The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jul. 06, 2010
Filed:
Apr. 04, 2003
Ananth Madhavan, New York, NY (US);
Artem V. Asriev, Winchester, MA (US);
Scott J. Kartinen, New York, NY (US);
Jian Yang, Sharon, MA (US);
Vitaly Serbin, Boston, MA (US);
Ian Domowitz, New York, NY (US);
Kenneth E. Gosier, Cambridge, MA (US);
Ananth Madhavan, New York, NY (US);
Artem V. Asriev, Winchester, MA (US);
Scott J. Kartinen, New York, NY (US);
Jian Yang, Sharon, MA (US);
Vitaly Serbin, Boston, MA (US);
Ian Domowitz, New York, NY (US);
Kenneth E. Gosier, Cambridge, MA (US);
ITG Software Solutions, Inc., Culver City, CA (US);
Abstract
A factor risk model based method for generating risk forecasts. In one embodiment, the method includes: selecting a set of securities; selecting a set of risk factors; determining the risk factor returns; constructing a risk factor covariance matrix; constructing an idiosyncratic variance matrix; determining, for each risk factor, a factor loading coefficient for each selected security; projecting the risk factor covariance matrix into a future forecast; and projecting the idiosyncratic variance matrix into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities. In some embodiments, the step of estimating factor loadings includes performing a time series regression to obtain the sensitivity of each stocks' return to variations in the factor's return.