A Cholesky-MIDAS model for predicting stock portfolio volatility
AbstractThis paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance covariance matrix. The method proposed here combines this methodology with advances made in the MIDAS literature to produce a forecasting methodology that is flexible, scales easily with the size of the portfolio and produces superior forecasts in simulation experiments and an empirical application.
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Bibliographic InfoPaper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 60.
Length: 32 pages
Date of creation: 31 Aug 2010
Date of revision:
Cholesky; Midas; volatility forecasts;
Other versions of this item:
- Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Cholesky-MIDAS model for predicting stock portfolio volatility," Centre for Growth and Business Cycle Research Discussion Paper Series 149, Economics, The Univeristy of Manchester.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- G00 - Financial Economics - - General - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-20 (All new papers)
- NEP-ETS-2010-11-20 (Econometric Time Series)
- NEP-FOR-2010-11-20 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
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