NDLI: Forecasting Multidimensional Tail Risk at Short and Long Horizons

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Forecasting Multidimensional Tail Risk at Short and Long Horizons

Content Provider	Scilit
Author	Polanski, Arnold Stoja, Evarist
Copyright Year	2017
Description	Journal: SSRN Electronic Journal Multidimensional Value at Risk (MVaR) generalises VaR in a natural way as the intersection of univariate VaRs. We reduce the dimensionality of MVaRs which allows for adapting the techniques and applications developed for VaR to MVaR. As an illustration, we employ VaR forecasting and evaluation techniques. One of our forecasting models builds on the progress made in the volatility literature and decomposes multidimensional tail events into long-term trend and short-term cycle components. We compute short and long-term MVaR forecasts for several multidimensional time series and discuss their (un)conditional accuracy.
Related Links	https://ueaeprints.uea.ac.uk/id/eprint/63443/1/Forecasting_Multidimensional_Tail_Events.pdf https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2984767
ISSN	10914358
e-ISSN	15565068
DOI	10.2139/ssrn.2984767
Journal	SSRN Electronic Journal
Language	English
Publisher	Elsevier BV
Publisher Date	2017-06-09
Access Restriction	Open
Subject Keyword	Journal: SSRN Electronic Journal Statistics and Probability Multidimensional Risk Multidimensional Value At Risk Two-factor Decomposition Lonh-horizon Forecasting
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Psychiatry and Mental Health

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