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.

Colombo, E., Rossignoli, R., Forte, G., Carry Trade Returns with Support Vector Machines, <<INTERNATIONAL REVIEW OF FINANCE>>, 2018; 2018 (Febbraio): 1-22. [doi:10.1111/irfi.12186] [http://hdl.handle.net/10807/121414]

Carry Trade Returns with Support Vector Machines

Colombo, Emilio
Primo
;
2018

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.
2018
Inglese
Colombo, E., Rossignoli, R., Forte, G., Carry Trade Returns with Support Vector Machines, <<INTERNATIONAL REVIEW OF FINANCE>>, 2018; 2018 (Febbraio): 1-22. [doi:10.1111/irfi.12186] [http://hdl.handle.net/10807/121414]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/121414
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