Journal of Investment Strategies
ISSN:
2047-1238 (print)
2047-1246 (online)
Editor-in-chief: Ali Hirsa
Need to know
- Complete algorithms and source code for constructing statistical risk models.
- Methods for fixing the number of risk factors, including based on effective rank.
- A complete non-iterative algorithm and source code for computing eigenpairs.
- The presentation is intended to be essentially self-contained and pedagogical.
Abstract
In this paper, we give complete algorithms and source code for constructing statistical risk models, including methods for fixing the number of risk factors. One such method is based on effective rank (eRank) and yields results similar to (and further validates) the prior method of Kakushadze. We also give a complete algorithm and source code for computing eigenvectors and eigenvalues of a sample covariance matrix, (i) which requires no costly iterations and (ii) for which the number of operations is linearly proportional to the number of returns. This paper is intended to be pedagogical and oriented toward practical applications.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
