Forecasting Multidimensional Tail Risk at Short and Long Horizons

Posted: 30 Apr 2019

See all articles by Arnold Polanski

Arnold Polanski

University of East Anglia

Evarist Stoja

University of Bristol

Multiple version iconThere are 2 versions of this paper

Date Written: October 27, 2017

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

JEL Classification: C52, C53

Suggested Citation

Polanski, Arnold and Stoja, Evarist, Forecasting Multidimensional Tail Risk at Short and Long Horizons (October 27, 2017). International Journal of Forecasting, Vol. 33, No. 4, 2017, Available at SSRN: https://ssrn.com/abstract=3379019

Arnold Polanski

University of East Anglia ( email )

Norwich, Norfolk NR4 7TJ
United Kingdom
44 (0)1603 59 7166 (Phone)

HOME PAGE: http://https://www.uea.ac.uk/eco/people/All+People/Academic/Arnold+Polanski

Evarist Stoja (Contact Author)

University of Bristol ( email )

School of Accounting and Finance
8 Woodland Road
Bristol, BS8 1TN
United Kingdom

HOME PAGE: http://sites.google.com/view/evarist-stoja/

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