Financial Risk Modeling

Traditional risk models break during market regime changes because they rely on historical correlations that shift under stress. This notebook shows you how to build structural causal risk models with Fount that capture the mechanisms driving market behavior ΓÇö not just statistical patterns. You'll model the causal relationships between macroeconomic factors, sector indicators, and asset-level risk, enabling your models to remain accurate during volatility spikes, interest rate changes, and geopolitical events.

What You'll Accomplish

Build VaR and CVaR models that stay calibrated through market regime changes
Identify which macroeconomic factors causally drive portfolio risk vs. spurious correlations
Stress-test portfolios against specific causal scenarios (rate hikes, oil shocks, credit events)
Detect early warning signals by monitoring causal pathways rather than lagging indicators

Prerequisites

Python 3.8+Fount API keyMarket data with factor exposures (CSV)

Code Preview

Financial-Risk-Modeling.py

# Python program to find Area of a circle 

def findArea(r): 
	PI = 3.142
	return PI * (r*r); 

# Driver method 
print("Area is %.6f" % findArea(5));

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