Özet:

LITERATURE In the past few years, considerable uncertainty and volatility have been observed in the emerging and mature financial markets worldwide. Financial analysts and investors are concerned about the fluctuating returns of their investments due to the market risk and variation in the market price speculation as well as the unstable business performance. Quantitative models are used in financial econometrics to decipher the investor’s attitude towards the risks and returns as well towards the volatility. In this study, the volatility model of BIST Banking index is estimated based on the daily closing data of the index. There are only a few studies in the literature investigating volatility modelling based on the banking index, such as Köseoğlu (2010), Karahanoğlu and Ercan (2015), Koy (2016), Kula and Baykut (2017). So, this situation makes this study important because of its contribution to the literature.   DESIGN AND METHOD   In the study, firstly, the most preferred unit root test (Augmented Dickey-Fuller unit root test) in the literature, was used for the static analysis of the series. Then, ARCH, GARCH, EGARCH and TGARCH models were used in the volatility modelling of the BIST Banking index. In ARCH and GARCH models, it is assumed that the effect of a positive shock and a negative shock on conditional variance is the same. So these models are symmetrical. However, research shows that the effects of positive and negative news on conditional variance in financial markets are not the same. Therefore, it was aimed to obtain more detailed results by adding asymmetrical EGARCH and TGARCH models to the study. For this purpose, 2514 log data is taken from the database of the Republic of Turkey Central Bank’s Electronic Data Delivery System between 04/01/ 2010 and 31/ 12/2019.   FINDINGS AND DISCUSSION   Findings show us that the stationary of the price series was investigated with the help of the Augmented Dickey-Fuller unit root test that it was a first-degreed stationary series. Then, it was determined that the best mean equation model is ARMA (2,2) among the autoregressive models. Also, it was observed that there was an ARCH effect in the error terms of the mean equation, and it was tested that with which conditional heteroscedasticity model or models, the series of BIST Banking index can be explained. According to the test results obtained, it was determined that the model that gives the best results in estimating the volatility modelling of the BIST Banking series is TGARCH (0,1,1) compared to the information criteria and EGARCH (1,1,1) compared to the forecasting performance.   CONCLUSION, RECOMMENDATION AND LIMITATIONS   When we look at the banking index on volatility modelling studies conducted in Turkey, it seems to be quite limited. While studies in the literature examine volatility modelling, they generally focused on the BIST 100 index. Nevertheless, when our study is compared with the limited number of similar studies examined in the literature, it is seen that it gives similar results with Karahanoğlu and Ercan (2015). The results of the study are the basis for some future work and should be approached with caution. More advanced and different results can be obtained by using different data sets and methods. For example, in this study, the data set was created to cover the period after the crisis in order to remove the effects of the 2008 global economic crisis. In future studies to include the crisis period (especially may include the banking crisis in 2001, Turkey) can expand analysis results.

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