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

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  • Ng, Pin
  • Wong, Wing-Keung
  • Xiao, Zhijie

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.

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  • Ng, Pin & Wong, Wing-Keung & Xiao, Zhijie, 2017. "Stochastic dominance via quantile regression with applications to investigate arbitrage opportunity and market efficiency," European Journal of Operational Research, Elsevier, vol. 261(2), pages 666-678.
  • Handle: RePEc:eee:ejores:v:261:y:2017:i:2:p:666-678
    DOI: 10.1016/j.ejor.2017.02.047
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    2. Green, Lawrence & Sung, Ming-Chien & Ma, Tiejun & Johnson, Johnnie E. V., 2019. "To what extent can new web-based technology improve forecasts? Assessing the economic value of information derived from Virtual Globes and its rate of diffusion in a financial market," European Journal of Operational Research, Elsevier, vol. 278(1), pages 226-239.
    3. Wong, W.-K. & Lean, H.H. & McAleer, M.J. & Tsai, F.-T., 2018. "Why did Warrant Markets Close in China but not Taiwan?," Econometric Institute Research Papers EI2018-22, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Liesiö, Juuso & Xu, Peng & Kuosmanen, Timo, 2020. "Portfolio diversification based on stochastic dominance under incomplete probability information," European Journal of Operational Research, Elsevier, vol. 286(2), pages 755-768.

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