Below is the written testimony of IRA co-founder Christopher Whalen for the hearing before the House Committee on Science & Technology, Subcommittee on Investigations and Oversight, regarding the role of VaR models and other statistical methods in causing the financial crisis. In our written comments, we focused on the idea advanced by our colleague Ann Rutledge in the interview we published earlier this year, "Back to Basis for Securitization and Structured Credit: Interview With Ann Rutledge,"  namely that the ultimate cause of the crisis is confusion among market participants and policy makers as to the difference between objective and subjective analysis. Securities and derivatives that cannot be validated objectively in the markets by all participants are subjective and deceptive by design, and thus should be prohibited by law and regulation. 

Chairman Miller, Congressman Broun, members of the Committee, my name is Christopher Whalen and I live in the State of New York. I work in the financial community as an analyst and a principal of a firm that rates the performance of commercial banks. Thank you for inviting my comments today on this important subject.

The Committee has asked witnesses to comment on the topic of "The Risks of Financial Modeling: VaR and the Economic Meltdown." The comments below reflect my own views, as well as comments from my colleague and business partner Dennis Santiago, and others in the financial and risk management community.

By way of background, our firm provides ratings for assessing the financial condition of US banks and commercial companies. We build the analytical tools that we use to support these rating activities and produce reports for thousands of consumer and professional users.

We use mathematical tools such as models to explore the current financial behavior of a given subject. In the course of our work, we use these tools to make estimates, for example, as to the maximum probable loss in a bank's loan portfolio through an economic cycle or the required Economic Capital for a financial institution. Models help us understand and illustrate how the financial condition of a bank or other obligor have changed and possibly will change in the future.

But in all that we at Institutional Risk Analytics do in the world of ratings and financial analysis, we do our best to separate objective measures based upon empirical observations, and subjective analyses that employ speculative assumptions and directives which are often inserted into the very ground rules for the analysis process itself. The difference between subjectivity and objectivity in finance has significant implications for national policy when it comes to financial markets and institutions.

I strongly suggest to the Committee that they bear the distinction between objective and subjective measures in mind when discussing the use of models in finance. Obtaining a better understanding of the role of inserting subjectivity into models is critical for distinguishing between useful deployments of modeling to manage risk and situations where models are the primary failure pathway towards creating systemic risk. The distinction affects our economic stability and public policy.

Used as both a noun and a verb, the word "model" has become the symbol for the latest financial crisis because of the use, or more precisely, the misuse of such simulations to price unregistered, illiquid securities such as subprime mortgage backed securities and derivatives of such securities. The anecdotal cases where errant models have led to mischief are many and are not limited to the world of finance alone.

The Trouble with Models

The problem is not with models themselves. The trouble happens when they are (a) improperly constructed and then (b) deliberately misapplied by individuals working in the financial markets.

In the physical sciences, models can be very usefully employed to help analysts understand complex systems such as disease, buildings and aircraft. These models tend to use observable data as inputs, can be scientifically validated and are codified in a manner that is transparent to all involved in the process. Models used in the physical world share one thing in common that financial models do not: they are connected to and are confirmed or refuted by the physical world they describe.

Financial models, on the other hand, are all intellectual abstractions designed to manipulate arbitrarily chosen, human invented concepts. The chief reason for this digression from the objective use of models observed in the physical sciences is the injection of economics into the world of finance. Whereas financial models were once merely arithmetic expressions of expected cash flows, today in the world of financial economics, models have become vehicles for rampant speculation and outright fraud.

In the world of finance, modeling has been an important part of the decision making toolkit of executives and analysts for centuries, helping them to understand the various components in a company or a market and thereby adjust to take advantage of the circumstances. These decision analysis models seek to measure and report on key indicators of actual performance and confirm the position of the entity with respect to its' competitive environment. For instance, the arithmetic calculation of cash flows adheres to the scientific method of structures and dynamics, and is the foundation of modern finance as embodied by the great theorists such as Benjamin Graham and David Dodd.

At our firm, we employ a "measure and report" model called The IRA Bank Monitor to survey and stress test all FDIC insured banks each quarter. By benchmarking the performance of banks with a consistent set of tests, we are able to not only characterize the relative safety and soundness of each institution, but can draw reasonable inferences about the bank's future performance.

But when the world of finance marries the world of outcome driven economics - the world of "what if" and "I want" - models cease to be mechanistic tools for validating current outcomes with hard data and assessing a reasonable range of possible future events. Instead models become enablers for speculation, for the use of skillful canards and legal subterfuge that ultimately cheat investors and cause hundreds of billions of dollars in losses to private investors and insured depository institutions.

Take the world of mortgage backed securities or MBS. For decades the investment community had been using relatively simple models to predict the cash flow of MBS in various interest rate scenarios. These predictions have been relatively simple and are validated against the monthly mortgage servicer data available to the analyst community. The MBS securitization process was simple as well. A bank would sell conforming loans to GNMA and FNMA, and sell inferior collateral to a handful of investment banks on Wall Street to turn the loans into private MBS issues.

At the beginning of the 1990's, however, Wall Street's private MBS secret sauce escaped. A firm named Drexel, Burnham, Lambert went bankrupt and the bankruptcy court sold copies of Drexel's structured finance software to anyone and everyone. It eventually wound up in the hands of the mortgage issuers themselves. These banks and non-banks naturally began to issue private MBS by themselves and discovered they could use the mathematics of modeling to grow their mortgage conduit businesses into massive cash flow machines. When brought to market, these private MBS were frequently under-collateralized and could therefore be described as a fraud.

