Intermediate
Case Study: Using Monte Carlo Simulation to Estimate Portfolio Value at Risk
An end-to-end case study for quant analyst aspirants, prop trading applicants, and systematizing traders. You follow one ₹1 crore portfolio of Nifty 50 stocks and a gold ETF from raw NSE price data to a finished risk memo: estimating the covariance matrix, correlating random shocks with Cholesky decomposition, choosing between normal and fat-tailed distributions, simulating 10,000 scenarios in Python, extracting VaR and Expected Shortfall, then backtesting, stress testing, and decomposing the result the way a real risk desk would.
Value at RiskMonte Carlo SimulationCovariance and CorrelationCholesky DecompositionExpected ShortfallBacktestingStress TestingComponent VaR
MODULES
4
DURATION
4 Hours
TRACK
Quantitative Finance
What You'll Master
Build a Monte Carlo VaR model for a real multi-asset Indian portfolio from scratch
Estimate volatilities and a covariance matrix from NSE price history
Use Cholesky decomposition to generate correlated return scenarios
Compare normal and Student's t shocks and see how fat tails change the answer
Extract 95% and 99% VaR and Expected Shortfall from a simulated loss distribution
Backtest, stress test, and decompose VaR by holding
Present the result in a clear risk memo a portfolio manager can act on
Access Level
LEARNER
Everything included
Full Text Playbooks
Actionable Exercises
Mobile Reading Mode
Lifetime Updates
Curriculum Breakdown
Chapter 1: The Case: A ₹1 Crore Portfolio and the Question the Risk Desk Asked
4 Lessons▶
The Brief: Meet the Portfolio and the Risk Question10 min read
▶
VaR in One Page: Confidence, Horizon, and What the Number Does Not Say11 min read
▶
Why Monte Carlo for This Portfolio: Where Historical and Parametric VaR Fall Short12 min read
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Gathering the Data: NSE Price History, Corporate Action Adjustments, and Log Returns12 min read