Homework6

Homework6 - Kelly Chang Samantha Komosinski Rajan Phakey...

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Kelly Chang, Samantha Komosinski, Rajan Phakey Homework 6 1. source(" ") install.Rmetrics() library(quantmod) S = getSymbols("K", from="2011-01-01", to ="2014-12-08") K.adj= K\$K.Adjusted Kdr=dailyReturn(K, type='log') average = mean(Kdr) average K\$CenterReturn = (Kdr\$daily.returns-average) acf(K\$CenterReturn) pacf(K\$CenterReturn) acf(K\$CenterReturn^2) pacf(K\$CenterReturn^2)

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Kelly Chang, Samantha Komosinski, Rajan Phakey Homework 6 Squaring both plots filtered out most of the autocorrelation in the data. 2. We found that our data most optimally fits into an AR(1) model.
Kelly Chang, Samantha Komosinski, Rajan Phakey Homework 6 3. plot(Kdr) There exists an ARCH effect in the daily log returns of the stock because there is high volatility in the returns, as well as volatility clustering. 4. m2=garchFit(~arma(1,0)+garch(1,1),data=K\$CenterReturn,trace=F,cond="norm ") > summary(m2) Title:  GARCH Modelling  Call:  garchFit(formula = ~arma(1, 0) + garch(1, 1), data = K\$CenterReturn,      cond.dist = "norm", trace = F)  Mean and Variance Equation:  data ~ arma(1, 0) + garch(1, 1) <environment: 0x0000000008e5a6a8>  [data = K\$CenterReturn] Conditional Distribution:  norm

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Kelly Chang, Samantha Komosinski, Rajan Phakey Homework 6
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