Risk Analysis of Gold Prices in Pakistan Using Extreme Value Theory

Ghulam Raza Khan,Zahid Iqbal,Maqsood Ahmad,Osama Alamri,Alanazi Talal Abdulrahman,Ishfaq Ahmad

doi:10.1155/2021/2704142

Abstract

Extreme value theory (EVT) is useful for modeling the impact of crashes or situations of extreme stress on investor portfolios. EVT is mostly utilized in financial modeling, risk management, insurance, and hydrology. The price of gold fluctuates considerably over time, and this introduces a risk on its own. The goal of this study is to analyze the risk of gold investment by applying the EVT to historical daily data for extreme daily losses and gains in the price of gold. We used daily gold prices in the Pakistan Bullion Market from August 1, 2011 to July 30, 2021. This paper covers two methods such as Block Maxima (BM) and Peak Over Threshold (POT) modeling. The risk measures which are adopted in this paper are Value at Risk (VaR) and Expected Shortfall (ES). The point and interval estimates of VaR and ES are obtained by fitting the Generalized Pareto (GPA) distribution. Moreover, in this paper, return-level forecasting is also included for the next 5 and 10 years by analyzing the Generalized Extreme Value (GEV) distribution.

Highlights

Gold is a familiar valuable metal for investment as compared to silver, platinum, and palladium
It is a nonparametric test that can be used in the emplacement of an unpaired t-test and in the extreme type of data that present variability that damaged the property of homogeneity
Monotonic and nonmonotonic trends, we examined whether the sample data are stationary with respect to monotonic nonlinear or linear trends. ey help us in nonparametric tests based on Spearman’s rank correlation coefficient. e British psychologist Charles Spearman from 1863 to 1945 performed a nonparametric estimate of the statistical dependence between two random variables

Summary

Introduction

Gold is a familiar valuable metal for investment as compared to silver, platinum, and palladium. E most widely useful application of the block maxima approaches is estimating the return levels based on the Generalized Extreme Value (GEV) distribution.

Results

Conclusion