timing foreign exchange markets

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ID: 132906
2016
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Abstract
To improve short-horizon exchange rate forecasts, we employ foreign exchange market risk factors as fundamentals, and Bayesian treed Gaussian process (BTGP) models to handle non-linear, time-varying relationships between these fundamentals and exchange rates. Forecasts from the BTGP model conditional on the carry and dollar factors dominate random walk forecasts on accuracy and economic criteria in the Meese-Rogoff setting. Superior market timing ability for large moves, more than directional accuracy, drives the BTGP’s success. We explain how, through a model averaging Monte Carlo scheme, the BTGP is able to simultaneously exploit smoothness and rough breaks in between-variable dynamics. Either feature in isolation is unable to consistently outperform benchmarks throughout the full span of time in our forecasting exercises. Trading strategies based on ex ante BTGP forecasts deliver the highest out-of-sample risk-adjusted returns for the median currency, as well as for both predictable, traded risk factors.
Reference Key
malone2016econometricstiming Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Samuel W. Malone;Robert B. Gramacy;Enrique ter Horst
Journal developmental cognitive neuroscience
Year 2016
DOI
10.3390/econometrics4010015
URL
http://www.mdpi.com/2225-1146/4/1/15
Keywords Keywords not found

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