Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/306122 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 43 [Issue:] 6 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2024 [Pages:] 2163-2186
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
To improve the dynamic assessment of risks of speculative assets, we apply a Markov switching MGARCH approach to portfolio risk forecasting. More specifically, we take advantage of the flexible Markov switching copula multivariate GARCH (MS‐C‐MGARCH) model of Fülle and Herwartz (2022). As an empirical illustration, we take the perspective of a risk‐averse agent and employ the suggested model for assessments of future risks of portfolios composed of a high‐yield equity index (S&P 500) and two safe‐haven investment instruments (i.e., Gold and US Treasury Bond Futures). We follow recent suggestions to employ the expected shortfall as a prime assessment of tail risks. To accurately evaluate the merits of the new model, we back‐test the risk forecasting for daily returns over 10 years for heterogeneous market environments including, for example, the COVID‐19 pandemic. We find that the MS‐C‐MGARCH model outperforms benchmark volatility models (MGARCH, C‐MGARCH) in predicting both value‐at‐risk and expected shortfall. The superiority of the MS‐C‐MGARCH model becomes stronger, when the share of comparably risky assets in the portfolio is relatively large.
Subjects: 
copula
ES
forecasting
Markov switching
MGARCH
VaR
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article
Document Version: 
Published Version

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