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Static and dynamic models for multivariate distribution forecasts: proper scoring rule tests of factor-quantile vs. multivariate GARCH models

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Version 2 2023-07-04, 14:15
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journal contribution
posted on 2023-07-04, 14:15 authored by Carol AlexanderCarol Alexander, Yang Han, Xiaochun MengXiaochun Meng
A plethora of static and dynamic models exist to forecast Value-at-Risk and other quantile-related metrics used in financial risk management. Industry practice tends to favour simpler, static models such as historical simulation or its variants whereas most academic research centres on dynamic models in the GARCH family. While numerous studies examine the accuracy of multivariate models for forecasting risk metrics, there is little research on accurately predicting the entire multivariate distribution. Yet this is an essential element of asset pricing or portfolio optimization problems having non-analytic solutions. We approach this highly complex problem using a variety of proper multivariate scoring rules to evaluate forecasts of eight-dimensional multivariate distributions: of exchange rates, interest rates and commodity futures. This way we test the performance of static models, viz. empirical distribution functions and a new factor-quantile model, with commonly used dynamic models in the asymmetric multivariate GARCH class.

History

Publication status

  • Published

File Version

  • Published version

Journal

International Journal of Forecasting

ISSN

0169-2070

Publisher

Elsevier

Issue

3

Volume

39

Page range

1078-1096

Department affiliated with

  • Accounting and Finance Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2022-04-13

First Open Access (FOA) Date

2022-08-12

First Compliant Deposit (FCD) Date

2022-04-12

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