Hello, I have divergence issue and I think I need some reparameterization. I would like to perform Bayesian inference with stock price. Attached are posterior outcome from weekly, monthly and yearly data.
Is my posterior dist. valid with the prior values given by the example?
Parameters from example:
Thanks and Regards.
I tried to adjust the parameter values of lambda to 1./1 for nu or v and sigma to get uniform prior after knowing that the exponential distribution is a special form of gamma distribution with alpha = 1 from PyMC2 documentation. Since the beta corresponds with the lambda, so I adjusted the value of beta or lambda to 1. I got no divergence after the adjustment except for the weekly data possibly because of low amount of samples. Anyway, I dealt with that by increasing target_accept. I hope my solution should be reasonable enough.
I gather that the number of data point is different in these cases? and there are more data point in Yearly? (is that what the
My suggestion would be try changing the prior of sigma and nu to HalfNormal or HalfCauchy.
Thanks for the reply. Yup, the size indicates the number of data point. Anyway, I have stuck with Yearly data because it is more accurate. By the way, may I know is the stochastic volatility model shown in example a GARCH model?
Just realized that it isn’t the GARCH model, hopefully there will be documentation for GARCH model.