Paper Title
Forecasting Bitcoin Volatility with Asymmetric -WARIMAX-GARCH Models

Abstract - As an alternative to traditional currencies, crypto currencies have started to take place in the financial markets both as an investment and payment method. Bitcoin is not dependent on the central authority and its price effectivness factors affecting supply and demand with high volatility. The volatility and endonegenious factor is taken with one of the methods frequently used in the literature in the application part of the study and The asymmetric Generalized Auto-Regressive Conditional Heteroscedasticity models such as GARCH, ARCH-M, EGARCH and GJR-GARCH used to determine the asymmetric volatility which is frequently used in the literature in the application part of the study. This model is taken with Wavelet Auto-Regressive Integrated Moving Average with Exogenous Variable and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) This model is exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. In the application part of this study the price data of Bitcoin closing prices have been taken between 2019 and 2020 and the Etherium(ETH). The out of sample bitcoin forecasting is made for 2021 for 50 days. Keywords - Bitcoin, WARIMAX -Asymmetric-GARCH Models, Out of sample Volatility