MODELING RANDOMLY WALKING VOLATILITY WITH CHAINED GAMMA DISTRIBUTIONS

Volatility clustering is a common phenomenon in financial time series. Typically, linear models
can be used to describe the temporal autocorrelation of the (logarithmic) variance of returns. Considering the difficulty in estimating this model, we construct a Dynamic Bayesian Network, which
utilizes the conjugate prior relation of normal-gamma and gamma-gamma, so that its posterior form
locally remains unchanged at each node. This makes it possible to find approximate solutions using variational methods quickly. Furthermore, we ensure that the volatility expressed by the model
is an independent incremental process after inserting dummy gamma nodes between adjacent time
steps. We have found that this model has two advantages: 1) It can be proved that it can express
heavier tails than Gaussians, i.e., have positive excess kurtosis, compared to popular linear models. 2) If the variational inference(VI) is used for state estimation, it runs much faster than Monte
Carlo(MC) methods since the calculation of the posterior uses only basic arithmetic operations. And
its convergence process is deterministic.
We tested the model, named Gam-Chain, using recent Crypto, Nasdaq, and Forex records of varying
resolutions. The results show that: 1) In the same case of using MC, this model can achieve comparable state estimation results with the regular lognormal chain. 2) In the case of only using VI, this
model can obtain accuracy that are slightly worse than MC, but still acceptable in practice; 3) Only
using VI, the running time of Gam-Chain, under the most conservative settings, can be reduced to
below 20% of that based on the lognormal chain via MC.