Assessing the risk of a cataclysm

Mon May 25, 2009 8:04am EDT
 
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By Jane Baird

LONDON (Reuters) - Professor Zari Rachev scorns the idea that market cataclysms cannot be forecast. He says his statistical models have predicted them, and his customers agree.

His daughter is now president of New York-based company FinAnalytica, which uses his models to provide investors and risk managers with a risk indicator that takes into account the worst-case scenarios.

As those who have so far survived the financial crisis pick over the wreckage to develop enhanced predictors of market risk, Rachev's are among the offerings for people who believe statistical models can help.

"This past year was very important for us, because it validated everything that we worked for," Boryana Racheva-Iotova told Reuters by telephone from Bulgaria.

Her firm's risk measure, fat-tailed expected tail loss or ETL, gave investors advance notice of a sharp fall in the Dow Jones Industrial Average among other markets: the Dow fell from a life high in November 2007 to a 12-year low in March 2009, sliding sharpest after Lehman Brothers failed in September 2008.

Fat-tailed ETL builds on the statistical phenomenon popularized by former options trader Nassim Nicholas Taleb's focus on the massively unexpected.

Think of a bell curve on a statistician's chart that reflects "normal distribution." It is tall and wide in the middle -- where most events fall -- and drops and flattens out at the edges, where fewer things happen, making a shape on a graph like a bell. When the edges or tails swell, instead of nearly vanishing, they are called "heavy" or "fat."

Streams of financial commentators have over the past year reveled in a desire to present the crash as coming out of the blue to math whizzes paid a fortune to study the statistical stars and presage such events.   Continued...

 
<p>New York-based FinAnalytica has developed an indicator - called fat-tailed expected tail loss or fat-tailed ETL - to alert investors to their potential exposure to extreme losses. REUTERS/Graphic</p>