Value-at-Risk Estimation Method Based on Normal Distribution, Logistics Distribution and Historical Simulation

Dwi Susanti,Maria Jatu Verrany,Sukono Sukono

doi:10.47194/orics.v1i1.19

Abstract

This paper discusses the risk analysis of single stock and portfolio returns. The stock data analyzed are BNI, BRI shares and portfolio. After obtaining a stock return, value at risk (VaR) will be estimated using the normal distribution approach, logistic distribution, and historical simulation. From the VaR results, a backtest is then conducted to test the validity of the model and the backtest results for BNI and the portfolio produce a smaller QPS on the historical simulation method compared to the normal distribution and logistics distribution approaches. This shows that BNI VaR and VaR portfolios with the historical simulation method are more consistent than other methods. While the backtest results for BRI produced the smallest QPS on the normal distribution approach compared to the logistical distribution and historical simulation approaches. This shows that the VaR BRI using the normal distribution approach is more consistent than the other methods.

Highlights

Every company, especially those engaged in finance or financial institutions are very vulnerable to risk, and financial activity is very unstable
Quantitative risk measurement can use Value at Risk which has been widely used in finance and has become a general standard in risk calculation because it can be applied to all types of risk (Marrison, 2002)
value at risk (VaR) and Backtest Analysis for a Single Investment Based on the results of the backtest that has been obtained, the greater the confidence coefficient will result in a smaller QPS

Summary

Introduction

Especially those engaged in finance or financial institutions are very vulnerable to risk, and financial activity is very unstable. If there is a crisis that destroys the economic sector so that it can result in losses, one of the losers is an investor. A risk measurement method is needed that is able to translate these risks in a quantitative form so that they can be widely used and serve as an early warning in the financial sector that can be dealt with immediately (Dowd, 2002). Quantitative risk measurement can use Value at Risk (commonly abbreviated as VaR) which has been widely used in finance and has become a general standard in risk calculation because it can be applied to all types of risk (Marrison, 2002).

Objectives

Methods

Results