Forecasting realised volatility using ARFIMA and HAR models

Har Modeling for Realized Volatility Forecasting

Bayesian Tail Risk Forecasting using Realised Volatility DCC-Copula-GARCH Models

A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio Value at Risk and Conditional Value at Risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using tradi...

Realised power variation and stochastic volatility models

Limit distribution results on realised power variation, that is sums of absolute powers of increments of a process, are derived for certain types of semimartingale with continuous local martingale component, in particular for a class of flexible stochastic volatility models. The theory covers, for example, the cases of realised volatility and realised absolute variation. Such results should be ...

Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model

and Applied Analysis 3 daily return volatility, while most literatures on RV at present (such as Wang et al. [13] and Corsi [9]) have not taken it into consideration. According to researches of Martens [20] and Koopman et al. [6], considering the overnight return variance, we adjust RV as

Econometric analysis of realised volatility and its use in estimating stochastic volatility models

The availability of intra-day data on the prices of speculative assets means that we can use quadratic variation like measures of activity in financial markets, called realised volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realised volatility erro...