McCormick Magazine

Risk averse

Financial engineers model the financial world

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vadim linetskyIt has been called the worst financial crisis since the Great Depression: consumers and banks suffering from the subprime mortgage crisis, airlines reeling from high oil prices, financial institutions like IndyMac, Bear Stearns, and Lehman Brothers collapsing, investment banks restructuring.

What were the chances of this happening? Could better risk models have prevented these crises? These are questions for which financial engineers Vadim Linetsky and Jeremy Staum seek answers.

Financial engineering is an interdisciplinary field that integrates methods and knowledge from mathematics, statistics, economics, operations research, and computer science. Financial engineers develop quantitative tools that help banks, manufacturing and service firms, and public institutions make disciplined financial decisions in the face of risk and uncertainty. Financial engineers also devise computational algorithms to implement these tools and calibrate them to financial market data.

Gauging financial risk
Staum’s research group focuses on big-picture risk, creating computer simulations that can model an entire financial institution’s risk. Global financial institutions have large portfolios that include investments in different parts of the world and in different markets. To consider the risk of that entire portfolio, an institution must take into account factors such as stock prices, interest rates, exchange rates, and credit risk. Above all, the financial institution must consider the chance that it will become insolvent. “That’s when people start worrying about major banks or hedge funds going bankrupt, and the talking heads on TV are saying the entire financial system might seize up,” says Staum, who is an associate professor of industrial engineering and management sciences. “What we’re seeing now are the extremely severe consequences of using inadequate risk models. This is the extreme scenario that shows financial institutions that they should invest in risk management technology in order to hold adequate capital reserves to absorb possible losses from their portfolios.”

Most financial institutions focus on protecting against losses 99 percent of the time. But there is no good model that shows what could happen under extreme scenarios, so banks are often unprepared when extreme events happen. “It’s very difficult to model everything because of all the interdependencies and correlations,” Staum says.

Part of the problem lies in computing. Much of the financial world relies on simpler models because they take less computing time and are cheaper to implement. Those models say that extreme events are highly unlikely, so institutions often can get comfortable with those models and then fail to prepare for the worst.

That was the mistake made by Long Term Capital Management, a hedge fund that collapsed in the 1990s. “That was one instance where the model was an utter failure,” Staum says. “Over the past 11 years we have faced crisis after global financial crisis in which people’s risk models broke down, including the dot-com bubble and the current mortgage crisis. That led people to say we need to develop better models. With the current events, it’s like when a bridge collapses — nobody says we’d better stop building bridges. They say, We really better figure out how to build safer bridges. So we really need to figure out how to create better risk management models.”

The computation required to create better models is challenging and expensive, so Staum and his team are looking for more efficient algorithms to make the computing faster and cheaper. They are also working to combine several models and, looking at the degree of plausibility from each model, using that information to get a better picture of the risks that an institution faces.

Understanding credit risk
jeremy staumVadim Linetsky, professor of industrial engineering and management sciences, focuses on modeling credit risk. Credit risk is the risk of default by a corporation or an individual on a financial obligation, such as a bond, loan, mortgage, or pension. Linetsky’s work is distinctive because it creates a model that combines both market and credit risk. His current focus is on risk in financing an asset acquisition, such as a real estate mortgage or an aircraft mortgage. He is working with the global aviation industry on creating better models to assess risk in aircraft mortgages — loans to airlines to purchase aircraft.

Lenders financing such a purchase face the risk that the airline will default on its loans — a risk that heightens when oil prices go up. If the airline defaults on the loan, the lender will have to repossess the aircraft and try to sell it to another airline — which, since airline bankruptcies tend to cluster during the times of economic downturns, is exactly the time when the market for used aircraft is depressed. Therefore the risk of airline’s default and the risk of aircraft market price decline are closely linked.

Linetsky is creating probabilistic models that consider this relationship between market and credit risk. Based on his risk models, he is modeling how much interest a financial institution should charge to finance the purchase of an airplane, and he is creating models that manage the risk of that liability over the life of the loan. “Banks need to make sure they have adequate capital reserves to cover losses,” he says. “It can get complex.”

It gets even more complicated when a government provides export credit guarantees to foreign purchasers of domestically manufactured aircraft. In that case, if an airline goes into bankruptcy and defaults on its aircraft mortgages, the government can seize the aircraft and sell it. Linetsky currently works with the industry to create better models to evaluate commercial aircraft loans and come up with fair premiums for these export credit guarantees.

Linetsky believes that his work on modeling aircraft mortgages can be extended to model the risk in residential home mortgages as well as in financing other types of assets, from real estate to mobile equipment to energy generation assets.

In addition to credit-risk models, Linetsky has also developed an interest-rate model called the Black-Gorovoi-Linetsky (BGL) model that was eventually adopted by the Bank of Japan, that nation’s version of the Federal Reserve. Linetsky is also creating a model for highly volatile commodity prices, such as oil, and he and his PhD students use so-called mean-reverting jump processes to model violent price spikes observed in commodity and energy prices.

To read more about financial engineering, visit www.fe.mccormick.northwestern.edu.

—Emily Ayshford