Risk and Finance

The consequences of the mismanagement of credit risk and mispricing of structured credit portfolios are notorious. The purpose of this project is to research, develop and implement superior methods for managing credit derivatives, from single name instruments such as credit default swaps to complex structured products, such as mortgage-backed securities and collateralized debt obligations. The algorithms investigated will employ a bottom-up approach, based on realistic modeling of the underlying collateral instruments (e.g. mortgages).

Traders in both financial markets and commodity markets must make educated decisions about when to trade and at what price; this project develops tools to assist with this decision-making process. Working with energy companies, financial software companies as well as companies from the banking and insurance sectors, the research team develops optimal portfolio methods that produce both the best investment decisions and the best hedging strategies for claims in general markets.

This project deals with the mathematics of risk modeling and resource management. Using mathematical and statistical methods, the team develops new tools to help the financial services industry make better decisions about when to trade and at what price based on the available financial data. During the past year, the team focused on the development of statistical methods for measuring volatility and assessing asset pricing models in financial markets.

With many baby-boomers entering retirement in North America, and with the increasing size of the aging population worldwide due to economic and social development, managing retirement income becomes an important question for the finance and insurance industry as well as for individuals. At the same time, corporations are stepping away from the standard “pensions" they have traditionally offered employees. As a result, individuals are responsible more often for managing the risks associated with securing their retirement income.