Predicting Bitcoin’s volatilityPristovnik, Jan (Avtor) Todorovski, Ljupčo (Mentor) Basrak, Bojan (Komentor) volatilityforecastingtime seriescryptocurrencytime-series analysismachine learningrecurrent neural networksLSTMThe master's thesis addresses analyzing and modeling the volatility of Bitcoin, the cryptocurrency with the largest marketcap. Volatility is a statistical measure of the dispersion of returns. We approximated it with realized volatility calculated on intra-daily log returns. We defined two baseline models based on a constant value and martingale property and tried to outperform them with both econometric and machine learning models. We used three error functions relative to our baseline models: MAE, MAPE, and RMSE. The best-performing econometric model is the HAR model. The best-performing machine learning model, which also outperforms the HAR model, is the LSTM recurrent neural network.20222022-12-29 08:15:02Magistrsko delo/naloga143592sl