Abstract:

With the everyday development of capital markets, the movement of globalization, the increase in risks and the increasing uncertainty that the markets become more complex, the increasing volatility movements have made the analysis of the structures of the exchanges even more important. The lifting effect in the financial series, asymmetry, etc. The increased volatility due to its characteristics can prevent the effectiveness of stock prices in the exchanges. It is especially important that the concept of volatility is revealed in the stock exchanges, especially in developing countries with high levels of external transparency and vulnerability. With the advances in information communication technologies, the possibility of 24-hour transactions on the markets has been provided. The prediction of the volatility variable, which has recently become an important variable in global investors’ investment decisions, has become even more important in developing countries, especially because they are more vulnerable than developed countries. The prediction of this change makes it even more important especially for decision makers who consider investing in the relevant company or becoming a partner. Since traditional models remain insufficient to express the volatility variable, the ARCH, GARCH, EGARCH and TGARCH models, non-linear conditional variable models, have begun to be used. The study, which covers the period 2011-2017/3 of the BIST 100 Index and addresses the daily closing values, aims to reveal the return volatility of the BIST 100 Index with the ARCH, GARCH, EGARCH and TGARCH models, which is the highest level of explanation. The results of the study showed that the TGARCH model among the relevant models showed successful results in the discovery of the BIST 100 return volatility.

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