Volatility forecasts using stochastic volatility models with nonlinear leverage effects

Kenichiro McAlinn, Asahi Ushio, Teruo Nakatsuma

Research output: Contribution to journalArticlepeer-review

Abstract

The leverage effect—the correlation between an asset's return and its volatility—has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence puzzlingly does not show that this effect exists for many individual stocks, mischaracterizing risk, and therefore leading to poor predictive performance. We examine this puzzle, with the goal to improve density forecasts, by relaxing the assumption of linearity of the leverage effect. Nonlinear generalizations of the leverage effect are proposed within the Bayesian stochastic volatility framework in order to capture flexible leverage structures. Efficient Bayesian sequential computation is developed and implemented to estimate this effect in a practical, on-line manner. Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find that our proposed nonlinear leverage effect model improves predictive performances for 89% of all stocks compared to the conventional stochastic volatility model.

Original languageEnglish
Pages (from-to)143-154
Number of pages12
JournalJournal of Forecasting
Volume39
Issue number2
DOIs
Publication statusPublished - 2020 Mar 1

Keywords

  • Bayesian analysis
  • leverage effect
  • particle learning
  • stochastic volatility
  • volatility forecasting

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

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