Artikel

Portfolio optimization on multivariate regime-switching garch model with normal tempered stable innovation

This paper uses simulation-based portfolio optimization to mitigate the left tail risk of the portfolio. The contribution is twofold. (i) We propose the Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. The volatility of each asset independently follows the regime-switch GARCH model, while the correlation of joint innovation of the GARCH models follows the Hidden Markov Model. (ii) We use tail risk measures, namely conditional value-at-risk (CVaR) and conditional drawdown-at-risk (CDaR), in the portfolio optimization. The optimization is performed with the sample paths simulated by the MRS-MNTS-GARCH model. We conduct an empirical study on the performance of optimal portfolios. Out-of-sample tests show that the optimal portfolios with tail measures outperform the optimal portfolio with standard deviation measure and the equally weighted portfolio in various performance measures. The out-of-sample performance of the optimal portfolios is also more robust to suboptimality on the efficient frontier.

Sprache: Englisch

Erschienen in: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 5 ; Pages: 1-23

Klassifikation: Management

Thema: conditional value-at-risk
conditional drawdown-at-risk
GARCH model
Markov regime-switching model
normal tempered stable distribution
portfolio optimization

Ereignis: Geistige Schöpfung

(wer): Peng, Cheng
Kim, Young Shin
Mittnik, Stefan

Ereignis: Veröffentlichung

(wer): MDPI

(wo): Basel

(wann): 2022

DOI: doi:10.3390/jrfm15050230

Handle: http://hdl.handle.net/10419/274752

Letzte Aktualisierung: 10.03.2025, 11:41 MEZ

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Artikel

Beteiligte

Peng, Cheng
Kim, Young Shin
Mittnik, Stefan
MDPI

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2022

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Portfolio optimization on multivariate regime-switching garch model with normal tempered stable innovation

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