Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2022

Abstract

Fractile Graphical Analysis (FGA) was proposed by Prasanta Chandra Mahalanobis in 1961 as a method for comparing two distributions at two different points (of time or space) controlling for the rank of a covariate through fractile groups. We use bootstrap techniques to formalize the heuristic method used by Mahalanobis for approximating the standard error of the dependent variable using fractile graphs from two independently selected “interpenetrating network of subsamples.” We highlight the potential and revisit this underutilized technique of FGA with a historical perspective. We explore a new non-parametric regression method called Fractile Regression where we condition on the ranks of the covariate and compare it with existing regression techniques. We apply this method to compare mutual fund inflow distributions after conditioning on ranks or fractiles of pre-tax and post-tax returns and compare distributions of private and public equity returns after controlling for fractiles of assets under management size using the two sample smooth test.

Keywords

non-parametric regression, Fractile Graphical Analysis, rank regression, quantile regression, smooth test, F-tests, bootstrap tests, mutual fund returns, private equity returns

Discipline

Finance and Financial Management

Research Areas

Finance

Publication

Journal of Risk and Financial Management

Volume

15

Issue

9

First Page

1

Last Page

20

ISSN

1911-8066

Identifier

10.3390/jrfm15090412

Publisher

MDPI

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.3390/jrfm15090412

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