In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto exchange—Binance. Given the unique features of the crypto asset market, we find that conventional regression models exhibit strong model specification uncertainty. To circumvent this issue, we suggest using least squares model-averaging methods to model and forecast Bitcoin ... Binance La unchpad, the ... speculative and technical determinants as well as the 2016 events- which drove the value of Bitcoin in times of economic and geopolitical chaos. We use a Bayesian ... Regression (NNR), Bayesian Linear Regression (BLR), and Boosted Decision Tree Regression (BDTR) algorithms. International Journal of Engineering & Technology Shah et al. utilized a “latent source model” Bayesian regression created by Chen et al. , which is designed to leverage binary classification to predict Bitcoin price variations. Using a support vector machine algorithm, Georgoula et al.  examined the relationship between Bitcoin price and determinants including economic variables, technological factors, and sentimenFt. This post introduces and discusses a Bayesian model for estimating the date that the distribution of new COVID-19 cases in a particular country changes. The model coefficients are interpretable and can be used for future analysis on the effectiveness of social distancing measures adopted by different countries. In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto exchange—Binance. Given the unique features of the crypto asset market, we find that conventional regression models exhibit strong model specification uncertainty. To circumvent this issue, we suggest using least squares model-averaging methods to model and forecast Bitcoin ...
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