1. The “Wolf” of Wall Street
Group Four
Members: Chan Yong Ming, Kwek Zi Wei Bernetta, Peh Ching Hui Timothy
Presenter: Chan Yong Ming

2. Assigned Reading
Salmon, Felix (2009). Recipe for Disaster: The Formula That Killed Wall Street. Wired Magazine 17 (3).

3. Outline Background Story of a Man Correlation The “Wolf” Implications
Uncertainty
What can be done?

4. http://static5. businessinsider

5. David X. Li Master’s Degree in Economics from Nankai University
MBA from Laval University in Quebec
Master's in actuarial science from Ontario's University of Waterloo
PhD in statistics from Ontario's University of Waterloo

6. David X. Li JPMorgan Chase Risk Metrics unit in 2000
Director and global head of credit derivatives research at Citigroup in 2003
Barclays Capital quantitative analytics team in 2004

7. David X. Li Illustration: David A. Johnson

8. Timeline 1997: Worked at Canadian Imperial Bank of Commerce
1960s: Grew up in rural China
Smart and intelligent; had multiple degrees
1997: Worked at Canadian Imperial Bank of Commerce
First financial career
2004: Worked at Barclays Capital
Headed credit quantitative analytics team
2008: Worked for China International Capital Corporation
headed up the credit quantitative analytics team

9. The “Formula”
David X. Li's Gaussian copula function as first published in 2000

10. The “Formula” For five years it was a breakthrough
Allowed hugely complex risks to be modelled with more ease and accuracy than ever before
Traders able to sell vast quantities of new securities, expanding financial markets to unimaginable levels
Example: Credit default swaps rose from $920 billion in 2001 to $62 trillion in 2007

11. Correlation
measure and describe the strength and direction of the relationship between two variables
Correlation NOT = Causation!!
Measuring Linear relationship
Coefficient denoted by “r”
r ranges from -1 to 1

12. Probability: 5%
If investors were trading securities based on the chances of those things happening only to Alice, they would all trade at more or less the same price.

13. 5% Divorced Witnessed >95% Wins 0%
r close to 0
Witnessed
>95%
r close to 1
Wins 0%
r close to 0

14. Correlation
Investors trading securities based on 1 person  more or less same price
Trading based on 2 people  correlations can vary a lot  prices fluctuate all over

15. http://www. clipartkid

16. The “Wolf” Formula Probability Both A and B Equality
Dangerously precise concept
Leaves no room for error
Distribution functions
Probabilities how long A and B are likely to survive
Not certainties & dangerous
Survival Times
Expected time of A and B
Copula
Individual probabilities associated with A and B
Gamma
Correlation parameter
Reduces Correlation to a single constant
Highly impossible concept that made Li’s formula special

17. Implications
The model fell apart and cracks started appearing early on
Financial markets began behaving in ways that users of Li's formula hadn't expected
Truth became apparent as defaults began to rise among borrowers with mortgages
House of cards that was built on leveraged debt began to collapse
2008: Ruptures in the financial system's foundation swallowed up trillions of dollars and put the survival of the global banking system in serious danger

18. Uncertainty Random Circumstances Outcome is uncertain
Use Probabilities, P-value and Hypothesis Testing to measure
We can only get a best estimate

19. Can we really blame him?
People were making so much money that warnings about its limitations were largely ignored
Li himself cautioned that his model relied on limited historic data so it could not capture all possibilities

20. Can we really blame him? Biostatistician
Former Associate Dean of Faculty of Statistics in University of Waterloo
"Models are only as good as their assumptions. If you ignore the assumptions that go into the model, it can produce things it was never intended to produce.”
Tagging him with responsibility for the meltdown seems like blaming Albert Einstein for the nuclear disaster at Chernobyl. Both developed a brilliant theory; others used it for their own purposes.
Professor Steve Brown

21. What can be done?
Identifying different factors that affect investments
Confounders
Historical statistics
Better statistical analysis; better equations

23. THANK YOU FOR YOUR ATTENTION!