• Medientyp: E-Book
  • Titel: Information Spillovers and Predictable Currency Returns : An Analysis via Machine Learning
  • Beteiligte: Jia, Yuecheng [Verfasser:in]; Wu, Yangru [Sonstige Person, Familie und Körperschaft]; Yan, Shu [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [S.l.]: SSRN, [2020]
  • Umfang: 1 Online-Ressource (43 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.3320199
  • Identifikator:
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 4, 2019 erstellt
  • Beschreibung: This paper employs the post — Least Absolute Shrinkage and Selection Operator (post — LASSO) to make rolling 1-month--ahead currency excess return forecasts using all other currencies' lagged forward discounts as candidate predictors. The trading strategy of buying (selling) quintile currency portfolios of the high (low) post — LASSO forecasts yields a monthly excess return of 1.05% for the 48-currency sample. The results do not change even after controlling for various predictors. The return predictive power of the post — LASSO comes from two sources. First, it identifies the origin currencies of information spillovers in the FX market, which are sparse and time-varying. Second, it incorporates cross-sectional variations in currencies' predictive relations with the origin currencies
  • Zugangsstatus: Freier Zugang