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 …

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 … autocorrelation of volatility innovations acts as an indicator of the importance of the squared-return for volatility updating. When …

The primary purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility … spillovers between international crude oil and associated financial markets. The paper investigates co-volatility spillovers … (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-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 … Stochastic Volatility (SV)model. However, efficient Monte Carlo simulationmethods for SV models have been developed to overcome …

improved volatility measurements but has also inspired research into their potential value as an information source for … volatility forecasting. In this paper we explore the forecasting value of historical volatility (extracted from daily return … series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of …

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 …

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 … conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …

dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … volatility forecasting of stock returns and exchange rates. …