Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation. In order to simplify the … comprehension of these estimation methods, the main methods for estimating stochastic volatility are discussed, with focus on their …

This paper features an analysis of the cointegration relationships among agricultural commodity, ethanol and Cushing … cointegration and Markov-switching VECM and Impulse Response Analysis, confirms that these markets have significant linkages which … vary according to whether they are in low or high volatility regimes. …

methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …

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 …

in the daily retail price for gasoline (taxes excluded) for the period 1996-2004 taking care of volatility clustering by … estimating an EGARCH model. It turns out the volatility process is asymmetrical: an unexpected increase in the producer price has …

methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …

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 …