statistics. First, does the information on exchange rate fundamentals provide valuable information to construct optimal currency portfolio that outperform simple benchmark portfolios, and thus is there a value added in engaging in active portfolio management-or can the portfolio manager achieve the same (risk-adjusted) profit by just investing in some simpler assets (benchmark portfolios)?…”

“…As simpler assets we consider the single assets of which the optimal portfolio consists as well as the equally weighted portfolio based on forecasts from the model based on macroeconomic fundamentals as well as on random walk predictions. This research question links our work to the large literature on the statistical and economic evaluation of exchange rate forecasts (see Abbate & Marcellino, 2018;Della Corte, Sarno, & Tsiakas, 2009;Rossi, 2013) and provides a novel evaluation context that goes beyond the existing methods based on forecast errors and directional change statistics.…”

Markov-Switching Three-Pass Regression Filter

“…The first application is related to exchange rate forecasting. This is highly relevant given that it has long been recognized that non-linearities play an important role in the dynamics of exchange rates (see, e.g., the early contribution in Chinn (1991) and more recently Rossi (2013) and Abbate and Marcellino (2016)). However, it is only recently that the literature on exchange rate forecasting has concentrated on the role and importance of factors for predicting exchange rates (see, e.g., Engel et al (2015) in a linear context).…”