SSRN Author: Christopher G GreenChristopher G Green SSRN Content
http://www.ssrn.com/author=429464
http://www.ssrn.com/rss/en-usSat, 16 Dec 2017 02:43:44 GMTeditor@ssrn.com (Editor)Sat, 16 Dec 2017 02:43:44 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: The Univariate t-Distribution Information MatrixThe information matrix for a univariate t-distribution is available as a special case of a number of results dealing with multivariate t-distributions, e.g., Lange et al. (1989). However, specializing such multivariate results to the case of a univariate t-distribution requires the reader to deal with complex multivariate notation. On the other hand, we have not found a derivation of the information matrix for the simple case of a univariate t-distribution with a three-parameter model in the statistics literature. Therefore, we provide here a straightforward (though tedious) derivation, the results of which we use in Martin and Zhang (2017).
http://www.ssrn.com/abstract=3086998
http://www.ssrn.com/1652034.htmlFri, 15 Dec 2017 09:32:40 GMTNew: Robust Detection of Multivariate Outliers in Asset Returns and Risk Factors DataIt is well-known that outliers exist in the type of multivariate data used by financial practitioners for portfolio construction and risk management. Typically, outliers are addressed prior to model fitting by applying some combination of trimming and/or Winsorization to each individual variable. This approach often fails to detect and/or mitigate multivariate outliers. Existing literature documents the use of the robust Mahalanobis squared distance (RSD) based on the minimum covariance determinant (MCD) estimator to detect and to shrink multivariate outliers in financial data. We use MCD-based RSDs, along with a modified version of the Iterated Reweighted MCD methodology of Cerioli, to illustrate the presence of outliers in the asset returns and firm fundamental data that equity portfolio managers would use to build and monitor portfolios. We demonstrate how RSDs based on the MCD estimate are superior to Mahalanobis distances based on the classical mean and covariance estimates for ...
http://www.ssrn.com/abstract=3046092
http://www.ssrn.com/1630296.htmlMon, 02 Oct 2017 17:34:33 GMTNew: Fama-French 1992 Redux with Robust StatisticsRobust statistical methods provide estimates that are not much influenced by a small percentage of outliers but perform in a nearly optimal manner for normally distributed data. Unfortunately, such methods are rarely used in quantitative finance research despite their potential utility, particularly in empirical asset pricing studies. As a means of stimulating the use of robust statistical methods in such studies and in quantitative finance in general, we demonstrate the efficacy of using a theoretically well-justified robust regression method in the cross-sectional regressions often used in empirical asset pricing studies. We compare the results of using both least squares and robust regression methods for the models presented in Fama and French (1992) (FF92), as well as some extensions to these models, over the time period 1963–2015 and subsets thereof. Our analysis clearly demonstrates that a very small fraction of outliers, in the returns and/or the factors, often distorts least ...
http://www.ssrn.com/abstract=2963855
http://www.ssrn.com/1588628.htmlFri, 05 May 2017 12:55:55 GMT