BACK-TESTING APPROACHES FOR VALIDATING VAR MODELS

Authors

  • Kirit Vaniya Research scholar, Department of Mathematics, Gujarat University, India
  • Ravi Gor Department of Mathematics, Gujarat University, India

DOI:

https://doi.org/10.29121/ijoest.v6.i6.2022.408

Keywords:

Value At Risk (Var), Back-Testing, Variance Co-Variance Method, Historical Simulation, Monte Carlo Simulation, Cubic Polynomial Regression Method, Basel Traffic Light Zone Test, Kupiec Pof-Test, Kupiec Tuff-Test, Haas’ Mixed-Kupiec Test

Abstract

Value at risk (VaR) is one of the important market risk measures. It measures the possible potential loss on given investment in terms of value, with certain probability for certain time horizon. In this paper, our aim is to discuss different back-testing approaches to validate VaR models, and also test it the real market data. We back tested VaR of Nifty 50 index obtained by Variance Co-variance method, Historical simulation method, Monte-Carlo simulation, and cubic polynomial regression method. We have used Total exceptions by binary back-testing over entire population. we have also used Basel Traffic Light Zone Test, Kupiec POF-test, Kupiec TUFF-test, and Haas’ Mixed-Kupiec test and analyzed the above methods.

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Published

2022-11-23

How to Cite

Vaniya, K., & Gor, R. (2022). BACK-TESTING APPROACHES FOR VALIDATING VAR MODELS. International Journal of Engineering Science Technologies, 6(6), 9–18. https://doi.org/10.29121/ijoest.v6.i6.2022.408

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