Scenario Analysis

The Scenario Analysis module provides tools for evaluating the impact of various market scenarios on financial portfolios or instruments.

Functions

Usage Example

Here’s an example of how to use the scenario analysis functionality:

import torch
from torchquantlib.core.risk.market_risk.scenario_analysis import run_scenario_analysis

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

# Define scenarios
scenarios = {
    "base_case": {"asset1": 0.05, "asset2": 0.03, "asset3": 0.04},
    "bull_market": {"asset1": 0.15, "asset2": 0.12, "asset3": 0.10},
    "bear_market": {"asset1": -0.10, "asset2": -0.08, "asset3": -0.12},
    "sector_rotation": {"asset1": -0.05, "asset2": 0.10, "asset3": 0.02}
}

# Run scenario analysis
results = run_scenario_analysis(portfolio, scenarios)

# Print results
for scenario, outcome in results.items():
    print(f"Scenario: {scenario}")
    print(f"Portfolio value: ${outcome['portfolio_value']:.2f}")
    print(f"Absolute change: ${outcome['absolute_change']:.2f}")
    print(f"Percentage change: {outcome['percentage_change']:.2f}%")
    print()

Notes

• Scenario analysis helps in understanding the potential outcomes of different market conditions.

• It’s important to consider a wide range of scenarios, including both positive and negative outcomes.

• The results can be used for strategic decision-making and risk mitigation planning.

See Also

• stress_testing for assessing the impact of extreme market conditions.