Measuring the Speed of Convergence of Stock Prices: A Nonparametric and Nonlinear Approach

Hyeongwoo Kim () and Deockhyun Ryu

No auwp2013-06, Auburn Economics Working Paper Series from Department of Economics, Auburn University

Abstract: This paper evaluates the speed of convergence across national stock markets employing a nonlinear, nonparametric stochastic model of relative stock prices. We use operational algorithms for estimating general measures of persistence of the relative stock price that are based on two statistical notions: the short memory in mean (SMM) and the short memory in distribution (SMD). Using G7 countries¡¯ stock indices, we obtain strong empirical evidence in favor of the contrarian strategy for France, Germany, Italy, and the UK relative to the US market, while our results imply quite limited usefulness of the strategy for Canada and Japan. Further, we obtain fairly fast convergence rates toward the equilibrium for the former group.

Keywords: Speed of Convergence; Contrarian Strategy; Short Memory in Mean; Short-Memory in Distribution; mixing; Max Half-Life; Max Quarter-Life (search for similar items in EconPapers)
JEL-codes: C14 C22 F36 G11 G14 (search for similar items in EconPapers)
Date: 2013-03
New Economics Papers: this item is included in nep-ore
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Related works:
Journal Article: Measuring the speed of convergence of stock prices: A nonparametric and nonlinear approach (2015)
Working Paper: Measuring the Speed of Convergence of Stock Prices: A Nonparametric and Nonlinear Approach (2015)
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