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Range-based covariance estimation using high-frequency data : the realized co-range

Bannouh, Karim (contributor); Dijk, Dick van (contributor); … - 2008

Persistent link: https://www.econbiz.de/10003754160

Partial liklihood-based scoring rules for evaluating density forecasts in tails

Diks, Cees G. H. (contributor); … - 2008

Persistent link: https://www.econbiz.de/10003798183

Range-based covariance estimation using high-frequency data : the realized co-range

Bannouh, Karim; Dijk, Dick van; Martens, Martin - In: Journal of financial econometrics : official journal of … 7 (2009) 4, pp. 341-372

Persistent link: https://www.econbiz.de/10003907520

An alternative Bayesian approach to structural breaks in time series models

Hauwe, Sjoerd van den; Paap, Richard; Dijk, Dick van - 2011

Persistent link: https://www.econbiz.de/10008857052

Nonlinear forecasting with many predictors using kernel ridge regression

Exterkate, Peter; Groenen, Patrick J. F.; Heij, Christiaan - 2011

Persistent link: https://www.econbiz.de/10008809883

Nonlinear forecasting with many predictors using kernel ridge regression

Exterkate, Peter; Groenen, Patrick J. F.; Heij, Christiaan - 2013

Persistent link: https://www.econbiz.de/10009751849

Forecasting volatility with the realized range in the presence of noise and non-trading

Bannouh, Karim; Martens, Martin; Dijk, Dick van - 2012

Persistent link: https://www.econbiz.de/10009664660

Comparing the accuracy of copula-based multivariate density forecasts in selected regions of support

Diks, Cees G. H.; Panchenko, Valentyn; Sokolinskiy, Oleg; … - 2013

Persistent link: https://www.econbiz.de/10009756306

Comparing the accuracy of multivariate density forecasts in selected regions of the copula support

Diks, Cees G. H.; Panchenko, Valentyn; Sokolinskiy, Oleg; … - In: Journal of economic dynamics & control 48 (2014), pp. 79-94

Persistent link: https://www.econbiz.de/10010485831

Nonlinear forecasting with many predictors using Kernel ridge regression

Exterkate, Peter; Groenen, Patrick J. F.; Heij, Christiaan - 2011

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the...

Persistent link: https://www.econbiz.de/10011382698