Wall Street, in turn, created even more complex modeling systems to squeeze even more profits from the original MBS template. The expanding bubble of financial innovation caught the eye of policy makers in the Congress, who then created political models envisioning the possibility that "innovation" could be used to make housing accessible to more Americans.

Spurred on to chase the "policy outcome" of affordable housing, an entire range of deliberately opaque and highly leveraged financial instruments were born with the full support of Washington, the GSEs and the Congress. Their purpose now was to use the alchemy of financial modeling to create the appearance of mathematical safety out of dangerous toxic ingredients. Wall Street firms paid the major rating agencies to award "AAA" ratings to derivative assets that were ultimately based on subprime mortgage debt. And the stage was set for a future economic disaster.

In the case of subprime toxic waste, the models became so complex that all transparency was lost. The dealers of unregulated, unregistered complex structured assets used proprietary models to price and sell deals, but since the "underlying" for these derivative securities was invisible, none of the investment or independent ratings community could model the security. There was no validation, no market discipline. Buy Side customers were dependent upon the dealer who sold them the toxic waste for valuation. The dealers that controlled the model often time would not even make a market in the security.

Clearly we have now many examples where a model or the pretense of a model was used as a vehicle for creating risk and hiding it. More important, however, is the role of financial models for creating opportunities for deliberate acts of securities fraud. These acts of fraud have caused hundreds of billions of dollars in losses to depository institutions and investors.

Whether you talk about toxic mortgage assets or credit default swaps, the one common element that the misuse of models seems to contain is a lack of a visible underlying market against which to judge or "mark" the model. Indeed, the use of models in a subjective context seems to include the simulation of a nonexistent market as the primary role for the financial model.

In single-name credit default swaps or "CDS" for example, there is often insufficient trading in the supposed underlying corporate debt security to provide true price discovery. In the case of CDS on complex structured assets, there is no underlying market to observe at all. The subjective model becomes the market in terms of pricing the security.

In the spring of 2007, however, the fantasy land consensus that allowed people to believe that a model is a market came undone. We have been dealing with the consequences of the decisions that originally built the house of cards since that time.

An Objective Basis for Finance and Regulation

The term "model" as it applies to finance can be a simulation of reality in terms of predicting future financial outcomes. The author Nassim Taleb, who is appearing at this hearing, says the term "VaR" or value at risk describes a statistical estimate of "the expected maximum loss (or worst loss) over a target horizon within a given confidence interval."

VaR models and similar statistical methods pretend to estimate the largest possible loss that an investor might experience over a given period of time to a given degree of certainty. The use of VaR type models, including the version imbedded in the Basel II agreement, involves a number of assumptions about risk and outcomes that are speculative. More important, the widespread use of these statistical models for risk management suggest that financial institutions are subject to occasional "Black Swans" in the form of risk events that cannot be anticipated.

We take a different view. We don't actually believe there is such a thing as a "Black Swan." Our observations tell us that a more likely explanation is that leaders in finance and politics simply made the mistake of, again, believing in what were in fact flawed models and blinded themselves to what should have been plainly calculable innovation risks destined to be unsustainable. Or worse, our leaders in Washington and on Wall Street decided to be short sighted and not care about the inevitable debacle.

We suggest that going forward our national interest needs to demand a higher standard of tangible proof from "outcome designers" of public policies. If financial markets and the models used to describe them are limited to those instruments that can be verified objectively, then we no longer need to fear from the ravages of Black Swans or systemic risk. The source of systemic risk in the financial markets is fear born from the complexity of opaque securities for which there is no underlying basis. The pretext for issuing these ersatz securities depends on subjectivity injected into a flawed model.

If we accept that the sudden change in market conditions or the "Black Swan" event that Taleb and other theorists have so elegantly described arises from a breakdown in prudential regulation and basic common sense, and not from some unknowable market mechanism, then we no longer need to fear surprises or systemic risk. We need to simply ensure that all of the financial instruments in our marketplace have an objective basis, including a visible, cash basis market that is visible to all market participants. If investors cannot price a security without reference to subjective models, then the security should be banned from the US markets as a matter of law and regulation. To do otherwise is to adopt deception as the public policy goal of the US when it comes to financial markets regulation.

As Graham and Dodd wrote nearly a century ago, the more speculative the inputs the less the analysis matters. Models only have real value to society when their workings are disciplined by the real world. When investors, legislators and regulators all mistook models for markets, and even accepted such speculations as a basis for regulating banks and governing over-the-counter or OTC markets for all types of securities, we as a nation were gambling with our patrimony. If the Committee and the Congress want to bring an end to the financial crisis, we must demand higher standards from our citizens who work in and regulate our financial markets

As we discussed in a commentary last month,"Systemic Risk: Is it Black Swans or Market Innovations?," published in The Institutional Risk Analyst , "were the failures of Bear Stearns, Lehman Brothers, Washington Mutual or the other "rare" events really anomalous? Or are we just making excuses for our collective failure to identify and manage risk? A copy of our commentary follows this testimony. I look forward to your questions.

Questions? Comments? info@institutionalriskanalytics.com

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