What to do with divergences in my case

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.

Weekly (size=7)

Is my posterior dist. valid with the prior values given by the example?

Parameters from example:

si∼N(si−1, σ^−2)
log(yi)∼ t(ν,0,exp(−2si))

Thanks and Regards.

Monthly (size=24)

Yearly (size=244)

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 size= indicates?)

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.

We have a small example on GARCH model https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/garch_example.py