1、1.1a. 用百分数表示简单月收益率（略）表 1.三支股票的简单日收益率（百分比）的描述性统计American Express Caterpillar StarbucksMean 0.09718% 0.08473% 0.145%Standard Deviation 2.250181% 2.153127% 3.049927%Skewness 0.088388067 0.123793024 0.039442047Excess Kurtosis 2.391684 2.235672 5.319044Minimum Value -13.596% -12.162% -28.286%Maximum Valu
2、e 12.770% 10.845% 14.706%b. 简单收益率换成对数收益率（略）c. 把对数收益率用百分比表示出来（略）表 2.三支股票的对数日收益率（百分比）的描述性统计American Express Caterpillar StarbucksMean 0.07188% 0.06157% 0.09839%Standard Deviation 2.248449% 2.150728% 3.055389%Skewness -0.061668893 -0.015342216 -0.354252488Excess Kurtosis 2.513587 2.390580 7.884276Minim
3、um Value -14.61362% -12.96760% -33.24842%Maximum Value 12.01802% 10.29626% 13.72021%d. 对数收益率零均值检验（ ）=0.051对于 American Expressdata: dailylogre$V2 t = 1.6044, df = 2518, p-value = 0.1087alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -0.01596993 0.15972365 sample es
4、timates:mean of x 0.07187686故接受原假设2对于 Caterpillardata: dailylogre$V3 t = 1.4369, df = 2518, p-value = 0.1509alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -0.02245558 0.14560208 sample estimates:mean of x 0.06157325故接受原假设3对于 Starbucksdata: dailylogre$V4 t = 1.616
5、2, df = 2518, p-value = 0.1062alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:-0.02098409 0.21776370 sample estimates:mean of x 0.09838981故接受原假设1.2a. 用百分数表示简单月收益率（略）表 1.四支股票的简单月收益率（百分比）的描述性统计IBM VW EW SPMean 1.175% 1.191% 1.591% 0.9032%Std. Dev. 7.813905% 4.561798%
6、 5.653741% 4.443510%Skewness 0.3296855 -0.6261454 -0.1811941 -0.4721635Excess Kurtosis 1.625445 2.232391 4.3010991.873961Min. Value -26.190% -22.534% -27.231% -21.763%Max. Value 35.380% 14.150% 29.921% 13.177%b.简单收益率换成对数收益率（略）c.把对数收益率用百分比表示出来（略）表 2.四支股票的对数月收益率（百分比）的描述性统计IBM VW EW SPMean 0.8722% 1.08
7、0% 1.421% 0.8006%Std. Dev. 7.713084% 4.594938% 5.652219% 4.469454%Skewness -0.07136507 -0.9192215 -0.7291766 -0.7426311Excess Kurtosis 1.494529 3.440515 5.2609232.902870Min. Value -30.3676% -25.533% -31.788% -24.5428%Max. Value 30.2915% 13.234% 26.176% 12.3783%d. 对数收益率零均值检验（ ）=0.051对于 IBMdata: daily
8、logre$V2 t = 2.1095, df = 347, p-value = 0.03562alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:0.05899196 1.68541655 sample estimates:mean of x 0.8722043故拒绝原假设2对于 VWdata: dailylogre$V3 t = 4.3846, df = 347, p-value = 1.542e-05alternative hypothesis: true mean is n
9、ot equal to 0 95 percent confidence interval:0.5955359 1.5644506 sample estimates:mean of x 1.079993故拒绝原假设3对于 EWdata: dailylogre$V4 t = 4.6899, df = 347, p-value = 3.937e-06alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:0.8250521 2.0169110 sample estimates:mean of
10、 x 1.420982故拒绝原假设4对于 SPdata: dailylogre$V5 t = 3.3417, df = 347, p-value = 0.0009235alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:0.329396 1.271850 sample estimates:mean of x 0.8006232故拒绝原假设1.3a. 平均 年对 数收益率 =2004=197520041975=2005=197512=129其中， 为第 年对数收益率， 为第 年第 月
11、的月对数收益率 则 SP 的平均年对数收益率= 9.607479%b. =10.09607479(20041975)=16.218761.4a. ，0:()=0 =0.05建立检验统计量 =()6(0,1)计算结果为=1.81106=0.070132“Accept Null Hypothesis“故接受原假设b. ， 0:()=0 =0.05建立检验统计量=()324(0,1)计算结果为=6.23215=4.6007610“Reject Null Hypthesis;Accept Alternative Hypothesis“故拒绝原假设，接受被择假设1.5a. 计算月对数收益率（略）b. 表
12、 3 汇率的对数收益率的描述性统计CA UK JP EUMean 0.9121249 0.9314796 4.768879 0.6860763Std. Dev. 0.03579592 0.0436185 0.06665886 0.05869332Skewness -0.8804152 0.9082561 0.02320874 0.7679195Excess Kurtosis -3.356219 -2.656026 -3.96654-3.604097Min. Value 0.8193392 0.864155 4.631812 0.6026753Max. Value 0.9604224 1.066
13、261 4.910962 0.8264973c. 汇率对数收益率的经验特征Appendix1.1R 语言程序源码setwd(“F:/Financial Econometrics/Homework Guide/TsayDat“)dailyrealpha)result$conclusion-(“Accept Null Hypothesis“)else result$conclusion-(“Reject Null Hypthesis;Accept Alternative Hypothesis“)return(result)1.5 R 语言程序源码setwd(“F:/Financial Econom
14、etrics/Homework Guide/TsayDat“)fxca-read.table(file=“d-fxca00.txt“)fxuk-read.table(file=“d-fxuk00.txt“)fxjp-read.table(file=“d-fxjp00.txt“)fxeu-read.table(file=“d-fxeu00.txt“)fxca$V2=log(1+fxca$V2)fxuk$V2=log(1+fxuk$V2)fxjp$V2=log(1+fxjp$V2)fxeu$V2=log(1+fxeu$V2)mean(fxca$V2)mean(fxuk$V2)mean(fxjp$V2)mean(fxeu$V2)min(fxca$V2)min(fxuk$V2)min(fxjp$V2)min(fxeu$V2)max(fxca$V2)max(fxuk$V2)max(fxjp$V2)max(fxeu$V2)sd(fxca$V2)sd(fxuk$V2)sd(fxjp$V2)sd(fxeu$V2)library(fBasics)skewness(fxca$V2)skewness(fxuk$V2)skewness(fxjp$V2)skewness(fxeu$V2)kurtosis(fxca$V2)-3kurtosis(fxuk$V2)-3kurtosis(fxjp$V2)-3
