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Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance

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Author Info
Golosnoy, Vasyl
Herwartz, Helmut
Abstract

We consider the problem of ex-ante forecasting conditional correlation patterns using ultra high frequency data. Flexible semiparametric predictors referring to the class of dynamic panel and dynamic factor models are adopted for daily forecasts. The parsimonious set up of our approach allows to forecast correlations exploiting both estimated realized correlation matrices and exogenous factors. The Fisher-z transformation guarantees robustness of correlation estimators under elliptically constrained departures from normality. For the purpose of performance comparison we contrast our methodology with prominent parametric and nonparametric alternatives to correlation modeling. Based on economic performance criteria, we distinguish dynamic factor models as having the highest predictive content.

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Paper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics working papers with number 2007,23.

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Date of creation: 2007
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Handle: RePEc:zbw:cauewp:5903

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Web page: http://www.wiso.uni-kiel.de/econ/

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Related research
Keywords: Correlation forecasting Epps effect Fourier method Dynamic panel model Dynamic factor model

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing

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