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 …

Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of … leads to gains in forecasting accuracy for some time series. …

forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of volatility … leads to gains in forecasting accuracy for some time series. …

forecasting volatility. We outline the genesis of this approach from similar models of turbulent flows in statistical physics and …

-t). Second, we perform a comprehensive panel forecasting analysis of the MSM models as well as other competing volatility models …

that estimated parameters can be used for forecasting the evolution of the turbulent flow. We compare forecasting results … by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and …

-t). Second, we perform a comprehensive panel forecasting analysis of the MSM models as well as other competing volatility models … models. -- Multiplicative volatility models ; long memory ; Student-t innovations ; international volatility forecasting …

for realized volatility (RV). We estimate the RV-MSM model via Generalized Method of Moments and perform forecasting by … memory ; international volatility forecasting …

that estimated parameters can be used for forecasting the evolution of the turbulent flow. We compare forecasting results … by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and … foreign exchange markets. -- Random Lognormal cascades ; GMM estimation ; best linear forecasting ; volatility of financial …