I already know this, I did a second post, where I mention this, but I have still problems with the implementation, especially in case of other distributions! But thanks for your answer.
2013/4/7 Patrick Burns <pbu...@pburns.seanet.com>: > There is an example of using the t distribution > for VaR in: > > http://www.portfolioprobe.com/2012/11/19/the-estimation-of-value-at-risk-and-expected-shortfall/ > > The trick is to know what the variance of the > distribution is for a given value of the degrees > of freedom. > > Pat > > > > On 06/04/2013 10:54, Stat Tistician wrote: >> >> Hi, >> I want to calculate the Value at Risk with using some distirbutions and a >> volatility model. >> I use the following data(http://uploadeasy.net/upload/cdm3n.rar) which are >> losses (negative returns) of a company of approx. the last 10 years. So I >> want to calculated the Value at Risk, this is nothing else than the >> quantile. Since I have losses I consider the right tail of the >> distribution. >> >> Consider a first simple example, I assume the returns to follow a normal >> distribution with mean zero and a volatility, which is estimated for each >> day with a volatility model. Let's use a simple volatility model: The >> empirical standard deviation of the last 10 days. So I calculate the >> standard deviation of the first ten days and this is my estimate for the >> 11th day and so on, until the end of my data. So I assume for each day a >> normal distribution with mean zero and a sigma estimated by the voaltility >> mdoel. So I use this estimated sigma to calculate the quantile, which >> gives >> me the Value at Risk. The code would be: >> >> volatility<-0 >> quantile<-0 >> for(i in 11:length(dat)){ >> volatility[i]<-sd(dat[(i-10):(i-1)]) >> } >> >> for(i in 1:length(dat)){ >> quantile[i]<-qnorm(0.975,mean=0,sd=volatility[i]) >> } >> # the first quantile value is the VaR for the 11th date >> >> #plot the volatility >> plot(c(1:length(volatility)),volatility,type="l") >> >> #add VaR >> lines(quantile,type="l",col="red") >> >> >> So in this case I understand everything and I can implement this. But now >> comes my problem: I want to use a t-distribution with paramters mu, nu and >> beta or even a generalized hyperbolic distribution. So in this case, I >> don't know how to plug in the estimates for sigma, since there is no sigma >> in the paramters? How can I implement the volatility model and e.g. the >> generalized hyperbolic distribution in this case to calculate the Value at >> Risk? >> >> Thanks >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > -- > Patrick Burns > pbu...@pburns.seanet.com > twitter: @burnsstat @portfolioprobe > http://www.portfolioprobe.com/blog > http://www.burns-stat.com > (home of: > 'Impatient R' > 'The R Inferno' > 'Tao Te Programming') ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
