Market Panics: Correlation Dynamics, Dispersion and Tails

Lisa Borland

Contents

Introduction to 'Lessons from the Financial Crisis'

1.

The Credit Crunch of 2007: What Went Wrong? Why? What Lessons Can be Learned?

2.

Underwriting versus Economy: A New Approach to Decomposing Mortgage Losses

3.

The Shadow Banking System and Hyman Minsky’s Economic Journey

4.

The Collapse of the Icelandic Banking System

5.

The Quant Crunch Experience and the Future of Quantitative Investing

6.

No Margin for Error: The Impact of the Credit Crisis on Derivatives Markets

7.

The Re-Emergence of Distressed Exchanges in Corporate Restructurings

8.

Modelling Systemic and Sovereign Risks

9.

Measuring and Managing Risk in Innovative Financial Instruments

10.

Forecasting Extreme Risk of Equity Portfolios with Fundamental Factors

11.

Limits of Implied Credit Correlation Metrics Before and During the Crisis

12.

Another view on the pricing of MBSs, CMOs and CDOs of ABS

13.

Pricing of Credit Derivatives with and without Counterparty and Collateral Adjustments

14.

A Practical Guide to Monte Carlo CVA

15.

The Endogenous Dynamics of Markets: Price Impact, Feedback Loops and Instabilities

16.

Market Panics: Correlation Dynamics, Dispersion and Tails

17.

Financial Complexity and Systemic Stability in Trading Markets

18.

The Martingale Theory of Bubbles: Implications for the Valuation of Derivatives and Detecting Bubbles

19.

Managing through a Crisis: Practical Insights and Lessons Learned for Quantitatively Managed Equity Portfolios

20.

Active Risk Management: A Credit Investor’s Perspective

21.

Investment Strategy Returns: Volatility, Asymmetry, Fat Tails and the Nature of Alpha

The financial crisis of 2008 prompted us to explore statistical signatures of market panic. In particular, we focus on the properties of the cross-sectional distribution of returns and propose a model for the cross-sectional dynamics. On daily timescales, we find a significant anti-correlation between dispersion and cross-sectional kurtosis such that dispersion is high but kurtosis is low in times of panic, and vice versa in normal times. This appears counter-intuitive at first, because times of panic have wild returns, so-called “black swans” (Taleb 2007) and rare events, which are typically associated with fat-tailed distributions (high kurtosis). We also find that the co-movement of stock returns increases in times of panic. In order to model these dynamics, we have proposed (Borland 2009) a joint stochastic process to describe the behaviour of stocks across time as well as their interactions at each point in time. The basic idea is a simple model of self-organisation, such that there occurs a spontaneous phase transition to a highly correlated state at the onset of panic. We quantify this with a simple statistic, s, the normalised sum of signs of returns on a given day, which

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