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dc.contributor.authorRohrbach, Janick-
dc.contributor.authorSuremann, Silvan-
dc.contributor.authorOsterrieder, Jörg-
dc.description.abstractMomentum trading strategies are thoroughly described in the academic literature and used in many trading strategies by hedge funds, asset managers, and proprietary traders. Baz et al. (2015) describe a momentum strategy for different asset classes in great detail from a practitioner’s point of view. Using a geometric Brownian Motion for the dynamics of the returns of financial instruments, we extensively explain the motivation and background behind each step of a momentum trading strategy. Constants and parameters that are used for the practical implementation are derived in a theoretical setting and deviations from those used in Baz et al. (2015) are shown. The trading signal is computed as a mixture of exponential moving averages with different time horizons. We give a statistical justification for the optimal selection of time horizons. Furthermore, we test our approach on global currency markets, including G10 currencies, emerging market currencies, and cryptocurrencies. Both a time series portfolio and a cross-sectional portfolio are considered. We find that the strategy works best for traditional fiat currencies when considering a time series based momentum strategy. For cryptocurrencies, a cross-sectional approach is more suitable. The momentum strategy exhibits higher Sharpe ratios for more volatile currencies. Thus, emerging market currencies and cryptocurrencies have better performances than the G10 currencies. This is the first comprehensive study showing both the underlying statistical reasons of how such trading strategies are constructed in the industry as well as empirical results using a large universe of currencies, including cryptocurrencies.de_CH
dc.publisherSocial Science Research Networkde_CH
dc.relation.ispartofInternational Finance eJournalde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleMomentum and trend following trading strategies for currencies revisited : combining academia and industryde_CH
dc.typeBeitrag in Magazin oder Zeitungde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
Appears in Collections:Publikationen School of Engineering

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