Stochastic processes are used extensively throughout quantitative finance - for example, to simulate asset prices in risk models that aim to estimate key risk metrics such as Value-at-Risk (VaR), Expected Shortfall (ES) and Potential Future Exposure (PFE). Estimating the parameters of a stochastic processes - referred to as 'calibration' in the parlance of quantitative finance -usually involves:

• computing the distribution of price returns for a financial asset;
• deriving point-estimates for the mean and volatility of the returns; and then,
• solving a set of simultaneous equations to back-out the parameters of the process.

The purpose of this Python notebook is to demonstrate how Bayesian Inference and Probabilistic Programming (using PYMC3), is an alternative and more powerful approach that can be viewed as a unified framework for:

• exploiting any available prior knowledge on market prices (quantitative or qualitative);
• estimating the parameters of a stochastic process; and,
• naturally incorporating parameter uncertainty into risk metrics.

We use pipenv for managing project dependencies and Python environments (i.e. virtual environments). All of the direct packages dependencies required to run the code (e.g. NumPy for arrays/tensors and Pandas for DataFrames), as well as all the packages used during development (e.g. Jupyter and IPython for interactive console and sessions and serving notebooks), are described in the Pipfile. Their precise downstream dependencies are described in Pipfile.lock.

To get started with Pipenv, first of all download it - assuming that there is a global version of Python available on your system and on the PATH, then this can be achieved by running the following command,

pip3 install pipenv

Pipenv is also available to install from many non-Python package managers. For example, on OS X it can be installed using the Homebrew package manager, with the following terminal command,

brew install pipenv

For more information, including advanced configuration options, see the official pipenv documentation.

Make sure that you're in the project's root directory (the same one in which the Pipfile resides), and then run,

pipenv install --dev

This will install all of the direct project dependencies as well as the development dependencies (the latter a consequence of the --dev flag).

In order to continue development in a Python environment that precisely mimics the one the project was initially developed with, use Pipenv from the command line as follows,

pipenv run python3

The python3 command could just as well be ipython3 or the JupterLab, for example,

pipenv run jupyter lab

This will fire-up a JupyterLab where the default Python 3 kernel includes all of the direct and development project dependencies. This is how we advise that the notebooks within this project are used.

Prepending pipenv to every command you want to run within the context of your Pipenv-managed virtual environment, can get very tedious. This can be avoided by entering into a Pipenv-managed shell,

pipenv shell

which is equivalent to 'activating' the virtual environment. Any command will now be executed within the virtual environment. Use exit to leave the shell session.