Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management … ; realized betas ; volatility modeling … the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns.In turn, this so …

Heteroscedasticity) volatility dynamics. DCS models are robust to extreme observations, whereas standard financial time series models are … observations, stochastic seasonality with dynamic amplitude, and volatility dynamics. These seasonality dynamics of the GTQ/USD are …

market information can be used to improve realized volatility forecasts in a large cross-section of international equity … markets. We use volatility data for the U.S. and 17 foreign equity markets from the Oxford Man Institute's realized library … and augment for each foreign equity market our benchmark HAR model with U.S. equity market volatility information. We show …

aggregate U.S. stock market on: 1) the volatility predictions of asymmetric time series models, 2) implied volatility, and 3 …) realized volatility. Both asymmetric time series models and implied volatility predict an increase in volatility following … large negative surprise returns and ex post realized volatility normally rises as predicted. However, while asymmetric time …

In this paper, we review econometric methodology that is used to test for jumps and to decompose realized volatility …

using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock … independent shocks on volatility through time, while avoiding typical orthogonalization and ordering problems. Volatility impulse … response functions (VIRF) provide information about the impact of independent shocks on volatility. HHś VIRF extends a …