A factor model for predicting exchange rate fluctuations

Abstract

Weinvestigatetherelationshipbetweenriskandreturnsintheexchangeratemarket and propose a new statistical model for predicting currency returns using the Instrumented Principal Component Analysis (IPCA) (Kelly, Pruitt, and Su, 2019). Weshowthatthemodelwithtime-varyingloadingsandlatentfactorsoutperforms the existingfactor-basedstrategiesin-sampleandout-of-sample.Specifically,the four-factorIPCAmodelexplainsupto64%ofcurrencyreturnsvariation,whilethe model withobservablefactorsshowstheperformanceof57%.Wehavefoundthat the IPCAfactorsthatexplainthecross-sectionofcurrencyreturnsareglobalvolatil- ity,carrytrade,dollar,andmomentum.Theresultsaretestedin-sampleandout- sample and hold for individual currencies and managed portfolios.