Downside Risk Reduction Using Regime-Switching Signals: A Statistical Jump Model Approach
Yizhan Shu, Chenyu Yu, John M. Mulvey

TL;DR
This paper presents a regime-switching investment strategy using a statistical jump model (JM) to identify market regimes, effectively reducing downside risk and improving risk-adjusted returns across major global equity indices from 1990 to 2023.
Contribution
It introduces a robust jump model for regime detection that outperforms traditional models, optimizing strategy performance through a novel jump penalty selection method.
Findings
JM-guided strategy reduces volatility and maximum drawdown.
Strategy improves Sharpe ratio compared to benchmarks.
Outperforms traditional Markov-switching and buy-and-hold strategies.
Abstract
This article investigates a regime-switching investment strategy aimed at mitigating downside risk by reducing market exposure during anticipated unfavorable market regimes. We highlight the statistical jump model (JM) for market regime identification, a recently developed robust model that distinguishes itself from traditional Markov-switching models by enhancing regime persistence through a jump penalty applied at each state transition. Our JM utilizes a feature set comprising risk and return measures derived solely from the return series, with the optimal jump penalty selected through a time-series cross-validation method that directly optimizes strategy performance. Our empirical analysis evaluates the realistic out-of-sample performance of various strategies on major equity indices from the US, Germany, and Japan from 1990 to 2023, in the presence of transaction costs and trading…
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Taxonomy
TopicsCredit Risk and Financial Regulations · Global Financial Crisis and Policies · Banking stability, regulation, efficiency
MethodsSparse Evolutionary Training · Focus
