EVT-Based Value-at-Risk Estimation in Iranian financial markets Using the GUB Threshold Identification Method

Ebadinezhad, Narges; Dehghani Ahmadabad, Mohammadreza

doi:10.66224/ijf.2026.565753.1562

EVT-Based Value-at-Risk Estimation in Iranian financial markets Using the GUB Threshold Identification Method

Document Type : Original Article

Authors

Narges Ebadinezhad ¹

Mohammadreza Dehghani Ahmadabad ²

¹ Master’s Student in Financial Management, Faculty of Financial Sciences, Kharazmi University, Tehran, Iran.

² Assistant Prof., Faculty of Financial Sciences, Kharazmi University, Tehran, Iran.

10.66224/ijf.2026.565753.1562

Abstract

In classical risk management models, the assumption of normally distributed
returns leads to an underestimation of the true probability of extreme losses.
Extreme Value Theory (EVT), by focusing on tail behavior, provides an
effective framework for modeling heavy-tailed risks; however, determining the
threshold beyond which tail behavior begins remains one of its main
challenges. In this study, the Good–Usual–Bad (GUB) method introduced by
(González-Sánchez, 2021) and (González-Sánchez & Nave Pineda (2023) is
employed to separate returns into central and extreme components, enabling
the automatic identification of the tail threshold. Subsequently, Value-at-Risk
(VaR) is estimated using the Hill estimator and the Generalized Pareto
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Iranian Journal of Finance, 2026, Vol. 10, No. 2 (Ebadinezhad, N.)
Distribution (GPD). The dataset consists of daily returns for four asset classes
in the Iranian market over the period 2015-2025: 18-carat gold, the Tehran
Stock Exchange Overall Index (TEDPIX), the USD/IRR exchange rate, and
Bitcoin in Iranian Rial. Empirical results indicate that, at the univariate level,
the GUB method provides accurate estimates for heavy-tailed assets; however,
at the portfolio level, its sensitivity to dependence structures makes it
conservative and prone to overestimating risk. In contrast, dependence-based
models such as the t-student copula yield more reliable portfolio VaR
estimates, and hybrid approaches combining GUB with parametric models and
the modified GUB-based procedures proposed in this study produce more
balanced and efficient risk assessments.

Keywords

Extreme Value Theory (EVT)

Value-at-Risk (VaR)

GUB method

Hill estimator

t-student copula

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