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Bank Run Simulation and Risk Calculation Library

This library contains two Python scripts for performing bank run simulations and various risk calculations related to banking operations. bank_run.py

This script contains the BankRun class, which is used to simulate a bank run. The class is initialized with the bank's total capital, risk-weighted assets (RWA), initial cash on hand, and the number of simulations to run.

The main method in this class is bank_run_sim, which performs the bank run simulations. In each simulation, a random amount of deposit withdrawals is sampled, and the bank tries to meet this demand either from its available capital or by selling off assets at a discount. The bank's remaining capital and assets, CET1 ratio, and success (whether the CET1 ratio stayed above 8%) are recorded for each simulation. The results of the simulations are stored in a pandas DataFrame for easy manipulation and analysis. utility.py This script provides several functions for calculating various risk measures, as well as some utility functions:

  • rwa(credit_risk, market_risk, op_risk): Returns the total risk-weighted assets, which is the sum of the risk-weighted values of credit, market, and operational assets.

  • cet1(cet1_capital, rwa): Calculates the CET1 (Common Equity Tier 1) ratio, a key measure of a bank's financial strength.

  • calc_credit_risk(credit_list): Calculates the total credit risk from a list of credit assets.

  • calc_market_risk(value_at_risk, s_var): Calculates the market risk based on the value at risk and the stressed value at risk.

  • csv_to_dataframe(folder_path): Converts all CSV files in a folder to pandas DataFrames and stores them in a dictionary. It assumes that the CSV files contain numeric data with possibly dollar signs and commas, which are removed during the conversion. Missing values are filled with zero.

  • is_num(x): Checks if a variable is a number (int or float).

Technologies

  • Operating Systems: Mac OS, Windows

  • Programming Languages: Python

  • Frameworks: Python, JupyterLab, VS code, Streamlit

  • Libraries: Pandas, Numpy, Pathlib, Streamlit, Rich

Installation

  • Step 1: Install jupyter lab or VS code (up to preference)

  • Step 2: Install all packages/libraries required for the code to run. Refer to the libraries above and the picture below, repeat until all libraries have been installed.

Screenshot 2023-05-11 164451

  • Step 3: Grab the repository, navigate to your folder of choice (can be a premade or a new folder anywhere on your computer), and clone the repository. Refer to the photos

remakeprojectgitclone

Usage

  • Step 1: import the required classes and functions:
python

from bank_run import BankRun from utility import rwa, cet1, calc_credit_risk, calc_market_risk, csv_to_dataframe, is_num
  • Step 2: you can create an instance of BankRun and run simulations:
python

br = BankRun(total_capital=1000000, rwa=500000, initial_cash_on_hand=200000, num_iterations=1000) results = br.bank_run_sim()
  • The utility functions can also be used separately:
python

total_rwa = rwa(credit_risk=200000, market_risk=150000, op_risk=150000) cet1_ratio = cet1(cet1_capital=300000, rwa=total_rwa)
  • The csv_to_dataframe function can be used to load data from CSV files:
python

dfs = csv_to_dataframe(folder_path='path/to/your/csv/folder')

Note: This is a simple usage guide. The actual usage may vary depending on the specific details of the data and the requirements of the analysis.

  • After all of this you can also choose to run the code simply through streamlit or Jupyterlab. In order to do so you need to open a terminal and navigate into your project folder C:user/desktop/project1 or wherever you may have your project stored. Once there you need to simply type in Jupyter lab or Streamlit run wells_fargo_bank_run.pyand the website should open perfectly fine. Note:you dont have to open the streamlit with that .py, it can be any of the 4 simulations.

Contributors