Stress Testing

The Stress Testing module provides tools for assessing the impact of extreme market conditions on financial portfolios or instruments.

Functions

Usage Example

Here’s an example of how to use the stress testing functionality:

import torch
from torchquantlib.core.risk.market_risk.stress_testing import perform_stress_test

# Define a sample portfolio
portfolio = torch.tensor([100000.0, 50000.0, 75000.0])  # Holdings in three assets

# Define stress scenarios
scenarios = {
    "severe_recession": {"asset1": -0.3, "asset2": -0.4, "asset3": -0.25},
    "market_crash": {"asset1": -0.5, "asset2": -0.6, "asset3": -0.55},
    "currency_crisis": {"asset1": -0.2, "asset2": -0.1, "asset3": -0.4}
}

# Perform stress test
results = perform_stress_test(portfolio, scenarios)

# Print results
for scenario, impact in results.items():
    print(f"Scenario: {scenario}")
    print(f"Portfolio impact: ${impact:.2f}")
    print(f"Percentage change: {(impact / portfolio.sum().item()) * 100:.2f}%")
    print()

Notes

• Stress testing helps identify potential vulnerabilities in a portfolio under extreme market conditions.

• The scenarios should be carefully chosen to reflect realistic but severe market events.

• Regular stress testing is crucial for robust risk management and regulatory compliance.

See Also

• scenario_analysis for related scenario-based risk assessment techniques.