Maximum likelihood estimation of discretely observed diffusion processes is mostly hampered by the lack of a closed … for the estimation of a joint system of short-run and medium run investor sentiment and asset price dynamics using German …

apply generalized method of moments (GMM) estimation. We find that we can get relatively accurate parameter estimates with … form the best linear forecasts for future volatility we find that the behavioral model generates sensible forecasts that …

We examine the performance of volatility models that incorporate features such as long (short) memory, regime …-t). Second, we perform a comprehensive panel forecasting analysis of the MSM models as well as other competing volatility models … (GMM) estimation are both suitable for MSM-t models, (ii) empirical panel forecasts of MSM-t models show an improvement …

foreign exchange markets. -- Random Lognormal cascades ; GMM estimation ; best linear forecasting ; volatility of financial … Generalized Method of Moments (GMM) estimation procedure to cope with the documented difficulties of previous methodologies. We … by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and …

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 …

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 …

financial economics. Closed-form solutions provide for the possibility of exact maximum likelihood estimation for discretely …

forecasting financial volatility. We use the auto-covariances of log increments of the multi-fractal process in order to estimate … ?scaling? approach. Our empirical estimates are used in out-of-sample forecasting of volatility for a number of important …

-switching multi-fractal model in Calvet and Fisher (2001) which allows for estimation of its parameters via maximum likelihood and … 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 …

-switching multifractal model in Calvet and Fisher (2001) which allows for estimation of its parameters via maximum likelihood and Bayesian … 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 …