Template-Type: ReDIF-Paper 1.0
Author-Name: Emilio Colombo
Author-Email: emilio.colombo@unicatt.it
Author-Name: Gianfranco Forte
Author-Email: gianfranco.forte@unimib.it
Author-Name: Roberto Rossignoli
Author-Email: roberto.rossignoli@moneyfarm.com
Title: Carry trade returns with Support Vector Machines
Abstract: This paper proposes a novel approach to directional forecasts for carry trade strategies based on Support Vector Machines (SVMs), a learning algorithm that delivers extremely promising results. Building on recent findings in the literature on carry trade, we condition the SVM on indicators of uncertainty and risk. We show that this provides a dramatic performance improvement in strategy, particularly during periods of financial distress such as the recent financial crises. Disentangling the measures of risk, we show that conditioning the SVM on measures of liquidity risk rather than on market volatility yields the best performance.
Classification-JEL:
Creation-Date: 2017
File-URL: http://dipartimenti.unicatt.it/diseis-wp_1705.pdf
File-Format: Application/PDF
Number: dis1705
Handle: RePEc:dis:wpaper:dis1705