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 …

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 from … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …

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 …

Linear GARCH(1,1) and threshold GARCH(1,1) processes are established as regularly varying, meaning their heavy tails … considered a stylized fact for many financial returns assumed to follow GARCH-type processes. The result in this note aids in … establishing the asymptotic properties of certain GARCH estimators proposed in the literature …

return and its volatility. Although this characteristic of stock returns is well acknowledged, most studies about it are … return horizons and usually do not specify the quantitative measure of it. This paper provides nonparametric estimation of a … stochastic volatility context and for high frequency data. The consistency and limit distribution of the estimators are derived …