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RiskSim – Risk Simulation Framework

Quantitative risk management was among the subjects that interested me most during my master's studies. As a student assistant, I reimplemented three risk simulation methods (Monte Carlo, variance–covariance, and historical simulation) in Python, porting the Excel examples from the lectures.

A year later, I presented that implementation to students in my first lecture.

I have now revisited the project: RiskSim is a Python package with an object-oriented design and a Streamlit interface in place of the original Jupyter notebooks, distributed as a Docker image.

What RiskSim does

RiskSim models dependencies within a portfolio and computes risk measures such as:

  • Value-at-risk (VaR) – the loss that is not exceeded with a given probability over a stated horizon
  • Conditional VaR (CVaR) – the expected loss beyond the VaR
  • Power spectral risk measure – a coherent spectral measure that weights the quantiles of the loss distribution by a decreasing risk-aversion function

Key features

  • Three integrated risk modeling approaches: Monte Carlo (Gaussian copula), historical simulation, and variance–covariance
  • An interactive Streamlit dashboard for exploring dependencies, distributions, and risk measures
  • Quantification of the run-to-run variability of the Monte Carlo estimates
  • Flexible configuration of portfolio parameters, correlations, and simulation settings

Watch the demo

Watch the video

Availability

RiskSim is open source and is distributed as a Docker container.