Some Jupyter notebooks demonstrating various types of models for systemic financial risk, including indicator back-testing, network construction, network analytics, statistical stress-testing & economic models. Many of these can be run on Azure, when the required R & python packages are supported.
- https://notebooks.azure.com/ian-buckley/libraries/systemic-risk
- https://github.com/roguetrainer/systemic-risk
Name |
Language |
Package |
Author |
Description |
Publication |
|---|---|---|---|---|---|
| Py-ABCE--Tutorial.ipynb | Python | ABCE |
Taghawi-Nejad, Davoud | Agent-based complete economy tutorial. | + Taghawi-Nejad, Davoud, Rudy H. Tanin, R. Maria Del Rio Chanona, Adrián Carro, J. Doyne Farmer, Torsten Heinrich, Juan Sabuco, and Mika J. Straka. “ABCE: A Python Library for Economic Agent-Based Modeling.” In International Conference on Social Informatics, 17–30. Springer, 2017. |
| Py-neva--Network-based-stress-testing.ipynb | Python | neva |
Bardoscia, Marco | Network valuation in financial systems. Neva values equities of banks that hold cross-holding of debt. Several known contagion algorithms (e.g. Furfine, Eisenberg and Noe, and Linear DebtRank) are special cases of Neva. | + Barucca, Paolo, Marco Bardoscia, Fabio Caccioli, Marco D’Errico, Gabriele Visentin, Stefano Battiston, and Guido Caldarelli. “Network Valuation in Financial Systems.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, June 14, 2016. http://papers.ssrn.com/abstract=2795583. |
| Py-sfc_models.ipynb | Python | sfc_models |
Romanchuk, Brian | Stock-Flow Consistent (SFC) models in Python | + Romanchuk, Brian. An Introduction to SFC Models Using Python. BondEconomics, 2017. https://www.amazon.ca/Introduction-SFC-Models-Using-Python/dp/0994748094. |
| R-ccgarch.ipynb | R | ccgarch |
Nakatani, Tomoaki | Conditional Correlation GARCH models. Required for MES & SRISK measures. | + Nakatani, Tomoaki. Ccgarch: Conditional Correlation GARCH Models, 2014. https://cran.r-project.org/web/packages/ccgarch/index.html. |
| R-mFilter.ipynb | R | mFilter |
Balcilar, Mehmet | Miscellaneous time series filters e.g. for detrending financial time-series such as (early warning) indicators for systemic risk. | + Balcilar, Mehmet. MFilter: Miscellaneous Time Series Filters (version 0.1-3), 2007. https://cran.r-project.org/web/packages/mFilter/index.html. |
| R-systemicrisk--Bayesian-network-reconstruction.ipynb | R | systemicrisk |
Gandy, Axel | A toolbox for systemic risk based on liabilities matrices. A Bayesian approach to estimate the liabilities matrices where only row and column sums of the liabilities matrix. Alternative to entropic & other point estimate approaches. | + Gandy, Axel. CRAN - Package Systemicrisk. Accessed September 11, 2016. https://cran.r-project.org/web/packages/systemicrisk/index.html. |
| Sheppard--Python-for-econometrics--Example_GJR-GARCH.ipynb | Python | - |
Sheppard, Kevin | The example Estimating the Parameters of a GARCH Model, from Sheppards book: Python for Economics. Estimate the parameters of a GJR-GARCH(1,1,1) model by optimizing the log-likelihood function (quasi-maximum likelihood) with a constant mean. | + Sheppard, Kevin. Python for Econometrics, 2017. https://www.kevinsheppard.com/Python_for_Econometrics. |
| Eikon-API-Proxy.ipynb | Python | eikon |
Python scripts to request data from Thomson Reuters Eikon. |
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