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Predicting Risk-adjusted Returns using an Asset Independent Regime-switching Model

Author

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  • Nicklas Werge

    (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité)

Abstract

Financial markets tend to switch between various market regimes over time, making stationarity-based models unsustainable. We construct a regime-switching model independent of asset classes for risk-adjusted return predictions based on hidden Markov models. This framework can distinguish between market regimes in a wide range of financial markets such as the commodity, currency, stock, and fixed income market. The proposed method employs sticky features that directly affect the regime stickiness and thereby changing turnover levels. An investigation of our metric for risk-adjusted return predictions is conducted by analyzing daily financial market changes for almost twenty years. Empirical demonstrations of out-of-sample observations obtain an accurate detection of bull, bear, and high volatility periods, improving risk-adjusted returns while keeping a preferable turnover level.

Suggested Citation

  • Nicklas Werge, 2021. "Predicting Risk-adjusted Returns using an Asset Independent Regime-switching Model," Post-Print hal-03313129, HAL.
    • Handle: RePEc:hal:journl:hal-03313129
    DOI: 10.1016/j.eswa.2021.115576
    Note: View the original document on HAL open archive server: https://hal.science/hal-03313129v1
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    References listed on IDEAS

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    1. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
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    More about this item

    Keywords

    Hidden Markov model; Financial time series; Non-stationarity; Regime Switching; Prediction markets; Trading strategies;
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