Mastering Market Meltdowns

Mastering the skills to navigate crises by advancing the risk models that guide banking decisions

Ismail Iyigunler (Ph.D. AMAT ’12) first walked onto Illinois Institute of Technology’s campus as a first-year Ph.D. student in August 2008, excited to begin his studies and prepare for a career in the field of quantitative finance.

A month later, the industry collapsed.

Lehman Brothers, the nation’s fourth-largest investment bank at the time, filed for bankruptcy after 158 years of business as the company’s investments in subprime mortgages tanked. It triggered a decline in global markets, sending the entire industry into a tailspin.

“We didn’t know too much about what was going to happen to financial markets. I had no idea what was going on in the field,” Iyigunler says. “But it was helpful. I got see everything first-hand as a student, rather than being in the field. It helped me get familiar with some important topics.”

He found himself incentivized to understand the elements that caused the collapse, why it happened, and to understand the new methods needed to recover, as well as how to identify the problems that caused the crisis and how to prevent another one.

“When COVID-19 hit, we saw a similar situation,” Iyigunler says. “We saw a market meltdown in days and weeks, but we had a better idea on how to react to it.”

As director of global markets risk analytics at Bank of America, Iyigunler develops and maintains mathematical models that outline the best-case and worst-case scenarios—and everything in between.

These models allow bank officials to make valuable risk management decisions.

“My main focus is to ensure that the models are adequate, stable, and fit for risk management,” he says. “The modeling is a complex statistical analysis. It’s the math-related aspect of risk management.”

Whether a model is new or existing, it is put through numerous stress tests and demonstrations to determine how fit and robust it is. The model is fined-tuned for optimization based on its results and new market information.

These models are subject to strict regulatory oversight and must comply with a variety of risk management and investment guidelines. Some require augmentation to meet new regulatory standards, while others need adjustments to accommodate evolving market conditions.

Iyigunler says this means that he sometimes works with computer engineers, attorneys, traders, and risk managers to ensure that the models can be applied to shifting measures.

Most importantly, Iyigunler is assigned to communicate how the model works so that those relying on it understand how the results are determined.

“Stakeholders need to understand, and be comfortable with, the computations and limitations of the models,” he says. “You must be able to defend your model. You must prove that the model is fit for purpose.”

The models are typically used to assess the risks that a client can bring. They answer the questions such as, “What will happen should a client default on a loan?” and “What are the reverberations that it will bring across the bank?”

It allows stakeholders to assess how much risk they are willing to take under various market conditions.

“The models have forecast abilities,” Iyigunler says. “The models identify the black swan event that could put extreme pressure on a firm’s risk profile, helping them assess whether they are comfortable taking on that level of risk.

In essence, these models assess two different areas: market risk and the counterparty risk. Market risk includes how markets rise or crash, and if a market collapses, what loss a firm can see. The counterparty risk examines a client’s potential default and how that default will impact a firm’s losses.

“Certain defaults may occur during specific events,” Iyigunler explains. “We can then assess how these defaults correlate with the underlying asset and evaluate the potential impact on a firm’s losses.”

Iyigunler began his career as a quantitative analyst at Intercontinental Exchange, a clearinghouse. There, he gained valuable experience in understanding risk exposure, market dynamics, and the intricacies of clearing complex financial instruments.

It provided him with great exposure to a diverse set of transactions and how an assortment of markets operates.

“While many of my friends started their careers at banks after graduation, I chose a different path,” Iyigunler says. “They had the chance to develop deep expertise in specific areas, but working at the clearinghouse allowed me to gain a broader view of the industry, which felt like the right fit for me.”

He began his risk management career mostly looking at the credit default swap market. He helped build a centralized risk management operation, examining systemic risk for the whole market.

He moved to Bank of America two years ago after climbing the risk management ranks at Intercontinental Exchange for nine years—a career that he has built on the unique pathway that he chose early in his education journey.

Iyigunler began studying mathematics after high school in his native country of Turkey. He calls it a “radical” decision, as many in his home country take on studies for a very specific career, rather than choosing a general area study.

“I’ve long been fascinated by the intersection of mathematics and finance,” he states. “When I was applying to graduate programs, quantitative finance was an emerging, high-growth field. Its momentum, coupled with seeing many colleagues pursue similar roles, made it a compelling career choice.”