Panel Stochastic Dominance Test and Panel Informational Efficiency LR Test

C. de Peretti, Chia-Ying Chan, W. Wong, C. Siani
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Abstract

This paper propose a new panel stochastic dominance (SD) test-PDD test, the asymptotic properties are derived, which extends Davidson and Duclos (DD) SD test to a panel context. The PDD test also contributes to settle one of the demerits while working with financial derivatives time series: that the standard individual tests for Stochastic Dominance in time series are unsatisfactory in terms of power when the sample size is too small, and typically the financial derivatives have a limited life, in particular, stock options and covered warrants. This is because the pairwise SD tests are nonparametric, and nonparametric tests require large sample size, in this case, the individual tests for financial derivative time series may not distinguish between the null and the alternative hypotheses for each series, and lead to retain the null hypothesis, even if the alternative is true. Hence the PDD test would improve the power of individual SD tests: a panel test gathers all the information of all the series, and then increases the power compared to its corresponding individual test. This paper also extends the classical likelihood ratio (LR) information efficiency test to a panel framework to get more powerful new tests. A bootstrap methodology is developed to correct the size distortion of the LR test.
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面板随机优势检验和面板信息效率LR检验
本文提出了一种新的面板随机优势检验(SD - pdd),并推导了其渐近性质,将Davidson和Duclos (DD) SD检验推广到面板环境。PDD检验还有助于解决处理金融衍生品时间序列时的一个缺点:当样品量太小时,时间序列中随机优势的标准个体检验在功率方面不令人满意,并且金融衍生品通常具有有限的寿命,特别是股票期权和有担保权证。这是因为成对SD检验是非参数的,而非参数检验需要大样本量,在这种情况下,金融导数时间序列的个别检验可能无法区分每个序列的零假设和备选假设,并导致保留零假设,即使备选假设为真。因此,PDD测试将提高单个SD测试的功率:面板测试收集所有系列的所有信息,然后与相应的单个测试相比增加功率。本文还将经典的似然比(LR)信息效率检验扩展到面板框架,得到更强大的新检验。开发了一种自举方法来纠正LR测试的尺寸失真。
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