本文關(guān)鍵詞：中國(guó)股市收益率偏度特性研究　出處：《復(fù)旦大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 偏度性 厚尾性 偏斜分布 中國(guó)股市 收益率

【摘要】：在資本市場(chǎng)中,資產(chǎn)收益率分布經(jīng)常表現(xiàn)出不對(duì)稱(chēng)性,即偏度性,并且關(guān)于偏度現(xiàn)象的研究已經(jīng)有近四十年的時(shí)間了。然而,在實(shí)際應(yīng)用和研究中經(jīng)常假設(shè)資產(chǎn)收益率服從正態(tài)分布等對(duì)稱(chēng)性分布,忽視了偏度特性。如果偏度現(xiàn)象真實(shí)存在,且不容忽視,那么簡(jiǎn)單的正態(tài)分布假設(shè)極有可能造成對(duì)真實(shí)收益率和風(fēng)險(xiǎn)特征認(rèn)識(shí)的誤導(dǎo)。眾所周知,收益率影響著資產(chǎn)定價(jià)、投資組合、風(fēng)險(xiǎn)預(yù)測(cè)和市場(chǎng)參與者行為等諸多方面,偏度作為資產(chǎn)收益率的固有屬性,進(jìn)而影響著資產(chǎn)定價(jià),也決定著配置資產(chǎn)以及影響著市場(chǎng)參與者行為和監(jiān)管等等。因此,研究資本市場(chǎng)收益率的偏度特性具有極大的理論意義和實(shí)踐意義。文章以2006年到2013年間的中國(guó)股票市場(chǎng)為研究對(duì)象,基于不同分布構(gòu)建的隨機(jī)波動(dòng)模型(SV),研究論證了股市收益率偏度現(xiàn)象的存在性及其主要特點(diǎn)。其中,八種不同概率分布分別是：正態(tài)分布(N)、偏斜正態(tài)分布(SN)、學(xué)生t分布(T)、偏斜學(xué)生t分布(ST)、廣義誤差分布(GED)、偏斜廣義誤差分布(SGED)、混合正態(tài)分布(MN)以及混合偏斜正態(tài)分布(MSN)。通過(guò)實(shí)證研究,得到了已下幾點(diǎn)結(jié)論：(1)正態(tài)分布的擬合效果最差,收益率服從正態(tài)分布的假設(shè)并非最優(yōu)選擇；(2)SN和N、ST和T、SGED和GED以及MN和MSN四對(duì)八個(gè)分布中,每對(duì)分布中含偏度參數(shù)的分布至少不會(huì)比相對(duì)應(yīng)不含偏度參數(shù)的分布表現(xiàn)得差；(3)相比N和SN分布,具備刻畫(huà)厚尾能力的分布表現(xiàn)得更好,說(shuō)明中國(guó)股票市場(chǎng)不僅存在偏度現(xiàn)象,也伴隨著普遍的尖峰厚尾性；(4)綜合比較,混合偏斜正態(tài)分布(MSN)擬合效果最好,它不僅能夠抓住股市收益率的偏度性和厚尾性,而且還可以分別刻畫(huà)左、右尾部的“非常態(tài)”收益率情況。最后,我們以資本資產(chǎn)定價(jià)模型(CAPM)為一個(gè)例子,展示了偏度的應(yīng)用價(jià)值意義。我們?cè)贑APM模型中引入最好的MSN分布,以研究偏度對(duì)資本資產(chǎn)定價(jià)的影響,實(shí)證結(jié)果認(rèn)為：基于正態(tài)分布假設(shè)的經(jīng)典CAPM模型限制了α的真實(shí)值,如果用于回歸具有負(fù)偏形態(tài)的各行業(yè)指數(shù)收益率,得到的結(jié)論其實(shí)是高估了行業(yè)真實(shí)收益率、低估了風(fēng)險(xiǎn)�？傊�,如果沒(méi)能夠充分考慮收益率的偏度和厚尾性,極有可能對(duì)資產(chǎn)的真實(shí)收益率和風(fēng)險(xiǎn)情況產(chǎn)生錯(cuò)誤的認(rèn)識(shí)。在偏度的市場(chǎng)中,選擇相應(yīng)的偏斜的分布研究金融問(wèn)題,具有普遍的理論意義和現(xiàn)實(shí)意義。比如在資產(chǎn)定價(jià)、投資組合構(gòu)建、期貨交易保證金比例設(shè)定、金融風(fēng)險(xiǎn)預(yù)測(cè)以及監(jiān)管規(guī)則制定等等方面都大有用處。
[Abstract]:In the capital market, the distribution of return on assets often shows asymmetry, that is, skewness, and the research on skewness has been for nearly forty years. In the practical application and research, it is often assumed that the symmetrical distribution of the return rate of assets from normal distribution, ignoring the skewness characteristics, if the skewness exists, and can not be ignored. That simple assumption of normal distribution is likely to lead to misleading perceptions of the true rate of return and the characteristics of risk. As we all know, the rate of return affects asset pricing and portfolio. Risk forecasting and market participants' behavior, as the inherent attribute of asset return rate, affect asset pricing. It also determines the allocation of assets and influences the behavior and regulation of market participants and so on. It is of great theoretical and practical significance to study the skewness of capital market returns. This paper takes the Chinese stock market from 2006 to 2013 as the research object. Based on the stochastic volatility model constructed with different distributions, this paper studies the existence and main characteristics of the skewness of stock market returns. Among them, eight different probability distributions are normal distribution. Skew normal distribution, student t distribution, skew student t distribution, generalized error distribution and skew generalized error distribution. Mixed normal distribution (MN) and mixed skew normal distribution (MSN). Through empirical research, we have got the following conclusions: 1) the fitting effect of normal distribution is the worst. The assumption that the rate of return is normal distribution is not the best choice; SGED and GED and MN and MSN in four pairs of eight distributions. The distribution of biasing parameters in each pair of distributions is at least not worse than that of the corresponding distribution without bias parameters. Compared with the distribution of N and SN, the distribution with the ability to depict the thick tail is better, which indicates that the stock market in China has not only the phenomenon of bias, but also accompanied by the widespread spike and thick tail. Comprehensive comparison shows that the mixed skew normal distribution has the best fitting effect. It can not only grasp the skewness and thick tail of the stock market yield, but also depict the left. The "abnormal" yield at the right end. Finally, we take the capital asset pricing model (CAPMM) as an example. We introduce the best MSN distribution in CAPM model to study the influence of bias on capital asset pricing. The empirical results show that the classical CAPM model based on the normal distribution hypothesis limits the true value of 偽, if it is used to return the returns of industries with negative bias. The conclusion is that it overestimates the real yield of the industry and underestimates the risk. In a word, if we can not fully consider the skewness and thick tail of the yield. It is very likely to have a wrong understanding of the real return rate and risk of assets. In the biased market, choose the corresponding skew distribution to study the financial problems. It has universal theoretical and practical significance, such as asset pricing, portfolio construction, margin ratio setting of futures trading, financial risk forecasting and regulatory regulation.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F832.51

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