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Forecasting multidimensional tail risk at short and long horizons

Arnold Polanski () and Evarist Stoja ()
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Arnold Polanski: University of East Anglia
Evarist Stoja: School of Economics, Finance and Management, University of Bristol

No 660, Bank of England working papers from Bank of England

Abstract: 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.

Keywords: Multidimensional risk; multidimensional Value at Risk; two-factor decomposition; long-horizon forecasting (search for similar items in EconPapers)
JEL-codes: C52 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban, nep-for, nep-ore and nep-rmg
Date: 2017-06-12
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