Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in …

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this … paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility …, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model …

One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH (or EGARCH …) specification. In addition to asymmetry, which captures the different effects on conditional volatility of positive and negative … subsequent shocks to volatility. However, there are as yet no statistical properties available for the (quasi-) maximum …

Of the two most widely estimated univariate asymmetric conditional volatility models, the exponential GARCH (or EGARCH …) specification can capture asymmetry, which refers to the different effects on conditional volatility of positive and negative … shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator (QMLE) of the EGARCH …

We propose a novel multivariate GARCH model that incorporates realized measures for the variance matrix of returns. The … the variance matrix. Monte Carlo evidence for parameter estimation based on different small sample sizes is provided. We …

Of the two most widely estimated univariate asymmetric conditional volatility models, the exponential GARCH (or EGARCH …) specification can capture asymmetry, which refers to the different effects on conditional volatility of positive and negative … shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator (QMLE) of the EGARCH …

. They are applicable to the complete class of observation driven models and are valid for a wide range of estimation … that is embedded in the time-varying parameter path. We illustrate our findings in a volatility analysis for monthly …

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean …(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable …) models, known as the ARCH in Mean (ARCH-M)model. The estimation of ARCH models isrelatively easy compared with that of the …