Predicting the equity risk premium using the smooth cross-sectional tail risk: the importance of correlation

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Abstract

I provide a new monthly cross-sectional measure of stock market tail risk, SCSTR, defined as the average of the daily cross-sectional tail risk, rather than the tail risk of the pooled daily returns within a month. Through simulations, I find that SCSTR better captures monthly tail risk rather than merely the tail risk on specific days within a month. In an extended period from 1964 until 2018, this difference is important in generating strong in- and out-of-sample predictability and performs better than the historical risk premium and other commonly-used predictors for short- and long-term horizons.
Original languageEnglish
Article number100769
JournalJournal of Financial Markets
DOIs
Publication statusAccepted/In press - 1 Jul 2022

Keywords

  • Equity premium
  • Prediction
  • Cross-sectional

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