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Market Risk & Stress Testing in Python - OneDDL - 12-01-2026
![[Obrazek: 6f9413ccb832e630ddfcb306cc64fa80.avif]](https://i126.fastpic.org/big/2026/0112/80/6f9413ccb832e630ddfcb306cc64fa80.avif)
Free Download Market Risk & Stress Testing in Python
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 3m | Size: 493 MB
Build Value at Risk (VaR), tail risk, and stress testing workflows used by banks and institutional risk teams
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.
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 tableThroughout 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.
Homepage
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