Volatility Forecasting An Empirical Study on Bitcoin Using Garch and Stochastic Volatility models

Detta är en Master-uppsats från Lunds universitet/Nationalekonomiska institutionen

Sammanfattning: Cryptocurrencies are on the rise, with new financial assets, new frameworks need to be developed. This thesis sets out to the examine the GARCH(1,1), the bivariate-BEKK(1,1), and the Standard stochastic volatility model’s volatility forecasting performance on BTC/USD, where the bivariate model is estimated on both BTC/USD and ETH/USD closing price data. Furthermore, three loss functions are used to evaluate the forecast accuracy for each model. The functions are estimated using realized volatility based on BTC/USD data on a minute per minute basis. The result indicates that the GARCH(1,1) is the model that performs best regarding forecast accuracy. All three loss functions rank the models accordingly; first the GARCH(1,1), second the bivariate- BEKK(1,1), and finally the Stochastic volatility model.

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