Correlation Basics: Definitions, Applications and Terminology
Empirical Properties of Correlation: How do Correlations Behave in the Real World?
The Pearson Correlation Model – Work of the Devil?
Cointegration – A Superior Concept to Correlation?
Financial Correlation Modelling – Bottom-up Approaches
Valuing CDOs with the Gaussian Copula – What Went Wrong?
The One-Factor Gaussian Copula Model – Too Simplistic?
Financial Correlation Models – Top-Down Approaches
Stochastic Correlation Models
Quantifying Market Correlation Risk
Quantifying Credit Correlation Risk
Hedging Correlation Risk
Correlation Trading Strategies – Opportunities and Limitations
Credit Value at Risk under Basel III – Too Simplistic?
Basel III and XVAs
Fundamental Review of the Trading Book
The Future of Correlation Modelling
Answers to Questions and Problems in Correlation Risk Modelling and Management
“Anything that relies on correlation, is charlatanism”
– Nassim Taleb
In this chapter we show that, contrary to common beliefs, financial correlations display statistically significant and expected properties.
HOW DO EQUITY CORRELATIONS BEHAVE IN A RECESSION, NORMAL ECONOMIC PERIOD OR STRONG EXPANSION?
In our study, we observed daily closing prices of the 30 stocks in the Dow Jones Industrial Average (Dow) from January 1972 to July 2017. This resulted in 11,214 daily observations of the Dow stocks and hence 11,214 × 30 = 336,420 closing prices. We built monthly bins and derived 900 correlation values (30 × 30) for each month, applying the Pearson correlation approach (see Chapter 3 for details). Since we had 534 months in the study, altogether we derived 534 × 900 = 480,600 correlation values.
The composition of the Dow is changing in time, with successful stocks being input into the Dow and unsuccessful stocks being removed. Our study comprises the Dow stocks that represent the Dow at each particular point in time.
Figure 2.1 shows the 534 monthly averaged correlation levels: we created monthly 30 by 30 bins of the Dow stock returns from 1972 to 2017, derived