Showing posts with label statistics NASDAQ Composite. Show all posts
Showing posts with label statistics NASDAQ Composite. Show all posts

## How to get statistics NASDAQ Composite (^IXIC)

> getSymbols("^IXIC", from="2004-01-01", to=Sys.Date())
[1] "^IXIC"
> chartSeries(Cl(IXIC))

> ret <- dailyReturn(Cl(IXIC), type='log')
> par(mfrow=c(2,2))
> acf(ret, main="Return ACF");
> pacf(ret, main="Return PACF");
> acf(ret^2, main="Squared return ACF");
> pacf(ret^2, main="Squared return PACF")

> par(mfrow=c(1,1))
> m=mean(ret);s=sd(ret);
> par(mfrow=c(1,2))
> hist(ret, nclass=40, freq=FALSE, main='Return histogram');curve(dnorm(x,mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")

> plot(density(ret), main='Return empirical distribution');curve(dnorm(x,mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")

> par(mfrow=c(1,1))
> library("moments", lib.loc="~/R/win-library/3.6")

Attaching package: ‘moments’

The following objects are masked from ‘package:timeDate’:

kurtosis, skewness

> kurtosis(ret)
daily.returns
10.25008
> plot(density(ret), main='Return EDF - upper tail', xlim = c(0.1, 0.2),
+      ylim=c(0,2));

> curve(dnorm(x, mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")
> plot(density(ret), xlim=c(-5*s,5*s),log='y', main='Density on log-scale')

> curve(dnorm(x, mean=m,sd=s), from=-5*s, to=5*s, log="y", add=TRUE,
+       col="red")
> qqnorm(ret);qqline(ret);

> library("rugarch", lib.loc="~/R/win-library/3.6")

Attaching package: ‘rugarch’

The following object is masked from ‘package:stats’:

sigma

> chartSeries(ret)

> garch11.spec = ugarchspec(variance.model = list(model="sGARCH",garchOrder=c(1,1)), mean.model = list(armaOrder=c(0,0)))
> ixic.garch11.fit = ugarchfit(spec=garch11.spec, data=ret)
> coef(ixic.garch11.fit)
mu        omega       alpha1        beta1
7.437932e-04 3.473346e-06 9.976260e-02 8.737787e-01
> coef(ixic.garch11.fit)
mu        omega       alpha1        beta1
7.437932e-04 3.473346e-06 9.976260e-02 8.737787e-01
> vcov(ixic.garch11.fit)
[,1]          [,2]          [,3]          [,4]
[1,]  2.231885e-08  3.793626e-11  1.205145e-07 -3.237873e-07
[2,]  3.793626e-11  1.806952e-12  2.458478e-09 -1.265085e-08
[3,]  1.205145e-07  2.458478e-09  7.668355e-05 -7.432305e-05
[4,] -3.237873e-07 -1.265085e-08 -7.432305e-05  1.398529e-04
> infocriteria(ixic.garch11.fit)

Akaike       -6.274916
Bayes        -6.268560
Shibata      -6.274918
Hannan-Quinn -6.272662
newsimpact(ixic.garch11.fit)
\$zy
[1] 0.0090968121 0.0087377033 0.0083859234 0.0080414722
[5] 0.0077043497 0.0073745560 0.0070520910 0.0067369548
[9] 0.0064291473 0.0061286686 0.0058355186 0.0055496974
[13] 0.0052712050 0.0050000412 0.0047362063 0.0044797000
[17] 0.0042305226 0.0039886738 0.0037541539 0.0035269626
[21] 0.0033071002 0.0030945664 0.0028893615 0.0026914852
[25] 0.0025009377 0.0023177190 0.0021418290 0.0019732678
[29] 0.0018120353 0.0016581316 0.0015115566 0.0013723104
[33] 0.0012403929 0.0011158041 0.0009985442 0.0008886129
[37] 0.0007860104 0.0006907367 0.0006027917 0.0005221755
[41] 0.0004488880 0.0003829292 0.0003242992 0.0002729980
[45] 0.0002290255 0.0001923817 0.0001630667 0.0001410805
[49] 0.0001264230 0.0001190942 0.0001190942 0.0001264230
[53] 0.0001410805 0.0001630667 0.0001923817 0.0002290255
[57] 0.0002729980 0.0003242992 0.0003829292 0.0004488880
[61] 0.0005221755 0.0006027917 0.0006907367 0.0007860104
[65] 0.0008886129 0.0009985442 0.0011158041 0.0012403929
[69] 0.0013723104 0.0015115566 0.0016581316 0.0018120353
[73] 0.0019732678 0.0021418290 0.0023177190 0.0025009377
[77] 0.0026914852 0.0028893615 0.0030945664 0.0033071002
[81] 0.0035269626 0.0037541539 0.0039886738 0.0042305226
[85] 0.0044797000 0.0047362063 0.0050000412 0.0052712050
[89] 0.0055496974 0.0058355186 0.0061286686 0.0064291473
[93] 0.0067369548 0.0070520910 0.0073745560 0.0077043497
[97] 0.0080414722 0.0083859234 0.0087377033 0.0090968121

