market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of … to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an … the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection …

provided between frequentist and Bayesian estimation. No significant difference is found between the qualities of the forecasts …

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the … microstructure noise has an adverse effect on both spot variance estimation and jump detection. In our approach we can analyze high …

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence … of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component … of volatility in finance for portfolio allocation, derivative pricing and risk management. The method has a two …

This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying … sensitive to start value. Hence, two-stage QML has been suggested. In empirical estimation on two stock transaction data for …

We compare more than 1000 different volatility models in terms of their fit to the historical ISE-100 Index data and … for modeling the ISE-100 return volatility. The t-distribution seems to characterize the distribution of the heavy tailed … model to the historical ISE-100 return data indicates that the return volatility reacts to bad news 24% more than they react …

We establish a feasible central limit theorem with convergence rate $n^{1/8}$ for the estimation of the {integrated … volatility of volatility} (VoV) based on noisy high-frequency data with jumps. This is the first inference theory ever built for … VoV estimation under such a general setup. The central limit theorem is applied to provide interval estimates of the VoV …