Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this … stochastic volatility models. The empirical analysis on stock returns on the US market shows that 1% and 5 % Value …

predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression … superior market timing ability and volatility timing ability, while a mean-variance investor would be willing to pay an annual …

of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting … errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are … of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility …

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 …

We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular … practically always improves upon the na?ve forecast provided by historical volatility. As a somewhat surprising result, we also …

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 …

We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular … practically always improves upon the na?ve forecast provided by historical volatility. As a somewhat surprising result, we also …

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 …