Analysis of Istanbul Stock Market Returns Volatility with ARCH and GARCH Models

n today's world where globalization is intensely experienced, differences in risk perception, developments in capital markets, and the negativities faced in the markets due to uncertainty are very important when researching the structures of the stock markets, and therefore determining current volatilities. One of the biggest problems encountered is the inability to price stocks effectively. Therefore, estimating and modeling volatility becomes crucial. The diversity of the portfolio, created by international investors in the financial markets and the sustainability of their investment decisions, are closely related to the volatility variable. However, the fact that financial markets are more fragile in developing countries increases the importance of volatility. There are many different methods in the literature when estimating volatility. Due to the inadequacy of traditional time series models in estimating volatility, conditional heteroskedasticity models are used with ARCH and GARCH class models being frequently used. In this study, the series of daily opening values of the ISE100 Index covering from 02.01.2003 to 30.09.2022 was estimated using ARCH/GARCH models for volatility with the aim to determine which model has the higher explanatory power. According to the findings, the GARCH(1,1) model gave more meaningful results in explaining the ISE100 return volatility