Credit Modeling with Supercomputing
The creation and deployment of new numerical methods for economic and financial modeling is becoming a critical competitive weapon for banks, hedge funds and other investment firms. From a high performance computing (HPC) perspective, when quantitative analysts are asked how fast these new computations need to be processed, their answer is usually “at least fifteen minutes faster than our competitors with a lot of extra credit for finishing before the close of daily trading.” Consequently, computing requirements on Wall Street are growing exponentially as algorithms and models become more complex to support new investment opportunities, while incorporating ever larger data sets.
But the desktop computers that are used to develop these ever-growing financial codes are inadequate to support full scale production deployment, prompting investments firms to turn to HPC systems such as parallel servers, clusters or grids. Fortunately these systems now employ cost-effective multi-core processors from Intel and AMD, and as a result parallel supercomputing is finally accessible to Wall Street firms.
The problem is the new parallel hardware is like a fast highway that leads to a software wall. Parallel HPCs are unfamiliar computing platforms to most financial analysts, who are accustomed to working with popular mathematical tools such as MATLAB, Python and R to produce their financial models. Computing these models on parallel systems typically requires a team of highly trained programmers to rewrite hundreds of lines of analyst-written VHLL code into thousands of lines of complex C, C++, or FORTRAN involving the Message Passing Interface (MPI) or equivalent manual parallelization techniques. This redesign can take months of time while preventing the interactive experimentation and refinement of the models financial analysts require. These complex coding techniques require programmers versed in parallel programming. And programmers with those skills are not only expensive, they are in short supply in the financial services sector.
Consider the case of Julius Finance, a Wall Street research company that specializes in credit modeling analysis. The company focuses on credit derivative products, analyzing the relative valuation of synthetic collateralized debt obligations (CDOs). The computationally challenging analysis of credit factors such as spread, credit rating, foreign exchange and interest rates for a wide variety of corporate investments has made the credit derivative market a black art at best.
Until now, these financial products have been priced by investment firms using Copula models, a popular approach for modeling dependencies between random variables thanks to their relative mathematical simplicity. But the trade-off for this simplicity has been inconsistent, unconvincing results. “Existing mathematical frameworks for CDO valuation are far from compelling…to put it mildly,” says Peter Cotton, CEO of Julius Finance. “This is not surprising, as rigorous evaluation of credit models is prohibitively time consuming in any conventional research setup.”
Cotton knew that employing new, more sophisticated algorithmic models on massive amounts of variable financial data would give the company a tremendous competitive advantage when it came to making more accurate predictions about a portfolio’s potential.
The company installed a Linux-based cluster to provide the necessary processing power and memory capacity. But rather than employ computer scientists to parallelize the models, Julius Finance took a different approach, using Star-P software to transparently bridge analysts’ desktops with the Linux cluster. This way, the company’s analysts could continue to work in their familiar MATLAB environment, and enable their applications to run on the parallel clusters without reprogramming.
This interactive supercomputing approach allows for continual feedback and refinement from prototype to production, resulting in higher quality models, algorithms and, ultimately, much more accurate portfolio predictions. The company gained a quantum leap in computational performance to handle the massive data sets and model complexities, without having to lose the interactivity and ease of use of their desktop environment. “We took this approach to reduce prototyping time and facilitate memory intensive experiments…looking under more rocks, as it were, and finding very interesting things,” says Cotton.
Julius Finance is part of a growing trend on Wall Street establishing HPCs as a critical resource in the IT data center. The reason: as financial applications become more complex and more compute-intensive, the ability to offer real-time results diminishes with desktop-bound computing. And the big challenge on Wall Street is in providing actionable financial analysis before the window of opportunity closes. Shrinking the “time to solution at full scale” can offer tremendous competitive differentiation to investment firms.
Beyond the specific area of credit modeling, speeding up computations and scenario analyses is critical to all aspects of financial services — including trading desks, risk management desks, etc. — because each component, while a relatively small part of the overall environment, is potentially computationally expensive. Whether the reactions are in nearly real time, on an hourly basis, or at the end of the day, the decisions could often be improved if they included more trajectories, more scenarios. The models are dynamic — with frequent updates with new parameters — so flexibility in algorithm development and production deployment is key.
This new interactive supercomputing model can be generalized to a variety of financial analytical applications ranging from numerically-intensive workloads in simulation, optimization and valuation to data-intensive workloads performing pattern detection for fraud detection and trading opportunities, such as:
Monte Carlo Simulation – These simulations have many advantages, including the ease of implementation and applicability to multi-dimensional problems commonly encountered in finance. However, calculation using Monte Carlo techniques is very time consuming due to the need for simulating many trajectories with multiple parameters.
Portfolio Optimization — Taking an interactive supercomputing approach, analysts can run their models on parallel systems to optimize thousands of individual portfolios overnight based on the previous day’s trading results. Commercial and open source optimization libraries such as Axioma or CPLEX can typically be plugged in and executed in parallel – all from within the analyst’s desktop application.
Valuation of Financial Derivatives — Valuing financial derivatives is computationally intensive, and requires large amounts of computer time. A re-insurance firm, for example, may need to value and compute hedge strategies for hundreds of thousands of policy holders in its portfolio on a regular and timely basis. Analysts need to be able to explore new valuation methodologies from their desktop, using high performance computers to run billions of complex scenarios.
Detection of Credit Card Fraud — The rise of identity theft together with the popularity of online shopping has resulted in a huge increase in credit card fraud. As thieves become increasingly shrewd in exploiting security weaknesses, banks and credit card companies need to be extremely agile to stay ahead of them. Parallel HPCs enable a bank to easily run more sophisticated fraud detection algorithms against tens of millions of credit card accounts.
Hedge Fund Trading — In balancing a large portfolio of stocks, analysts need to search for short- and long-term patterns, identify correlations between securities, and develop forecasts. Intense computations are required against terabyte-sized “tick store” databases — potentially a decade or more of trading data for thousands of securities. HPCs allow for faster reaction time to market conditions, enabling analysts to evaluate more sophisticated algorithms that take into account larger data sets.
Until now, investment firms faced an “either-or” dilemma by choosing to live with the performance limitations of their desktop systems, or engaging a team of programmers to re-code their algorithms to take advantage of powerful parallel servers or clusters. But that situation changes with new interactive supercomputing models that provide a “both-and” opportunity combining the productivity breakthroughs of easy to use desktop development with a seamless transition to deployment of large, complex financial simulations on parallel servers. Analysts can focus on rapidly delivering the most accurate, comprehensive and actionable intelligence by leveraging abundant parallel system resources without the need for scarce human resources.
About the Author
Bill Blake is the Chief Executive Officer of Interactive Supercomputing Inc. He brings more than two decades of senior executive experience in developing high performance computing systems. He joins ISC from Netezza, where he was senior vice president of product development for the high-performance data warehouse appliance company. Bill previously was vice president of high performance technical computing at Compaq, where he led development and marketing efforts. He received undergraduate and graduate degrees in Electrical Engineering at the Lowell Technological Institute, and is a member of the Institute of Electrical and Electronics Engineers (IEEE), the Association for Computing Machinery (ACM), and the American Association of Artificial Intelligence. Bill is a member of the board of directors of supercomputing pioneer, Cray Inc. Supercomputers, as well as Etnus, Inc., a provider of analytical software for developing complex computer code.