The estimation of the volatility with high-frequency data is plagued by the presence of microstructure noise, which leads to biased measures. Alternative estimators have been developed and tested either on specific structures of the noise or by the speed of convergence to their asymptotic...

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that...

We study financial volatility during the Global Financial Crisis and use the largest volatility shocks to identify major events during the crisis. Our analysis makes extensive use of high-frequency financial data to model volatility and to determine the timing within the day when the largest...

Corporate earnings announcements unpack large bundles of information that should, if markets are efficient, almost surely trigger jumps in stock prices immediately after the news release. Testing this implication is difficult in practice because most earnings announcements occur in the...

This paper is enhancing the four-factor fixed income asset pricing model initially developed by Elton et al. (1995). Progress into fixed income asset pricing has been slow, and there is still no consensus of which combinations of bond indexes are most suitable for explaining the returns of fixed...