Orthogonal Methods for Generating Large Positive Semi-Definite Covariance Matrices
It is a common problem in risk management today that risk measures and pricing models are being applied to a very large set of scenarios based on movements in all possible risk factors. The dimensions are so large that the computations become extremely slow and cumbersome, so it is quite common that over-simplistic assumptions will be made. In particular, in order to generate the large covariance matrices that are used in Value-at-Risk models, some very strong constraints are imposed on the movements in volatility and correlations in all the standard models. The constant volatility assumption is also imposed, because it has not been possible to generate large GARCH covariance matrices with mean-reverting term structures.
|Date of creation:||2000|
|Contact details of provider:|| Postal: PO Box 218, Whiteknights, Reading, Berks, RG6 6AA|
Phone: +44 (0) 118 378 8226
Fax: +44 (0) 118 975 0236
Web page: http://www.henley.reading.ac.uk/
More information through EDIRC
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.:
- Engle, Robert F. & Ng, Victor K. & Rothschild, Michael, 1990.
"Asset pricing with a factor-arch covariance structure : Empirical estimates for treasury bills,"
Journal of Econometrics,
Elsevier, vol. 45(1-2), pages 213-237.
- Robert F. Engle & Victor Ng & Michael Rothschild, 1988. "Asset Pricing with a Factor Arch Covariance Structure: Empirical Estimates for Treasury Bills," NBER Technical Working Papers 0065, National Bureau of Economic Research, Inc.
- Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April. Full references (including those not matched with items on IDEAS)