Tail Parameters of Stable Distributions Using One Million Observations of Real Estate Returns from Five Continents

38 Pages Posted: 11 Apr 2014

See all articles by Michael Stein

Michael Stein

University of Duisburg-Essen

Daniel Piazolo

THM Technische Hochschule Mittelhessen

Stoyan V. Stoyanov

Charles Schwab

Multiple version iconThere are 3 versions of this paper

Date Written: December 10, 2013

Abstract

This study focuses on global real estate return distributions. For our analysis, we employ the class of stable distributions that has become prominent in the real estate literature. We add to the literature by undertaking a global-scale analysis for the first time. By using data since the early 1990s, we show that there is considerable variation in the tail weights of return distributions, both between countries as well as among sectors within the countries. It is important to note that the tail parameters vary over time as well. Our results strengthen the recently discovered notion about non-constant tail parameters in stable distributions, which contradicts earlier findings about constant tail parameters. Additionally, we argue that merely changes over time were to be discovered, rather than pure methodological facts driving the variation, which is in contrast to the initial assumption associated with constant tail parameters. Our results provide an extensive overview of the tailedness of global real estate markets and offer a comprehensive insight into differing market distributions.

Keywords: Real Estate return distributions; stable distributions, tail dependence

JEL Classification: G01, G10, G12

Suggested Citation

Stein, Michael and Piazolo, Daniel and Stoyanov, Stoyan Veselinov, Tail Parameters of Stable Distributions Using One Million Observations of Real Estate Returns from Five Continents (December 10, 2013). Ruhr Economic Paper No. 465, Available at SSRN: https://ssrn.com/abstract=2423258 or http://dx.doi.org/10.2139/ssrn.2423258

Michael Stein (Contact Author)

University of Duisburg-Essen ( email )

Universitätsst. 12
Duisburg, 45117
Germany

HOME PAGE: http://www.fmoek.wiwi.uni-due.de/

Daniel Piazolo

THM Technische Hochschule Mittelhessen ( email )

University of Applied Sciences
Wilhelm-Leuschner-Straße 13
Friedberg, 61169
Germany

HOME PAGE: http://https://www.thm.de/wi/daniel-piazolo

Stoyan Veselinov Stoyanov

Charles Schwab ( email )

101 Montgomery Street (120K-15)
San Francisco, CA 94104
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
11
Abstract Views
360
PlumX Metrics