Using Unconventional Parametric VaR Models to Evaluate Extreme Risk

Authors

  • Boris Kuzman Prof. dr https://orcid.org/0000-0002-8661-2993
  • Katica Radosavljević National Key Laboratory of Smart Farm Technologies and Systems, Harbin 150001, China.
  • Anton Puškarić National Key Laboratory of Smart Farm Technologies and Systems, Harbin 150001, China.

Keywords:

stock markets, extreme risk reduction, parametric VaR portfolio optimization

Abstract

Abstract:

This paper aims to mitigate the extreme risk of the German DAX index by constructing parametric multivariate VaR portfolios that combine the DAX with emerging market indices from East Asia, the MENA region, and Central and Eastern Europe. Extreme risk is evaluated using several parametric Value-at-Risk models, including the normal, logistic, hyperbolic secant, and Laplace distributions. Results show substantial variation across models: the normal VaR produces the lowest risk estimates, while the Laplace VaR yields the highest. Kupiec test results indicate poor performance of the normal VaR, whereas heavier-tailed distributions perform better, particularly in the MENA portfolio.

 

Published

2026-08-24

How to Cite

(1)
Kuzman, B.; Radosavljević, K.; Puškarić, A. Using Unconventional Parametric VaR Models to Evaluate Extreme Risk. IR 2026, 15.

Issue

Vol. 15 No. 1-2 (2026): International Review

Section

Articles

License

Copyright (c) 2026 Boris Kuzman, katica_r@iep.bg.ac.rs ; anton.puskaric@gmail.com

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