Financial Correlations - Modeling, Trading, Risk Management and AI

Financial Correlations - Modeling, Trading, Risk Management and AI

Pre-conference workshop: July 16th

Financial Correlations - Modeling, Trading, Risk Management and AI

Led by:

Gunter Meissner, CEO, DerSoft and Adjunct Professor of MathFinance, COLUMBIA UNIVERSITY & NYU COURANT  

 

08:30

REGISTRATION & REFRESHMENTS 

09:0

Introduction: What are Financial Correlations and why are they Critical in Finance?

  • Investments and Correlation – A new Correlation based Portfolio Performance measure
  • Trading and Correlation
  • Risk Management and Correlation
  • The Global Financial Crisis and Correlation
  • Regulation and Correlation
  • AI and Correlation

Empirical Properties of Correlation: How do Correlations behave in the Real World?

  • How do equity correlations behave in a recession, normal economic period, and economic expansion?
  • Do equity correlations exhibit Mean Reversion?
    Excel Exercise: Programming a Mean Reversion Test of a Time Series          
  • Do equity correlations exhibit Autocorrelation?
     Excel Exercise: Programming Autocorrelation of a Time Series.
  • Is Mean Reversion the ‘reverse property’ of Autocorrelation?
  • How are equity correlations distributed?
10.30 MORNING BREAK
11.00

How can we Quantify Financial Correlations?

  • An Overview of 15 Models
    The most Critical Models:
  • The popular Pearson correlation model – Work of the Devil?
    Excel Exercise: Programming the Pearson multiple Regression Function and 
  • Correlation Parameters
  • Correlating Brownian motions (Heston 1993) and Extensions
    Excel Exercise: Programming the Heston Correlation Model
  • Copulas (Sklar 1959, Vasicek 1987, Li 2000)
    Excel Exercise: Programming the Gaussian Copula Model
  • Limitations of Copulas: Should we apply Copulas in Financial modelling?
  • Conclusion: Will there be a Black-Scholes-Merton Correlation model, which will dominate Correlation modelling?

Cointegration – A Superior Model to Correlation?

  • Basics: Stationary process and Integration to the order d
  • Application of Cointegration in Finance: Tracking an Index, Pairs Trading, Cost-effective Hedging
  • A practical Approach to Implementing Cointegration
  • Granger Causality: True causality or just Granger causality?
  • Conclusion: Is Cointegration superior to Pearson correlation?
12.30 Lunch
1.30

Should we model Financial Correlations with a Stochastic Process?           

  • Recap from Chapter 2: Empirical Properties of Financial Correlations 
  • Which ingredients should a Stochastic Process for Financial Correlations have?
    a) Should we include Mean Reversion
    b) Should we bound the Process?
    Excel Exercise: Programming White Noise, the Geometric Brownian Motion with Jumps and the bounded Jacobi process
  • The Buraschi et al 2010 model and the Lu and Meissner 2018 model

Correlation Trading

  • Empirical Correlation Trading
  • Pairs Trading
  • Multi-asset Options
  • Structured Products
  • Correlation Swaps
  • Dispersion trading
  • Which Trading Strategy is the most promising?
3.00

How to quantify Market Correlation Risk and Credit Correlation Risk

  • The Correlation Risk Parameters Cora and Gora
  • Applying Cora and Gora to Market Risk Modeling, i.e. VaR and ES 
  • Applying Cora and Gora to Credit Risk Modeling, i.e. to CDS and CDOs
  • The 2007-2009 CDO disaster. Are Cora and Gora of Copulas to blame?     

Correlation and AI

  • What fuels AI?
  • Will AI funds outperform the market?
  • Neural Networks and Correlation
  • Ridge and Lasso Regression – Pros and Cons
  • Ensemble learning – Consensus Clustering, Bayesian optimal classifier, Boosting