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

Nicklas Werge
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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

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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.

Keywords: Hidden Markov model; Financial time series; Non-stationarity; Regime Switching; Prediction markets; Trading strategies (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-rmg
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Published in Expert Systems with Applications, 2021, 184, pp.115576. ⟨10.1016/j.eswa.2021.115576⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03313129

DOI: 10.1016/j.eswa.2021.115576

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