Beyond Correlation: Using the Elements of Variance for Conditional Means and Probabilities

34 Pages Posted: 10 Mar 2016 Last revised: 2 Nov 2017

See all articles by Fred Viole

Fred Viole

OVVO Financial Systems; Fordham University

Date Written: November 1, 2017

Abstract

We derive conditional means from partial moment quadrants of the joint distribution. Restricting quadrants enables scenario analysis without the need for an underlying correlation assumption. Weighting of these conditional means permits more generalized scenarios with embedded dependence structures. The resulting analysis simultaneously considers multiple correlation assumptions and demonstrates that correlation is not necessary to derive expected values, rather merely a probability of that expected value for a given condition. Extending the analysis to mean/variance optimization identifies a major philosophical inconsistency with its treatment of correlation, and offers an alternative to the use of correlation in constructing portfolios.

Keywords: Partial Moments, Correlation, Dependence, Conditional Means

JEL Classification: C00

Suggested Citation

Viole, Fred, Beyond Correlation: Using the Elements of Variance for Conditional Means and Probabilities (November 1, 2017). Available at SSRN: https://ssrn.com/abstract=2745308 or http://dx.doi.org/10.2139/ssrn.2745308

Fred Viole (Contact Author)

OVVO Financial Systems ( email )

NJ
United States

Fordham University ( email )

Bronx, NY 10458
United States

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