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

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, ۲۰۲۱) and (González-Sánchez & Nave Pineda (۲۰۲۳) 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 ۲ Iranian Journal of Finance, ۲۰۲۶, Vol. ۱۰, No. ۲ (Ebadinezhad, N.) Distribution (GPD). The dataset consists of daily returns for four asset classes in the Iranian market over the period ۲۰۱۵-۲۰۲۵: ۱۸-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.

کلیدواژه ها:

Extreme Value Theory (EVT) ، Value-at-Risk (VaR) ، GUB method ، Hill estimator ، t-student copula