Stochastic dominance via quantile regression with applications to investigate arbitrage opportunity and market efficiency

Pin T Ng, Wing Keung Wong, Zhijie Xiao

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Tests for stochastic dominance constructed by translating the inference problem of stochastic dominance into parameter restrictions in quantile regressions are proposed. They are variants of the one-sided Kolmogorov–Smirnoff statistic with a limiting distribution of the standard Brownian bridge. The procedure to obtain the critical values of our proposed test statistics are provided. Simulation results show their superior size and power. They are applied to the NASDAQ 100 and S&P 500 indices to investigate dominance relationship before and after major turning points. Results show no arbitrage opportunity between the bear and bull markets. Our results infer that markets are inefficient and risk averters are better off investing in the bull rather than the bear market.

Original languageEnglish (US)
Pages (from-to)666-678
Number of pages13
JournalEuropean Journal of Operational Research
Volume261
Issue number2
DOIs
StatePublished - Sep 1 2017

Fingerprint

Market Efficiency
Stochastic Dominance
Quantile Regression
Arbitrage
Statistics
Brownian Bridge
Turning Point
Limiting Distribution
Test Statistic
Statistic
Critical value
Restriction
Market
Bear market
Stochastic dominance
Market efficiency
Quantile regression
Simulation
Bull market
Inference

Keywords

  • Brownian bridge
  • Internet bubble crisis
  • Quantile regression
  • Stochastic dominance
  • Subprime crisis

ASJC Scopus subject areas

  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Cite this

Stochastic dominance via quantile regression with applications to investigate arbitrage opportunity and market efficiency. / Ng, Pin T; Wong, Wing Keung; Xiao, Zhijie.

In: European Journal of Operational Research, Vol. 261, No. 2, 01.09.2017, p. 666-678.

Research output: Contribution to journalArticle

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