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.

中文翻译：

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使用随机均值 CVaR 优化和时变风险规避来避免动量崩溃

摘要

在所谓动量崩溃的情况下，通常有效的金融资产配置横截面动量策略会产生严重的负回报。我们开发了一种随机平均风险优化模型，以 CVaR 为特征来控制风险，动态调整 CVaR 尾部概率和目标函数权重，以及通过混合矩匹配生成的回报场景。在 95 年的回测中，通过我们的方法重新平衡的投资组合比通过横截面动量启发式重新平衡的投资组合提供更高的回报和更低的风险，同时避免动量崩溃。