forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically … ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility …

forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically … ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility …

topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility … (RV) models to improve their forecast performance during turbulent periods. The results of the empirical experimentation … indicate that the employment of PH information allows nonlinear and neural network models to better forecast RV during a …

In the era of diminishing power from US dollar and increasing competition among world currencies, Bitcoin, as a … completely new concept as a medium of exchange, has received increasing attentions over the world. Nowadays, Bitcoin also becomes … GARCH framework in fitting the daily Bitcoin exchange rate returns. Our results indicate the heavy-tailed distribution has …

Regular or automated processes require reliable software applications that provide accurate volatility and Value … R functions for automated forecasting systems is discussed. With the Markov-switching GARCH function constructed for …

returns for equity returns for all the countries across various forecast horizons and the length of out-of-sample periods …