Forecasting multidimensional tail risk at short and long horizons

Polanski, Arnold ORCID: and Stoja, Evarist (2017) Forecasting multidimensional tail risk at short and long horizons. International Journal of Forecasting, 33 (4). 958–969. ISSN 0169-2070

[thumbnail of Accepted manuscript]
PDF (Accepted manuscript) - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (755kB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: multidimensional risk,multidimensional value at risk,long horizon forecasting,two-factor decomposition
Faculty \ School: Faculty of Social Sciences > School of Economics
UEA Research Groups: Faculty of Social Sciences > Research Groups > Economic Theory
Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance
Depositing User: Pure Connector
Date Deposited: 10 May 2017 05:05
Last Modified: 29 Jan 2023 06:30
DOI: 10.1016/j.ijforecast.2017.05.005


Downloads per month over past year

Actions (login required)

View Item View Item