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...l data using a volatility model to estimate the VaR. So in case of a normal distribution, this would be very easy, I assume the returns to follow a normal distribution and calculate a volatility forecast for each day, so I have sigma_1,sigma_2,...,sigma_n,. I can calculate the VaR via (mu constant, z_alpha quantile of standard normal): VaR_(alpha,t)=mu+sigma_t * z_alpha. This is in case, I have losses, so I look at the right tail. So for each day I have a normal density with a constant mu but a different sigma corrensponding to the volatility model. Let's assume a very simple volatility model, e....

Hi, How do we get the value of a chi square as we usually look up on the table on our text book? i.e. Chi-square(0.01, df=8), the text book table gives 20.090 > dchisq(0.01, df=8) [1] 1.036471e-08 > pchisq(0.01, df=8) [1] 2.593772e-11 > qchisq(0.01, df=8) [1] 1.646497 > nono of them give me 20.090 Thanks, cruz

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares