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新兴市场中股票收益分布的非普遍性检验。

Tests of nonuniversality of the stock return distributions in an emerging market.

作者信息

Mu Guo-Hua, Zhou Wei-Xing

机构信息

School of Business, East China University of Science and Technology, Shanghai, China.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Dec;82(6 Pt 2):066103. doi: 10.1103/PhysRevE.82.066103. Epub 2010 Dec 2.

Abstract

There is convincing evidence showing that the probability distributions of stock returns in mature markets exhibit power-law tails and both the positive and negative tails conform to the inverse cubic law. It supports the possibility that the tail exponents are universal at least for mature markets in the sense that they do not depend on stock market, industry sector, and market capitalization. We investigate the distributions of intraday returns at different time scales ( Δt=1, 5, 15, and 30 min) of all the A-share stocks traded in the Chinese stock market, which is the largest emerging market in the world. We find that the returns can be well fitted by the q-Gaussian distribution and the tails have power-law relaxations with the exponents increasing with Δt and being well outside the Lévy stable regime for individual stocks. We provide statistically significant evidence showing that, at small time scales Δt<15 min, the exponents logarithmically decrease with the turnover rate and increase with the market capitalization. When Δt>15 min, no conclusive evidence is found for a possible dependence of the tail exponent on the turnover rate or the market capitalization. Our findings indicate that the intraday return distributions at small time scales are not universal in emerging stock markets but might be universal at large time scales.

摘要

有令人信服的证据表明,成熟市场中股票回报的概率分布呈现幂律尾部,且正负尾部均符合反立方律。这支持了至少在成熟市场中尾部指数具有普遍性的可能性,即它们不依赖于股票市场、行业部门和市值。我们研究了世界上最大的新兴市场——中国股票市场中所有A股股票在不同时间尺度(Δt = 1、5、15和30分钟)下的日内回报分布。我们发现回报可以很好地用q-高斯分布拟合,并且尾部具有幂律弛豫,指数随Δt增加,且对于个股而言远超出列维稳定区域。我们提供了具有统计显著性的证据表明,在小时间尺度Δt < 15分钟时,指数随换手率对数下降,随市值增加。当Δt > 15分钟时,未发现尾部指数可能依赖于换手率或市值的确凿证据。我们的研究结果表明,新兴股票市场中小时间尺度下的日内回报分布不具有普遍性,但在大时间尺度下可能具有普遍性。

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