上证180金融股波动性研究
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摘要
银行、保险、证券是一种特殊的行业,在一国金融乃至经济体系中的地位是非常重要的。尤其是商业银行,从金融体系来说,它是中央银行货币政策的主要传递者。从经济体系来看,它是现代社会经济运转的枢纽之一。他们的收益率从某种程度反映我国经济发展和市场发展状况,所以本文详细系统的研究上证180金融股波动性。
上交所与中证指数于2007年12月10日正式发布了上证180金融股指数,该指数从上证180指数中挑选银行、保险、证券和信托等行业的股票组成样本股,基日与上证180指数(2002年6月28日)相同,基点1000点。
上证180金融股指数采用了国际通用的权重上限的加权方法,提高了指数的抗操纵性,比较适于作为投资者的投资标的指数;180金融对沪深两市金融服务行业,有很强的行业代表性;从其财务指标、估值指标和历史表现来看,180金融盈利能力佳、估值相对较低、大蓝筹特征显著;其2003年以来的年度日均成交情况也凸显了较高的流动性。
尤其是,目前市场上指数基金的标的指数尚无行业指数,这使投资者错失了分享由行业指数相对大幅增长所带来的收益。上证180金融股指数的推出,提供了行业指数化投资的标的,为投资者带来了分享更多金融行业强劲增长的机会。用ARCH类模型对180金融股进行实证分析,比较它与上证综合指数、道琼斯工业平均指数的收益率和波动性,对它们之间的关系进行分析,这对我国的金融市场建设有着比较重大的现实意义。
Banking, insurance, securities are special kinds of enterprises in one country. The financial position is very important in the economy. In particular, from the financial system, The commercial banks, it is the primary transmitters of the central bank's monetary policy. From the system point of view, it is the hub of one of the modern socio-economic functioning. They yield to some extent reflect the country's economic development and market development situation, so I want to study the volatility of the detailed systematic 180 financial stocks.
The SSE 180 Index is announced by Shanghai Stock Exchange and China Securities Index on December 10, 2007 . The SSE 180 Index selected banks, insurance, securities .They are the composition of samples of the stock shares, based on the SSE 180 Index (June 28, 2002)and the basis points is 1,000 points.
The SSE 180 Index is using limit the weight of the weighting methods to improve the anti-manipulation of the index, comparative fit index as the underlying investors,the methods is accepted by international; The SSE 180 Index is services industry in Shanghai and Shenzhen, there are strong industry representation; its financial targets, valuation indicators and historical performance, the financial profitability of The SSE 180 good, valuations are relatively low, large blue-chip features significant; its 2003 annual average daily turnover since the situation is also highlights a more high liquidity.
In particular, the current market index fund is the subject index there is no industry index, which makes investors lost a substantial share from the industry index of the relative benefits of growth. The SSE 180 was launched, which providing the industry's investment in the subject of indexation for investors to share more financial industry which has brought strong growth opportunities. The ARCH model with The SSE 180 Index on the empirical analysis, compare with the Shanghai Composite Index, The Dow Jones Industrial Average Index.The rate of return and volatility on the analysis of the relationship between them, which is the building of China's financial market has a relatively great practical significance.
上交所与中证指数于2007年12月10日正式发布了上证180金融股指数,该指数从上证180指数中挑选银行、保险、证券和信托等行业的股票组成样本股,基日与上证180指数(2002年6月28日)相同,基点1000点。
上证180金融股指数采用了国际通用的权重上限的加权方法,提高了指数的抗操纵性,比较适于作为投资者的投资标的指数;180金融对沪深两市金融服务行业,有很强的行业代表性;从其财务指标、估值指标和历史表现来看,180金融盈利能力佳、估值相对较低、大蓝筹特征显著;其2003年以来的年度日均成交情况也凸显了较高的流动性。
尤其是,目前市场上指数基金的标的指数尚无行业指数,这使投资者错失了分享由行业指数相对大幅增长所带来的收益。上证180金融股指数的推出,提供了行业指数化投资的标的,为投资者带来了分享更多金融行业强劲增长的机会。用ARCH类模型对180金融股进行实证分析,比较它与上证综合指数、道琼斯工业平均指数的收益率和波动性,对它们之间的关系进行分析,这对我国的金融市场建设有着比较重大的现实意义。
Banking, insurance, securities are special kinds of enterprises in one country. The financial position is very important in the economy. In particular, from the financial system, The commercial banks, it is the primary transmitters of the central bank's monetary policy. From the system point of view, it is the hub of one of the modern socio-economic functioning. They yield to some extent reflect the country's economic development and market development situation, so I want to study the volatility of the detailed systematic 180 financial stocks.
