yatshunlee/caviar

Measure market risk by CAViaR model

Evaluate CAViaR by Quantile Regression

This is a group project of SDSC6013 Topics in Financial Engineering and Technology at City University of Hong Kong (CityU). We built a value-at-risk model directly modeling the quantile return directly by referring the paper CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles by Engle and Manganelli (2004).

Disclaimer

As I found the original optimization approach is computational costly, I have modified a bit the box constraints as well as the starting approach (the initial guess/start of the estimated parameters). For details, you may want to take a look on the documentation (you may also easily change the setting back accordingly in the source code) or the presentation.pdf about the experiments and results. Although the differences are insignificant, please use the package caviar with caution.

Known Issues

You are welcome to report bug in https://github.com/yatshunlee/CAViaR-Project/issues. :)

Quick Summary

We constructed two libraries: caviar and var_tests to model the value at risk and backtest the VaR estimate. For presentation, we constructed a dashboard to showcase how it can be possibly applied. If you are looking for some inspiration, we strongly suggest to take a look of the documentation below and the code in notebook-example.

Libraries:

1. CAViaR Model caviar
2. VaR Test var_tests

Demo Application:

• dashboard_caviar

Documentation:

• Click here

Main Author(s) / Maintainer(s) of caviar and var_tests:

1. Jasper Lee