Correlation Analysis Documentation
What is Correlation Analysis?
Correlation analysis is the study of how financial assets move together over time. When we say two assets are “correlated,” we mean their price movements tend to follow similar patterns - when one goes up, the other tends to go up as well (positive correlation), or when one goes up, the other tends to go down (negative correlation).
Understanding these relationships is fundamental to modern finance because asset correlations determine how much diversification benefit you can achieve in a portfolio, how effective your hedging strategies will be, and how much risk you're actually taking when you think you're spreading it across “different” investments.
Real-World Examples
Portfolio Diversification: An investor holding both technology stocks and utility stocks expects them to move somewhat independently. However, during the 2008 financial crisis, correlations across all sectors increased dramatically, reducing the diversification benefit when it was needed most.
Currency Hedging: A multinational company needs to hedge its exposure to the Euro. The effectiveness of using currency forwards depends critically on the correlation between the Euro exchange rate and the company's business performance in European markets.
Risk Management: A bank's risk model assumes certain correlations between different asset classes to calculate Value-at-Risk. If these correlations change unexpectedly, the bank could face much larger losses than anticipated.
This documentation will take you from these basic concepts through sophisticated mathematical models that can capture and forecast how correlations evolve over time, helping you make better financial decisions in an interconnected world.
Correlation Models
Fundamental Models
Core volatility modeling approaches for standard analysis
Use Cases: Standard volatility forecasting, risk measurement, and VaR calculation
Specialized Applications
Domain-specific models for advanced scenarios and alternative approaches
Use Cases: Credit risk modeling, multi-factor analysis, fat-tail distributions, multiplicative error models, and alternative parameterizations