The SSE 180 Index is announced by Shanghai Stock Exchange and China Securities Index on December 10, 2007 . The SSE 180 Index selected banks, insurance, securities .They are the composition of samples of the stock shares, based on the SSE 180 Index (June 28, 2002)and the basis points is 1,000 points.
The SSE 180 Index is using limit the weight of the weighting methods to improve the anti-manipulation of the index, comparative fit index as the underlying investors,the methods is accepted by international; The SSE 180 Index is services industry in Shanghai and Shenzhen, there are strong industry representation; its financial targets, valuation indicators and historical performance, the financial profitability of The SSE 180 good, valuations are relatively low, large blue-chip features significant; its 2003 annual average daily turnover since the situation is also highlights a more high liquidity.
In particular, the current market index fund is the subject index there is no industry index, which makes investors lost a substantial share from the industry index of the relative benefits of growth. The SSE 180 was launched, which providing the industry's investment in the subject of indexation for investors to share more financial industry which has brought strong growth opportunities. The ARCH model with The SSE 180 Index on the empirical analysis, compare with the Shanghai Composite Index, The Dow Jones Industrial Average Index.The rate of return and volatility on the analysis of the relationship between them, which is the building of China's financial market has a relatively great practical significance.
引文
[1]朱东辰,余津津.中国股市波动与经济增长关系的实证分析[J].经济科学,2003.2
[2]高铁梅.计量积极分析方法与建模[M].北京:清华大学出版社,2006.1
[3]易丹辉.数据分析与Eviews应用[M].北京:中国统计出版社,2002.10
[4]张晓峒.计量经济学软件Eviews使用指南[M].天津:南开大学出版社,2004.12
[5]张世英,樊智.协整理论与波动模型[M].北京:清华大学出版社,2004.9
[6]周爱民,徐辉等.金融计量学[M].北京:经济管理出版社,2006.1
[7]王若平.ARCH族计量模型的基本形式及应用[J].科学·经济·社会,2002.4
[8]田铮,吴芳琴,王红军.非线性时间序列模型的混合GARCH方法[J].系统仿真学报,2005
[9]王红军.非线性时间序列建模的混合自回归滑动平均模型[J].控制理论与应用,2005
[10]常松、何建敏.匹配追踪算法对上海股市价格行为的分析[J].管理工程学报,2002.1
[11]陈灯塔.中国股票市场个股随机占优的实证研究[J].经济科学,2003.1
[12]陈守东,陈雷.中国沪深股市收益率及波动性相关性分析[J].金融研究,2003.7
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[15]戴国强等.金融市场微观结构理论[M].上海:上海财经大学出版社2002.3
[16]樊智,张世英.多元GARCH建模及其在中国股市分析中的应用[J].管理科学学报,2003
[17]樊智,张世英.金融波动持续性的研究与预测[J].2003.1
[18]郭兴义,杜本峰,何龙灿.金融计量学研究最新进展[J].经济学动态,2003.1.
[19]郭金龙,刘海军,任国彪.证券系统的能量和嫡及应用研究[J].数量经济技术经济研究,2003.8
[20]刘金山,李楚霖.随机波动率模型下的最优证券组合选择[J].数学的实践与识,2003.5.
[21]刘星,迟建新,宿成建.股票价格指数灰色系统预测与分析[J].数量经济技术经济研究,2003.8.
