forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility … and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic …

We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study … although the conditional correlation between the mean and volatility is negative, the unconditional correlation is positive due …

Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good … forecasts of future volatility are critical for the implementation and evaluation of asset pricing theories. In response to this …, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility at the daily …

econometric issues are addressed including estimation of the number of dynamic factors and tests for the factor restrictions …

inflation process is well described by an unobserved component trend-cycle model with stochastic volatility or, equivalently, an …

factor-augmented vector autoregression estimated on a large data set. On the basis of this estimation, we establish eight …