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Forecasting multidimensional tail risk at short and long horizons

Arnold Polanski and Evarist Stoja

International Journal of Forecasting, 2017, vol. 33, issue 4, 958-969

Abstract: We define the Multidimensional Value at Risk (MVaR) as a natural generalization of VaR. This generalization makes a number of important applications possible. For example, many techniques developed for VaR can be applied to MVaR directly. 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 MVaR 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)
Date: 2017
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Handle: RePEc:eee:intfor:v:33:y:2017:i:4:p:958-969