MODELING AND FORECASTING VOLATILITY OF STOCK MARKET USING FAMILY OF GARCH MODELS: EVIDENCE FROM CPEC LINKED COUNTRIES

Abstract

For economists and investors, it is necessary to understand the random and nonlinear pattern of the stock market volatility. High volatility directly affects the financial market that leads to unpredictability. China–Pakistan Economic Corridor attracts economists and investors worldwide. Therefore, predicting the volatility of the stock markets related to CPEC is important. In this study we consider the most important stock markets lying on the route of CPEC, namely KSE 100 (Pakistan), SSE 100 (China), TADAWUL (Kingdom of Saudi Arabia), KASE (Kazakhstan), KLSE (Malaysia), BIST (Turkey), MOEX (Russia), FTSE (United Kingdom) and CAC40 (France). The daily returns of stock market indices consist of 1706 observations from December 2014 to July 2021. After the confirmation from the ARCH effect test, family GARCH models are employed, among them, based on AIC and BIC criteria, GARCH (1,1), EGARCH (1,1), and GARCH-M (1,1) are found suitable to forecast the volatility. The empirical study also suggests that the out-of-sample forecast GARCH-M (1,1) model is more appropriate as it has a minimum value of MAE, MSE, RMSE, MAPE, TheilU1, and Theil U2 among all the studied GARCH models. Furthermore, it is also found that the KSE-100 and SSE-100 have moderate and slow market average returns even though both stock markets are found to be the least risk-returns markets.