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Market Risk & Stress Testing in Python
Rating: 4.9 out of 5(8 ratings)
43 students

Market Risk & Stress Testing in Python

Build Value at Risk (VaR), tail risk, and stress testing workflows used by banks and institutional risk teams
Created byPython for Finance Mastery
Last updated 1/2026
English

What you'll learn

  • Implement historical and parametric Value at Risk (VaR) models in Python and interpret their results
  • Analyze tail risk, stress scenarios, and drawdowns to understand losses beyond standard VaR
  • Backtest market risk models and evaluate their strengths and limitations using real market data.
  • Build a concise, decision-ready market risk report combining VaR, tail risk, stress tests, and drawdowns.

Course content

6 sections • 15 lectures • 1h 3m total length

  • What This Course Covers (and What It Doesn’t)4:58
  • Final Market Risk Report (What We Will Build)3:38

Requirements

  • Basic Python knowledge is recommended. Familiarity with financial markets is helpful, but no prior risk management experience is required.

Description

Market Risk & Stress Testing in Python is a practical, career-focused course that teaches you how banks and institutional risk teams measure, report, and communicate market risk.

You will build end-to-end Value at Risk (VaR), tail risk, and stress testing workflows in Python, starting from raw market data and finishing with a professional risk report similar to those used in front office risk, market risk, and quantitative risk roles.

This course focuses on implementation, interpretation, and reporting — not trading strategies, alpha generation, or academic derivations.

You’ll learn how to:

• Load and prepare market data the way risk teams do
• Analyze return distributions and understand fat tails
• Build historical and parametric VaR models
• Backtest VaR and interpret exceptions for governance
• Measure tail losses beyond VaR
• Run hypothetical and historical stress scenarios
• Analyze drawdowns and worst-case periods
• Consolidate everything into a clear market risk summary table

Throughout the course, every concept is tied back to real-world usage, including risk limits, reporting cycles, management decision support, and model limitations.

This course is ideal for students, analysts, quants, and developers who want job-relevant Python skills in market risk, stress testing, and financial risk management — the exact skills demanded by banks, asset managers, and institutional risk teams.

Who this course is for:

  • This course is for Python users, analysts, and finance professionals who want a practical, hands-on introduction to market risk and stress testing.

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