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First passage times in portfolio optimization
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Orientador(es)
Resumo(s)
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.
Descrição
Funding Information: The authors thank three anonymous referees and Editor Roman Slowinski for their helpful and constructive feedback on an earlier version of this paper. Financial support from the Portuguese Science Foundation (FCT) through project PTDC/EGE-ECO/28924/2017, and (UID/ECO/00124/2013 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209) is also gratefully acknowledged. Publisher Copyright: © 2023 The Authors
Palavras-chave
First-passage probability Intra-horizon risk Markov chains Portfolio optimization General Computer Science Modelling and Simulation Management Science and Operations Research Information Systems and Management