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Avoiding momentum crashes using stochastic mean-CVaR optimization with time-varying risk aversion

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  • Xiaoshi Guo
  • Sarah M. Ryan

Abstract

In occasions called momentum crashes, the usually effective cross-sectional momentum strategy for financial asset allocation produces drastically negative returns. We develop a stochastic mean-risk optimization model featuring CVaR to control the risk, dynamically adjusted CVaR tail probability and objective function weight, and return scenarios generated by hybrid moment-matching. In a 95-year backtest, portfolios rebalanced by our method provide higher returns and lower risk than those rebalanced by a cross-sectional momentum heuristic, while avoiding momentum crashes.

Suggested Citation

  • Xiaoshi Guo & Sarah M. Ryan, 2023. "Avoiding momentum crashes using stochastic mean-CVaR optimization with time-varying risk aversion," The Engineering Economist, Taylor & Francis Journals, vol. 68(3), pages 125-152, July.
  • Handle: RePEc:taf:uteexx:v:68:y:2023:i:3:p:125-152
    DOI: 10.1080/0013791X.2023.2229620
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