A Practitioner's Guide to Factor Models by Edwin Burmeister; Richard Roll; Stephen A. Ross; Edwin J.

By Edwin Burmeister; Richard Roll; Stephen A. Ross; Edwin J. Elton; Martin J. Gruber; Richard Grinold and Ronald N. Kahn

This monograph offers the paintings of 3 teams of specialists addressing using single-factor types to provide an explanation for safety returns: Edwin Burmeister, Richard Roll, and Stephen Ross clarify the fundamentals of Arbitrage Pricing thought and speak about the macroeconomic forces which are the underlying assets of threat; Edwin J. Elton and Martin J. Gruber current multi-index types and supply tips on their reliability and value; and Richard C. Grinold and Ronald N. Kahn deal with multiple-factor types for portfolio threat.

Show description

Read Online or Download A Practitioner's Guide to Factor Models PDF

Similar business & money books

International sales law

Offers a selective research of the provisions of the CISG which were utilized in a 'critical mass' of courtroom and arbitral judgements. The booklet, assessing the kingdom of foreign revenues legislation, is well timed given the maturing nation of CISG jurisprudence. should be of curiosity to practitioners and students alike.

The Managing Change Pocketbook (Management Pocket Book Series)

For someone accountable for handling swap or dealing with imposed swap. Explains what swap is and why it is vital, why a few swap wishes proactive administration, the results of swap on humans, how you can achieve dedication, tips on how to deal with it. contains examples of good fortune and failure

Geopolitics for Investors

Geopolitical concerns have a profound impact on funding recommendations and effects. traders necessarily needs to stability possibility and gift. Geopolitics can convey either dangers and possibilities, huge and small, onto the funding panorama. The query is, How a lot effort and time could be dedicated to this actual activity?

Additional info for A Practitioner's Guide to Factor Models

Sample text

In addition, the structure should be parsimonious; that is, returns can be described in terms of a limited number of indexes. Finally, having the indexes represent separate influences would be desirable. Two statistical techniques accomplish these goals: factor analysis and The most common technique is factor analysis. ~ Factor analysis was devised to define a set of indexes mathematically so that the covariance between security returns is minimized after the indexes have been removed. This assures that cov(e,ej) is as close to zero as it can be.

For any hypothesized number of factors, factor analysis finds the indexes and the loadings on each index for each security to make the covariance between the unique returns as small as possible. Although the indexes produced by factor analysis need not be orthogonal to l5 In addition, researchers often want to construct the indexes from a larger sample of stocks or to have other properties such as being widely diversified. See, for example, Lehrnann and Modest (1988). l6 For example, in a single-index model, beta has been shown to be positively related to residual variance.

Additional principal components are then extracted until the user decides that they are picking up random influences in the data rather than real information. Of course, a prior estimate of the number of relevant influences will narrow the choice of how many principal components to extract. To obtain a multi-index model from a principal component solution some adjustments are usually performed. First, the indexes obtained from a principal components solution have decreasing standard deviations as additional influ- l4 Many researchers choose to perform principal component or factor analysis on the correlation matrix rather than the variance-covariance matrix.

Download PDF sample

Rated 4.59 of 5 – based on 27 votes