on some high frequency basis has spurred the research in the field of volatility modeling and forecasting into new … directions. First, the realized variance is a much better estimate of the latent volatility than the sum of the weighted daily … squared returns. As such it is better suited for comparing the out-of-sample performances of competing volatility models …

This paper looks at the interplay of volatility and liquidity on the Euronext trading platform during the December 2 …-traded stocks on Euronext, we study the ex-ante liquidity vs volatility and ex-post liquidity vs volatility relationships to … ascertain if the high volatility led to decreases in liquidity and large trading costs. We show that the provision of liquidity …

Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of … volatility components. From a practical point of view, ML also becomes computationally unfeasible for large numbers of components … forecasts which in principle is applicable for any continuous distribution with any number of volatility components. Monte Carlo …

forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of volatility … which in principle is applicable for any continuous distribution with any number of volatility components. Monte Carlo … linear compared to optimal forecasts is small. Extending the number of volatility components beyond what is feasible with MLE …

examines how well exchange rate volatility explains movements in stock market returns. The model-based predictions are …

making up the Dow Jones Industrial Index, I calculate intraday upside and downside volatility measures, following Becker et … document that for all the stocks in the sample, mean daily returns following the days when a stock's upside volatility measure … was higher or equal to its downside volatility measure are higher than following the days when the opposite relationship …