Market Risk & Stress Testing in Python

Load and prepare market data in Python by fetching SPY prices, computing daily simple returns, and cleaning data to form the foundation for all risk models.

Compute historical VaR from the empirical return distribution at 95% and 99% confidence using 5th and 1st percentiles; convert to currency by multiplying by portfolio value, noting transparency and limitations.

Compute parametric VaR under a normal distribution using the sample mean and volatility, and compare it with historical VaR to discuss tail risk, fat tails, and backtesting.

Backtest a historical 95% VAR with a constant threshold by counting exceptions where realized losses exceed the threshold and compare the observed exception rate to the expected 5%.

Examine tail risk and how worst 1% losses exceed var. Learn how tail risk complements var and informs capital planning and stress testing during extreme market conditions.

Explore forward-looking stress testing by applying simple market shocks to quantify portfolio P&L, compare hypothetical scenarios with historical stress days, and see how stress testing complements VAR and tail risk.

Examine how drawdowns capture persistent declines from prior peaks, highlight the maximum drawdown of about 33.7%, and show how they complement VAR, tail risk, and stress testing.

Assemble the market risk report to unify value at risk, drawdowns, and stress testing. Compare historical and parametric VAR, worst 1% losses, and maximum drawdown to reveal exposure.

Explain how market risk reports guide portfolio managers, risk managers, risk committees, and senior management in monitoring exposure, limits, and drawdowns under scenarios with VaR, stress testing, and tail risk.

Explore limitations of market risk models, including distributional assumptions, fat tails, regime shifts, correlations under stress, and limits of value at risk, acknowledging model risk and monitoring with judgment.

Extend market risk modeling to multi-asset portfolios, incorporating correlation effects, diversification and concentration risk, and explore Monte Carlo methods with expected shortfall for tail losses.