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

Assigned Reading Salmon, Felix (2009). Recipe for Disaster: The Formula That Killed Wall Street. Wired Magazine 17 (3). http://www.wired.com/techbiz/it/magazine/17-03/wp_q

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

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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 http://sps.columbia.edu/sites/default/files/styles/featured-content-460px/public/DavidXLi_1.jpg?itok=I4btiSSj

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 http://sps.columbia.edu/sites/default/files/styles/featured-content-460px/public/DavidXLi_1.jpg?itok=I4btiSSj

David X. Li Illustration: David A. Johnson https://www.wired.com/wp-content/uploads/archive/images/article/magazine/1703/wp_quant3_f.jpg

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

The “Formula” David X. Li's Gaussian copula function as first published in 2000 https://www.wired.com/wp-content/uploads/archive/images/article/magazine/1703/wp_quant3_f.jpg

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

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

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. http://clipartall.com/clipart/2430-alice-in-wonderland-clipart.html http://images.clipartpanda.com/divorce-clipart-cliparti1_divorce-clipart_03.jpg http://www.clipartkid.com/slipping-on-banana-cliparts/ http://images.clipartpanda.com/spelling-bee-clipart-black-and-white-spelling-bee-clip-art-welcome-to-our-newer-fans---hugs.jpg

5% Divorced Witnessed >95% Wins 0% r close to 0 Witnessed >95% r close to 1 Wins 0% r close to 0 http://images.clipartpanda.com/brown-hair-clipart-little_girl_4_brunette.png

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

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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

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

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

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

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 https://uwaterloo.ca/statistics-and-actuarial-science/people-profiles/steve-brown

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

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THANK YOU FOR YOUR ATTENTION! http://www.hollywoodreporter.com/sites/default/files/2013/10/the_wolf_of_wall_street.jpg