THE ROLE OF INFORMATION TECHNOLOGY

admin — Mon, 15 Aug 2011 15:49:26 +0000

Advances in information technology are behind the widespread adoption of modeling in finance. The most important advance has been the enormous increase in the amount of computing power, concurrent with a steep fall in prices. Government agencies have long been using computers for economic modeling, but private firms found it economically justifiable only as of the 1980s. Back then, economic modeling was considered one of the “Grand Challenges” of computational science.
In the late 1980s, firms such as Merrill Lynch began to acquire super- computers to perform derivative pricing computations. The overall cost of these supercomputing facilities, in the range of several million dollars, limited their diffusion to the largest firms. Today, computational facilities ten times more powerful cost only of a few thousand dollars.
To place today’s computing power in perspective, consider that a 1990 run-of-the-mill Cray supercomputer cost several million U.S. dollars and had a clock cycle of 4 nanoseconds (i.e., 4 billionths of a second or 250 million cycles per second, notated as 250 MHz). Today’s fast laptop computers are 10 times faster with a clock cycle of 2.5 GHz and, at a few thousand dollars, cost only a fraction of the price. Supercomputer performance has itself improved significantly, with top computing speed in the range of several teraflops7 compared to the several mega- flops of a Cray supercomputer in the 1990s. In the space of 15 years, sheer performance has increased 1,000 times while the price-performance ratio has decreased by a factor of 10,000. Storage capacity has followed similar dynamics.
The diffusion of low-cost high-performance computers has allowed the broad use of numerical methods. Computations that were once per- formed by supercomputers in air-conditioned rooms are now routinely performed on desk-top machines. This has changed the landscape of financial modeling. The importance of finding closed-form solutions and the consequent search for simple models has been dramatically reduced. Computationally-intensive methods such as Monte Carlo simulations and the numerical solution of differential equations are now widely used. As a consequence, it has become feasible to represent prices and returns with relatively complex models. Nonnormal probability distributions have become commonplace in many sectors of financial modeling. It is fair to say that the key limitation of financial econometrics is now the size of available data samples or training sets, not the computations; it is the data that limits the complexity of estimates.
Mathematical modeling has also undergone major changes. Techniques such as equivalent martingale methods are being used in derivative pricing and cointegration , the theory of fat-tailed processes, and state-space modeling (including ARCH/GARCH and stochastic volatility models) are being used in econometrics.
Powerful specialized mathematical languages and vast statistical software libraries have been developed. The ability to program sequences of statistical operations within a single programming language has been a big step forward. Software firms such as Mathematica and Math- works, and major suppliers of statistical tools such as SAS, have created simple computer languages for the programming of complex sequences of statistical operations. This ability is key to financial econometrics which entails the analysis of large portfolios.8
Presently only large or specialized firms write complex applications from scratch; this is typically done to solve specific problems, often in the derivatives area. The majority of financial modelers make use of high-level software programming tools and statistical libraries. It is difficult to overestimate the advantage brought by these software tools; they cut development time and costs by orders of magnitude.
In addition, there is a wide range of off-the-shelf financial applications that can be used directly by operators who have a general under- standing of the problem but no advanced statistical or mathematical training. For example, powerful complete applications from firms such as Barra and component applications from firms such as FEA make sophisticated analytical methods available to a large number of professionals.
Data have, however, remained a significant expense. The diffusion of electronic transactions has made available large amounts of data, including high-frequency data (HFD) which gives us information at the transaction level. As a result, in budgeting for financial modeling, data have become an important factor in deciding whether or not to under- take a new modeling effort.
A lot of data are now available free on the Internet. If the required granularity of data is not high, these data allow one to study the viability of models and to perform rough tuning. However, real-life applications, especially applications based on finely grained data, require data streams of a higher quality than those typically available free on the Internet.

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THE ROLE OF INFORMATION TECHNOLOGY