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We used neural-network based modelling to generalize the linear econometric return models and compare their out-of-sample predictive ability in terms of different performance measures under three density specifications.(...)
Persistent link: https://www.econbiz.de/10005844728
on some high frequency basis has spurred the research in the field of volatility modeling and forecasting into new …
Persistent link: https://www.econbiz.de/10010263102
This paper elaborates on the link between financial market volatility and real economic activity. Using monthly data for Germany from 1968 to 1998, we specify GARCH models to capture the variability of stock market prices, of the real exchange rate, and of a long-term and of a short-term rate of...
Persistent link: https://www.econbiz.de/10010275423
shown to be specific for commodity and market. A forecasting comparison on the basis of the identified models suggests that …
Persistent link: https://www.econbiz.de/10010291928
in forecasting from using bivariate models remained small otherwise. …
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-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …
Persistent link: https://www.econbiz.de/10010294979
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 …
Persistent link: https://www.econbiz.de/10010295056
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. …
Persistent link: https://www.econbiz.de/10010295106