This repository contains several IPython notebooks that explore various
methods of time series analysis. In particular, we are looking at financial
time series, which (not untypically):
- show no (partial) autocorrelation but
- exhibit volatility bunching.
The three notebooks
- Daily
- Monthly
- Recently.
do the same thing but for differrent time horizons, as the names imply.
Sepcifically, we use Kevin Shepard's excellent arch-package to find
(within limits) the best of various types of autoregressive conditionally heteroscedastic (ARCH) models to the returns.
Most of the heavy lifting in finding the optimal model parameters is outsourced
from the notebooks into a decently documented helpers package.
In the Bayesian notebook, we explore the possibilities and models offered by
Ross Taylor's fascinating PyFlux-package. As there
is a lot to play with, optimal model selection can take some time. To speed
things up considerably, we leverage the python multiprocessing module and
crunch through several models at the same time, in parallel. This is again
conveniently outsourced into the helpers package.
Everything seems to run smoothly with a fairly fresh install of Anaconda 4.3.
Other than arch or PyFlux, there are no additional dependencies.
This is for python 3.6.
The financial time series I used for these notebooks are, unfortunatley, proprietary and cannot be released under the same license as the notebooks. The notebooks themselves, however, should be really easy to adapt to any time series.