Displaying 2 results from an estimated 2 matches for "sigma_n".
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2009 Feb 28
0
Implementation of quasi-bayesian maximum likelihood estimation for normal mixtures
...existing packages including the quasi-bayesian mle, I
have to write my own function. Unfortunately, I have absolutely no
experience in doing this.
If you're not familiar with the QB-MLE, I attached the formula as pdf.
The idea is to extend the usual MLE with prior beliefs about the values
sigma_n and sigma_b. My priors are already included in the code below. I
intend to try a mixture of two normal distributions with same mean, and
variances 1 and 5 as starting values.
This is what I've done so far:
> R <-read.table("C:\\...\\rendite.txt", header=F)
> qbmle <-...
2013 Apr 07
0
Fitting distributions to financial data using volatility model to estimate VaR
...ssible.
I want to fit a distribution to my financial 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....