Risk Modelling Part 3: Implementation

Series: Introduction to Risk Modelling (Part 3 of 3)

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Putting It Together

Armed with the theoretical foundations and data sources, we can now implement a basic risk model.

A Simple Loss Model

import numpy as np

def simulate_losses(n_scenarios=10000, 
                    mean_hazard=0.1, 
                    exposure=1000000,
                    vulnerability=0.3):
    """
    Simple Monte Carlo loss simulation.
    
    """
    # Simulate hazard intensity (e.g., flood depth)
    hazard = np.random.exponential(mean_hazard, n_scenarios)
    
    # Calculate damage ratio based on vulnerability
    damage_ratio = np.minimum(hazard * vulnerability, 1.0)
    
    # Compute losses
    losses = exposure * damage_ratio
    
    return losses

# Run simulation
losses = simulate_losses()

# Key statistics
print(f"Expected Loss: ${losses.mean():,.0f}")
print(f"VaR 99%: ${np.percentile(losses, 99):,.0f}")
print(f"Max Loss: ${losses.max():,.0f}")

Expected Loss: $29,955
VaR 99%: $138,687
Max Loss: $250,859

Key Implementation Decisions

When building production risk models, consider:

1. Correlation structure - How do risks aggregate?
2. Tail dependence - Do extreme events cluster?
3. Computational efficiency - Can you run enough scenarios?
4. Validation - How do you test the model?

Further Reading

This series provides a foundation. For deeper dives:

Linked from

• Risk Modelling Part 2: Data