R is a programming language possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithm, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R,but many large companies also use R programming language, including Uber, Google, Airbnb, Facebook and so on

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Showing posts with label How to use Vector autoregressive models (VAR) in the stock market. Show all posts
Showing posts with label How to use Vector autoregressive models (VAR) in the stock market. Show all posts

Thursday, August 1, 2019

How to use Vector autoregressive models (VAR) in the stock market

How to use Vector autoregressive models (VAR) in the stock market.

We get data MSFT from '2004-01-02', to='2019-07-30'
Draw chartSeries(ClCl(MSFT))
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 MSFT.ret <- diff(log(Ad(MSFT)))
> MSFT.M <- to.monthly(MSFT.ret)$MSFT.ret.Close
ret <- dailyReturn(Cl(MSFT), 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")
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> par(mfrow=c(1,1))
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')


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