First passage times in portfolio optimization: A novel nonparametric approach

Gabriel Zsurkis, João Nicolau and Paulo Rodrigues

European Journal of Operational Research, 2024, vol. 312, issue 3, 1074-1085

Abstract: This paper introduces a portfolio optimization procedure that aims to minimize the intra-horizon (IH) risk subject to a minimum expected time to achieve a target cumulative return. To estimate the first passage probabilities and the expected time a novel nonparametric method and a new Markov chain order determination approach are developed. The optimization framework proposed allows us to include novel path-dependent measures of risk and return in the asset allocation problem. An empirical application to S&P 100 companies, a risk-free asset and stock indices is provided. Our empirical results suggest that the proposed framework exhibits more consistency between in-sample and out-of-sample performance than the mean-variance model and an alternative optimization problem that minimizes the MaxVaR measure of Boudoukh et al. (2004). Overall, the portfolio optimization approach we introduce results in higher out-of-sample annualized returns for relatively low levels of IH risk.

Keywords: Portfolio optimization; Markov chains; Intra-horizon risk; First-passage probability (search for similar items in EconPapers)
JEL-codes: C14 C22 C41 G11 G17 (search for similar items in EconPapers)
Date: 2024
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Working Paper: First passage times in portfolio optimization: a novel nonparametric approach (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:312:y:2024:i:3:p:1074-1085

DOI: 10.1016/j.ejor.2023.07.044

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