The Study of Value-At-Risk Calculation and Back-testing Using the ARMA-GARCH Model Based on Stock Returns: An Overview

Abstract

Stocks are investment instruments that provide returns but tend to be risky. The most important component of investing is volatility, where volatility is identical to the standard conditional deviation of stock price return. The important thing in investing in addition to return is a risk. Value-at-Risk (VaR) is a statistical method of estimating maximum losses. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. The goal of the study was to estimate the Autoregressive Moving Average-Generalized Conditional Heteroscedastic (ARMA-GARCH) model to determine Value-at-Risk and back-testing. ARMA is a combination of AR and MA models, while GARCH is a time series model with symmetrical properties. The method in this study is systematic browsing of libraries. Systematic library tracing is an attempt to identify, evaluate, and interpret all research relevant to a particular phenomenon.