The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Value-at-Risk Prediction: A Comparison of Alternative Strategies
Open accessAbstract
Given the growing need for managing financial risk, risk prediction plays an increasing role in banking and finance. In this study we compare the out-of-sample performance of existing methods and some new models for predicting value-at-risk (VaR) in a univariate context. Using more than 30 years of the daily return data on the NASDAQ Composite Index, we find that most approaches perform inadequately, although several models are acceptable under current regulatory assessment rules for model adequacy. A hybrid method, combining a heavy-tailed generalized autoregressive conditionally heteroskedastic (GARCH) filter with an extreme value theory-based approach, performs best overall, closely followed by a variant on a filtered historical simulation, and a new model based on heteroskedastic mixture distributions. Conditional autoregressive VaR (CAViaR) models perform inadequately, though an extension to a particular CAViaR model is shown to outperform the others.
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Cites
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Cited by
Robust performance hypothesis testing with the Sharpe ratio
Ledoit, O., & Wolf, M. (2008) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2008.03.002 · Cited by 948
Acebes, F., González-Varona, J. M., López-Paredes, A., & Pajares, J. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03180-5 · Cited by 74
Metadata
- Authors
- Kuester, K.
- Venue
- Journal of Financial Econometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-08-19
- Volume
- 4
- Issue
- 1
- Pages
- 53-89
- ISSN
- 1479-8409 (ISSN-L)
- 1479-8417
- OpenAlex
- W1989899182
- Cited by
- 632
- References
- 37
- FWCI
- 27.83
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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