Estimating Conditional Value at Risk in Non-Cyclical  Sector Companies Using the Extreme Value Theory Approach

Andi Muhammad  Hakam; Andi Kresna  Jaya

doi:10.20956/j.v21i1.35849

Authors

Andi Muhammad Hakam Hasanuddin University
Andi Kresna Jaya Departemen Statistika, Universitas Hasanuddin

DOI:

https://doi.org/10.20956/j.v21i1.35849

Keywords:

L-Moment, Conditional Value at Risk, Generalized Extreme Value, Generalized Pareto Distirbution.

Abstract

Conditional Value at Risk (CVaR) is an estimate of the risk of loss that exceeds the Value at Risk (VaR) level. VaR is one of the most commonly used stock risk measurement methods to assess the risk of large investments. Extreme Value Theory (EVT) is a method used to analyze data that contains extreme values. The goal of EVT is to estimate the probability of an extreme event occurring by examining the tails of a distribution based on observed extreme values. There are two general distributions used in EVT, namely Generalized Extreme Value (GEV) and Generalized Pareto Distribution (GPD). This research aims to determine the estimated level of loss that investors may experience when investing in PT Hanjaya Mandala Sampoerna Tbk (HMSP) and PT Japfa Comfeed Indonesia Tbk (JPFA). The L-Moment method is applied to estimate the parameters in this distribution so that an explicit parameter form is obtained. Based on CVaR analysis using the Block Maxima (BM) approach, investors in HMSP and JPFA are estimated to experience losses of 20.0752% and 29.6537% respectively. Using the Peaks Over Threshold (POT) approach, the estimated losses are 0.966% and 1.548% for HMSP and JPFA, respectively. Based on CVaR calculations using both approaches, the POT approach with GPD provides a more accurate and reliable investment risk estimate than the BM approach with GEV distribution

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