\$zx
[1] -0.300000000 -0.293939394 -0.287878788 -0.281818182
[5] -0.275757576 -0.269696970 -0.263636364 -0.257575758
[9] -0.251515152 -0.245454545 -0.239393939 -0.233333333
[13] -0.227272727 -0.221212121 -0.215151515 -0.209090909
[17] -0.203030303 -0.196969697 -0.190909091 -0.184848485
[21] -0.178787879 -0.172727273 -0.166666667 -0.160606061
[25] -0.154545455 -0.148484848 -0.142424242 -0.136363636
[29] -0.130303030 -0.124242424 -0.118181818 -0.112121212
[33] -0.106060606 -0.100000000 -0.093939394 -0.087878788
[37] -0.081818182 -0.075757576 -0.069696970 -0.063636364
[41] -0.057575758 -0.051515152 -0.045454545 -0.039393939
[45] -0.033333333 -0.027272727 -0.021212121 -0.015151515
[49] -0.009090909 -0.003030303  0.003030303  0.009090909
[53]  0.015151515  0.021212121  0.027272727  0.033333333
[57]  0.039393939  0.045454545  0.051515152  0.057575758
[61]  0.063636364  0.069696970  0.075757576  0.081818182
[65]  0.087878788  0.093939394  0.100000000  0.106060606
[69]  0.112121212  0.118181818  0.124242424  0.130303030
[73]  0.136363636  0.142424242  0.148484848  0.154545455
[77]  0.160606061  0.166666667  0.172727273  0.178787879
[81]  0.184848485  0.190909091  0.196969697  0.203030303
[85]  0.209090909  0.215151515  0.221212121  0.227272727
[89]  0.233333333  0.239393939  0.245454545  0.251515152
[93]  0.257575758  0.263636364  0.269696970  0.275757576
[97]  0.281818182  0.287878788  0.293939394  0.300000000

\$yexpr
expression(sigma[t]^2)

\$xexpr
expression(epsilon[t - 1])
signbias(ixic.garch11.fit)
t-value         prob sig
Sign Bias             2.5814648      9.873929e-03 ***
Negative Sign Bias  0.3395914  7.341823e-01
Positive Sign Bias  2.1695032    3.010396e-02  **
Joint Effect       27.2806127        5.141403e-06 ***
fitted(ixic.garch11.fit)
#obtain the fitted data series
residuals(ixic.garch11.fit)
uncvariance(ixic.garch11.fit)
[1] 0.0001312744
> uncmean(ixic.garch11.fit)
[1] 0.0007437932
> ni.garch11 <- newsimpact(ixic.garch11.fit)
> plot(ni.garch11\$zx, ni.garch11\$zy, type="l", lwd=2, col="blue",
+      main="GARCH(1,1) - News Impact", ylab=ni.garch11\$yexpr, xlab=ni.
+      garch11\$xexpr)

Error: unexpected symbol in:
"     main="GARCH(1,1) - News Impact", ylab=ni.garch11\$yexpr, xlab=ni.
garch11"
> plot(ni.garch11\$zx, ni.garch11\$zy, type="l", lwd=2, col="blue",main="GARCH(1,1) - News Impact", ylab=ni.garch11\$yexpr, xlab=ni.garch11\$xexpr)
> egarch11.spec = ugarchspec(variance.model = list(model="eGARCH",garchOrder=c(1,1)), mean.model = list(armaOrder=c(0,0)))
> ixic.egarch11.fit = ugarchfit(spec=egarch11.spec, data=ret)
> coef(icix.egarch11.fit)