GARCH Model Identification for Stock Crises Events

Abstract

The stock market crash is a sudden dramatic decline of stock price due to uncertainty in the stock market. The stock prices are the influence of many factors, such as global trends, local trends, and economic conditions. The identification stock crisis is a challenging task for stock traders and investors. The goal of this paper is to forecast stock crisis events. The experiment is carried out in two steps. First is the least square (LS) method, and the least absolute deviation (LAD) is considered to identify a correlation between mean and median. Based on the correlation between mean and median, the GARCH (General autoregression conditional heteroskedasticity) model proposed to calculate the error distribution in stock returns. To identify the appropriate error distribution, we have varied the degree of t distribution parameters. In the second step, the volatility of stock prices is given as input to the GARCH model to forecast the future crisis events. To carry out the proposed experiment, we have considered Infosys and sbi stock. Experiment results reduce the error in predicting stock crises events. © 2020 The Authors. Published by Elsevier B.V.