Forecasting multidimensional tail risk at short and long horizons

Arnold Polanski, Evarist Stoja

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
13 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)958–969
Number of pages12
JournalInternational Journal of Forecasting
Volume33
Issue number4
Early online date31 Jul 2017
DOIs
Publication statusPublished - Oct 2017

Keywords

  • Multidimensional Risk
  • Multidimensional Value at Risk
  • Long Horizon Forecasting
  • Two-Factor Decomposition

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