Volatility forecasting in stock markets: evidence from high frequency data of Istanbul Stock Exchange

Volatility, which is defined as a variation of price of a financial instrument over time, is important for investment decisions, asset pricing, portfolio allocation, risk management and overall economy. Hence, volatility forecasting is one of the most important areas, researchers focused on. Recently, with improvements in availability of high frequency data, researchers focused on high frequency based- volatility models in the literature.The main goal of this paper is to propose the best volatility forecasting model for Turkish Stock Markets. In this context, in the first chapter, we analyse data generating process. In the second chapter, we test the significance of jump component. In the third chapter, we present findings of volatility forecasting models in stock markets. The findings of this paper support the superiority of high frequency based volatility forecasting models over traditional GARCH models. MIDAS and HAR-RV-CJ models are found the best among high frequency based volatility forecasting models. Moreover, MIDAS model performs better in crisis period. The findings of paper are important for financial institutions, investors and policy makers.Keywords: Volatility, Realized Volatility, High Frequency Data, Price Jumps.