[22]陆惫祖.高等时间序列[M].上海:上海人民出版社,1999
[23]陆建军,管清友.中国股票市场个股价格异常波动实证分析[J].现代财经,2003.5
[24]皮埃特罗·潘泽,维普·班塞尔著.用VaR度量市场风险[M].机械工业出版社,2005
[25]宋逢明,江婕.中国股票市场波动性特征的实证研究[J].金融研究,2003.4
[26]田华,陆庆春.上海股市周日效应GARCH模型族的实证研究[J].系统工程理论与实践,2003.7
[27]王明涛.证券投资风险计量预测与控制[M].上海:上海财经大学出版社,2002
[28]吴文锋,吴冲锋,朱云.异常波动与资产定价模型与预测[J].系统工程理论与实践,2000.5
[29]吴文锋,吴冲锋.股票价格波动模型探讨[J].系统工程理论与实践,2000.4
[30]张人骥,王怀芳,王耀东.上海证券市场系统风险趋势与波动的实证分析[J].金融研究,2000.1
[31]赵振全、宋玉臣,股票市场信息对称性的影响因素分析[J].数量经济技术经济研究,2003.10
[32]赵留彦,王一鸣.A, B股之间的信息流动与波动溢出[J].金融研究,2003.10
[33]赵留彦,王一鸣.沪深股市交易量与收益率及波动的相关性:来自实证分析的证据[J].经济科学,2003.2
[34]朱林,常松,何建敏.我国股票市场多仿射特性研究[J].管理下程学报,2002.3
[35]朱东辰,余津津.中国股市波动与经济增长关系的实证分析[J].经济科学,2003.2
[36]宋逢明,江婕.中国股票市场波动特性的实证研究[J].金融研究,2003 .4
[37]吴长风.利用回归一GARCH模型对我国沪深股市的分析预测[J].金融研究,1999.4
[38]陈胜荣,高鸿祯.交易量与价格波动的GARCH模型分析[J].统计与决策,2004.7
[39]蒋祥林,土春峰,吴晓霖.中国股市信息流、波动性与交易量关系[M].系统工程理论方法应用.2005.2
[40]丁华.股价指数波动中的ARCH现象[J].数量经济技术经济研究,1999 .9
[41]殷玲、唐杰.GARCH-M模型与我国沪深股市的披动[J].江南大学学报.2002.4
[42] Engle,Robert F. Autoregressive conditional heteroskedasticity witestimates of the variance of U . K.in flation.Econometrica, 1982,(5):987-1008
[43] Engle,Robert E,David M .Lilien,and Russell P.Robins. Estimating time varying risk premia in the terms tructure:The ARCH-M model.E conometrica,1987,(55):391- 407
[44] Jeon,B.N. &Von Furstenberg,GM.Growing intenrational co-movement in stock price indexes. Quarterly Review of Economics and Business,1990,(30):15-30
[44]Hamao,Y。Masulis,R.W.and Ng,V..Correlations in price changes and volatility acrossin tenrational stock markets,Review of Financial Studies,1990,vol.3,
[44]Jeong,J.G Cross-border transmission of stock price volatility:evidence from the overlapping trading hours.Global Finance Joumal,1999,10:53-70
[45] Johansen,S .Estimation and hypothesis testing of cointegration vectors in G aussian vector autoregressive models.Econometrica,1991,59(6):1551-1580
[46]Engle,R.F,Ng,VK .Measuring and testing the impact of news on volatility.Joumal of Finance,19 93,48:1749-1778
[2]高铁梅.计量积极分析方法与建模[M].北京:清华大学出版社,2006.1
[3]易丹辉.数据分析与Eviews应用[M].北京:中国统计出版社,2002.10
[4]张晓峒.计量经济学软件Eviews使用指南[M].天津:南开大学出版社,2004.12
[5]张世英,樊智.协整理论与波动模型[M].北京:清华大学出版社,2004.9
[6]周爱民,徐辉等.金融计量学[M].北京:经济管理出版社,2006.1
[7]王若平.ARCH族计量模型的基本形式及应用[J].科学·经济·社会,2002.4
[8]田铮,吴芳琴,王红军.非线性时间序列模型的混合GARCH方法[J].系统仿真学报,2005
[9]王红军.非线性时间序列建模的混合自回归滑动平均模型[J].控制理论与应用,2005
[10]常松、何建敏.匹配追踪算法对上海股市价格行为的分析[J].管理工程学报,2002.1
[11]陈灯塔.中国股票市场个股随机占优的实证研究[J].经济科学,2003.1
[12]陈守东,陈雷.中国沪深股市收益率及波动性相关性分析[J].金融研究,2003.7
[13]DF·韩德瑞,秦朵.动态经济计量学[M].上海:上海人民出版社,1998.7
[14]戴国强,陆蓉.中国股票市场的周末效应检验[J].金融研究,1999. 4
[15]戴国强等.金融市场微观结构理论[M].上海:上海财经大学出版社2002.3
[16]樊智,张世英.多元GARCH建模及其在中国股市分析中的应用[J].管理科学学报,2003
[17]樊智,张世英.金融波动持续性的研究与预测[J].2003.1
[18]郭兴义,杜本峰,何龙灿.金融计量学研究最新进展[J].经济学动态,2003.1.
