The Study On The Volatility Decomposition Of Shenzhen Stock Market

Campbell, Lettau, Malkiel and Xu (2001) have put forward an innovati ve volatility decomposition model without the information of (3. They broke down the volatility in the terms of market, industry and firm three levels, and found that the market volatility and industry volatility are stable during the sample period, but idiosyncratic volatility has an upward trend overtime. Domestic research on decomposition of volatility is rare, and shows differe nt results as the sample period choice.Having read the relative literature about volatility decomposition, I foun d that the volatility decomposition can segment the overall market risk, to h elp us make a more in-depth study of stock market risk. As there is no res earch aim at Shenzhen market, so I have choosen the Shenzhen stock mark et volatility decomposition as the topic of my master thesis. My sample per iod is between July2001and December2011.This paper has divided into eight chapters and the contents are summar ized as followed:Chapter1IntroductionIn this chapter we not only described the background and significance, the research content and method of this study, but also described the structu ral arrangements and the innovations&deficiencies.Chapter2An overview of stock market volatilityThe content of this chapter is divided into two aspects:Firstly, a simpl e definition of stock market volatility, and describes about the different vola tility estimation methods, which including:the variance method, ARCH/G ARCH model, stochastic volatility and options implied volatility. Secondly, a brief introduction on the general characteristics of the stock market volatilit y,which including:peak fat-tail distribution of returns, the mean recovery, ag gregation, long memory and persistence, leverage, feedback effects, volatility smile and spillover effects.Chapter3An overview of the Shenzhen stock market volatilityIn this chapter we briefly describes the basic situation of the Shenzhen stock market.Then, we make a specific introduction to the fluctuations of t he Shenzhen Composite Index.Chapter4An review of volatility decompositionIn this section, we briefly describe the decomposition methods and the result of some volatility decomposition research.Chapter5Empirical analysis of volatility decompositionThe content of this chapter is divided into two aspects:firstly we intro duce the data and models used in this article. Secondly, we get the three v olatility series statistical description, and study their basic characteristics. Thi rdly, we study their trend characteristics through the regression model. Finall y, we use Granger causality analysis to test the correlation between them.Chapter6Analysis of industry volatilityIn this section, we analyze the characteristics of the industry volatility and the industry indiosycratic volatility, and use company number and total market capitalization to explain them.Chapter7Analysis of board volatilityIn this section, we analyze the characteristics of the idiosyncratic volatil ity of different boards, their influence to the whole market and their relevan ce.Chapter8SummaryIn this section, we summarize the main points of this paper and give t he corresponding recommendations.The main conclusions of this article are as followed:Firstly, Similar to the structure of Europe and the United States, China’ s stock market’s idiosyncrasy volatility is the highest, the market volatility is the next and the industry volatility is the lowest. There is no definitively trend in those three volatility series.Secondly, Industry volatility of Real estate, extractive industries, finance and insurance, cultural communications industry is high than other industry. Manufacturing industry’s risk on industry level is much smaller than other industry. Utilities industry has the lowest company idiosyncratic volatility. T he integrated industry and communication culture industry’s company idiosyn cratic volatility is higher than other industry.Thirdly, GEM’s idiosyncratic volatility is the highest, it followed by sm all board and motherboard’s A shares, B shares’idiosyncratic volatility is th e lowest. The joint of small board and GEM will raise the whole market id iosyncratic risk. Motherboard’s A shares and small board idiosyncratic volatil ity can explain each other.The innovation of this paper is as followed, firstly, we use the latest d ata of Shenzhen market; secondly, we calculate different broads’ idiosyncrati c volatility, and study their characteristics。There are still many deficiencies in this article:firstly, the sample peri od is short, only contains one complete volatile cycle of stock market, whic h may make the trend test not accuracy; secondly, the study is not deep en ough, we only analyze the basic characteristics of volatility but did not stud y their reasons.