Integrated Risk Management: Enterprise Risk Frameworks

Module: Fintech AI | Difficulty: Advanced

Risk Categories

• Market risk
• Credit risk
• Operational risk
• Liquidity risk

Risk Aggregation

unless risks are perfectly correlated.

Copula for Risk Aggregation

Economic Capital

import numpy as np

class EnterpriseRisk:
    def __init__(self):
        self.risk_categories = {}
    def add_risk(self, name, distribution):
        self.risk_categories[name] = distribution
    def aggregate_var(self, confidence=0.99, n_simulations=10000):
        # Monte Carlo aggregation
        losses = np.zeros(n_simulations)
        for name, dist in self.risk_categories.items():
            losses += dist.sample(n_simulations)
        return np.percentile(losses, confidence * 100)
    def diversification_benefit(self):
        individual_vars = sum(np.percentile(d.sample(10000), 99)
                             for d in self.risk_categories.values())
        portfolio_var = self.aggregate_var()
        return 1 - portfolio_var / individual_vars

Research Insight: Risk aggregation reveals the importance of correlation assumptions. The 2008 crisis showed that diversification benefits disappear when correlations increase during stress. Copula models that capture tail dependence provide more realistic risk estimates.