NCSA
HPCwire

Since 1986 - Covering the Fastest Computers
in the World and the People Who Run Them

Language Flags

Visit additional Tabor Communication Publications

Datanami
Digital Manufacturing Report
HPC in the Cloud
Green Computing Report

Tabor Communications
Corporate Video

Blog: From the Editor

From the Editor | Main Blog Index

Quants Gone Wild


It's fascinating to read the post-mortem analysis of the economic meltdown, especially as it relates to the role quantitative analysts and their high-tech financial models played in pushing the industry off a cliff.

In a Wired article this week titled The Formula That Killed Wall Street, Felix Salmon writes about how a single formula, the Gaussian copula function, came to dominate financial risk modeling, and how it ultimately undermined the investment industry. The formula was the brainchild of David Li, a quantitative analyst (quant) who developed Gaussian copula to correlate risk in the now infamous collateralized debt obligations (CDOs) and credit default swaps (CDSs).

To distill the global financial collapse down to a single mathematical formula seems like a stretch, but Salmon makes a compelling case for how the Gaussian copula function insinuated itself into the ecosystem of professional financial analysts and investors:

For five years, Li's formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.

His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched--and was making people so much money--that warnings about its limitations were largely ignored.

Then the model fell apart...

As it turns out, Gaussian copula, while elegant, was much too simplistic. For example, since the formula ignored the interrelationships of the individual loans that made up a CDO, as well as the historical data of housing assets, the risk correlation was all based on short-term behavior -- in this case, at a time when housing prices were rising dramatically. Investment bankers should have known that the risk was still out there. So, asks Salmon, why didn't the bankers question where the risk had gone?

They didn't know, or didn't ask. One reason was that the outputs came from "black box" computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula's weaknesses, weren't the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked.

Quantitative finance guru Paul Wilmott, who was cited in the Wired piece as one of the original critics of the copula model -- he's more of a Black-Scholes kind of guy -- thinks the industry's unexamined reliance on copula is just a symptom of a wider problem in the financial industry. In his blog this week, he laments about the cult-like tendencies that permeate the financial community:

Far more serious, because it extends to all of finance not just to a single model, is the poor education that people get in university financial engineering programs and also the blind-following-the-blind behavior that is so common throughout the industry.... It's getting quite tedious me telling people to get off their backsides and test the models for themselves.

The failure of the models is certain to have other repercussions, too. If we can believe a recent article in eFinancialCareers, the reputation of quants has taken a real beating:

What's a PhD with a bias towards quantitative finance to do? Banks have gone from screaming from the rooftops that they want quants, to whispering that they're only interested in a select handful of them. This leaves a lot of people on the sidelines.

Not only are fewer quantitative models being built, but firms are getting a lot more picky about their background. According to one recruiter, PhDs will still be needed to work on less lucrative algorithmic trading work, but funds are looking for "people experienced with dealing with noisy high frequency data sets, rather than the physicists and stochastic calculus experts previously sought after by banks."

In hindsight, it seems inevitable that financial risk models and the people designing them would have to take into account the larger social and economic context of the data. To me the whole episode should be viewed as a cautionary tale for all would-be model makers. Whether you're designing climate simulations, aircraft wings or designer drugs, it's always healthy questioning the applicability of the math. The fate of people's lives and livelihoods may hinge on such skepticism.

Posted by Michael Feldman - February 26, 2009 @ 4:55 PM, Pacific Standard Time

Sponsored Links

Webinar: Programming Heterogeneous X64+GPU Systems Using OpenACC
Join Michael Wolfe as he compares the advantages and costs of using both low-level models and the directive-based OpenACC model for programming accelerated heterogeneous systems. Registration is free.

High-Performance Computing in Action
Businesses that want to be on the cutting edge of their industries are increasingly turning to high-performance computing (HPC) solutions to handle complex compute processes and speed up their rate of innovation. Download this Executive Brief to see how businesses in energy, life sciences and entertainment put HPC solutions to work in their operations.

Accelerate your science with Seneca
One of the first HPC providers installing a 4X NVIDIA Kepler K-20 cluster. Invites you to a free evaluation on Seneca’s NVIDIA K20 Kepler cluster, pre-loaded with AMBER, NAMD, LAMMPS

Michael Feldman

Michael Feldman

Michael Feldman is the editor of HPCwire.

More Michael Feldman

Cray CS300-LC

Recent Comments

No Recent Blog Comments

Feature Articles

NSF Forges Further Beyond FLOPs

In a recent solicitation, the NSF laid out needs for furthering its scientific and engineering infrastructure with new tools to go beyond top performance, Having already delivered systems like Stampede and Blue Waters, they're turning an eye to solving data-intensive challenges. We spoke with the agency's Irene Qualters and Barry Schneider about..
Read more...

CERN, Google Drive Future of Global Science Initiatives

Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Read more...

Saddling Phi for TACC’s Stampede

The Xeon Phi coprocessor might be the new kid on the high performance block, but out of all first-rate kickers of the Intel tires, the Texas Advanced Computing Center (TACC) got the first real jab with its new top ten Stampede system.We talk with the center's Karl Schultz about the challenges of programming for Phi--but more specifically, the optimization...
Read more...

Short Takes

Building Supercomputers with Raspberries

May 22, 2013 | At some point in the not-too-distant future, building powerful, miniature computing systems will be considered a hobby for high schoolers, just as robotics or even Lego-building are today. That could be made possible through recent advancements made with the Raspberry Pi computers.
Read more...

Running Computational Fluid Dynamics in the Cloud

May 16, 2013 | When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
Read more...

Computing the Physics of Bubbles

May 15, 2013 | Supercomputers at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) have worked on important computational problems such as collapse of the atomic state, the optimization of chemical catalysts, and now modeling popping bubbles.
Read more...

Internet2 Awards Program Seeks Innovative Applications

May 10, 2013 | Program provides cash awards up to $10,000 for the best open-source end-user applications deployed on 100G network.
Read more...

Floating Funding to Exascale Island

May 09, 2013 | The Japanese government has revealed its plans to best its previous K Computer efforts with what they hope will be the first exascale system...
Read more...

Sponsored Whitepapers

Best Practices in Big Data Storage

05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.

Progress in Parallel: the Bull Parallel Programming Center

04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.

Sponsored Multimedia

SGI DMF ZeroWatt Disk Solution

In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.

Cray CS300-AC Cluster Supercomputer Air Cooling Technology Video

The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.

Blogs by Topics

Blogs by Author

HPC Blogroll

Xyratex

Featured Events


  • June 16, 2013 - June 20, 2013
    ISC'13
    Leipzig,
    Germany

  • June 17, 2013 - June 18, 2013
    Forecast 2013
    San Francisco, CA
    United States





HPCwire Events