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Carry trade returns with Support Vector Machines

Emilio Colombo (), Gianfranco Forte () and Roberto Rossignoli ()

No dis1705, DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo from Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS)

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.

Date: 2017
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Journal Article: Carry Trade Returns with Support Vector Machines (2019) Downloads
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