Modeling volatility dynamics in financial time series: Ananalysis of econometric models and machine learningmethods

In this thesis we present an analysis of different volatility dynamics in financial and cryptocurrency markets. We focus on the Borsa Istanbul stock exchange index BIST 100 (XU100), NASDAQ index, and Bitcoin/USD exchange rate (BTCUSD). Our aim is to understand the risk profiles of the data, forecast future risk using historical data, and statistically analyze structural characteristics of the volatility in the markets. In our analysis we used logarithmic returns of each financial asset we considered. Our first step of the investigation was to determine the best fitting ARIMA models, and analyze the parameters of these models using tools such as ADF-test, ACF- and PACF-plots, QQ-plots, and Kolmogorov-Smirnov test. Our working hypothesis is that if a model successfully explains the behaviour of an asset then the residual signal must be close to white noise. However, we found significant autocorrelations in the residuals which indicates that the ARIMA models we used did not fully capture all underlying patterns. In the next step, we used GARCH models to analyze the dynamics of the volatility inherent in each asset. While the risk profiles of the datasets varied, the structure coefficients of the models were consistent across all datasets. We finish the thesis by outlining how one might expand our study using endogeneous and exegeneous data, and different machine learning methods.