forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation … degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which … distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized …

volatility of carbon emissions, it is not surprising that crude oil and coal have recently become a very important research topic … causality and volatility spillovers in spot and futures prices of carbon emissions, crude oil, and coal. A likelihood ratio test … is developed to test the multivariate conditional volatility Diagonal BEKK model, which has valid regularity conditions …

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 …

for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … the intraday volatility measure. For forecasting horizons ranging from one day to one week the most accurate out … improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source …

empirical relevance of the accelerated GARCH updating is illustrated by forecasting daily volatility in return series of all … allows for a more flexible weighting of financial squared-returns for the filtering of volatility. The parameter for the …-return replaced by the product of the volatility innovation and its lagged value. This local estimate of the first order …

volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding …% and at least 78%). The results on the GMV portfolios show that realized covariance models exhibit lower ex-post volatility …

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 …