[19]郭金龙,刘海军,任国彪.证券系统的能量和嫡及应用研究[J].数量经济技术经济研究,2003.8
[20]刘金山,李楚霖.随机波动率模型下的最优证券组合选择[J].数学的实践与识,2003.5.
[21]刘星,迟建新,宿成建.股票价格指数灰色系统预测与分析[J].数量经济技术经济研究,2003.8.
[22]陆惫祖.高等时间序列[M].上海:上海人民出版社,1999
[23]陆建军,管清友.中国股票市场个股价格异常波动实证分析[J].现代财经,2003.5
[24]皮埃特罗·潘泽,维普·班塞尔著.用VaR度量市场风险[M].机械工业出版社,2005
[25]宋逢明,江婕.中国股票市场波动性特征的实证研究[J].金融研究,2003.4
[26]田华,陆庆春.上海股市周日效应GARCH模型族的实证研究[J].系统工程理论与实践,2003.7
[27]王明涛.证券投资风险计量预测与控制[M].上海:上海财经大学出版社,2002
[28]吴文锋,吴冲锋,朱云.异常波动与资产定价模型与预测[J].系统工程理论与实践,2000.5
[29]吴文锋,吴冲锋.股票价格波动模型探讨[J].系统工程理论与实践,2000.4
[30]张人骥,王怀芳,王耀东.上海证券市场系统风险趋势与波动的实证分析[J].金融研究,2000.1
[31]赵振全、宋玉臣,股票市场信息对称性的影响因素分析[J].数量经济技术经济研究,2003.10
[32]赵留彦,王一鸣.A, B股之间的信息流动与波动溢出[J].金融研究,2003.10
[33]赵留彦,王一鸣.沪深股市交易量与收益率及波动的相关性:来自实证分析的证据[J].经济科学,2003.2
[34]朱林,常松,何建敏.我国股票市场多仿射特性研究[J].管理下程学报,2002.3
[35]朱东辰,余津津.中国股市波动与经济增长关系的实证分析[J].经济科学,2003.2
[36]宋逢明,江婕.中国股票市场波动特性的实证研究[J].金融研究,2003 .4
[37]吴长风.利用回归一GARCH模型对我国沪深股市的分析预测[J].金融研究,1999.4
[38]陈胜荣,高鸿祯.交易量与价格波动的GARCH模型分析[J].统计与决策,2004.7
[39]蒋祥林,土春峰,吴晓霖.中国股市信息流、波动性与交易量关系[M].系统工程理论方法应用.2005.2
[40]丁华.股价指数波动中的ARCH现象[J].数量经济技术经济研究,1999 .9
[41]殷玲、唐杰.GARCH-M模型与我国沪深股市的披动[J].江南大学学报.2002.4
[42] Engle,Robert F. Autoregressive conditional heteroskedasticity witestimates of the variance of U . K.in flation.Econometrica, 1982,(5):987-1008
[43] Engle,Robert E,David M .Lilien,and Russell P.Robins. Estimating time varying risk premia in the terms tructure:The ARCH-M model.E conometrica,1987,(55):391- 407
[44] Jeon,B.N. &Von Furstenberg,GM.Growing intenrational co-movement in stock price indexes. Quarterly Review of Economics and Business,1990,(30):15-30
[44]Hamao,Y。Masulis,R.W.and Ng,V..Correlations in price changes and volatility acrossin tenrational stock markets,Review of Financial Studies,1990,vol.3,
[44]Jeong,J.G Cross-border transmission of stock price volatility:evidence from the overlapping trading hours.Global Finance Joumal,1999,10:53-70
[45] Johansen,S .Estimation and hypothesis testing of cointegration vectors in G aussian vector autoregressive models.Econometrica,1991,59(6):1551-1580
[46]Engle,R.F,Ng,VK .Measuring and testing the impact of news on volatility.Joumal of Finance,19 93,48:1749-1778