structure to commodity futures prices, and develop new algorithms for estimation of such models using unbalanced data sets in …

the world economy. We analyze the impact of the advent of fracking on the volatility of oil prices. Our model predicts a … large decline in this volatility …

growth rate and volatility of commodity spot prices. This view gained credence because in the 2000s trading volume increased …

We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the … specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use … data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility …

Many ways exist to measure and model financial asset volatility. In principle, as the frequency of the data increases …, the quality of forecasts should improve. Yet, there is no consensus about a true' or best' measure of volatility. In this … paper we propose to jointly consider absolute daily returns, daily high-low range and daily realized volatility to develop a …

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 … variation, we formally develop the links between the conditional covariancematrix and the concept of realized volatility. Next …

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 …