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This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only...
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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 …
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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
-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …
Persistent link: https://www.econbiz.de/10010295136
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. …
Persistent link: https://www.econbiz.de/10010295151