Safer Financial Networks
Stochastic systems are systems that have some element of randomness, making their behavior difficult to predict. Andreea C. Minca, Operations Research and Information Engineering, works with complex stochastic networks that model financial systems, trying to determine how to reduce risk and make a system more stable. With this CAREER award, she is developing a comprehensive framework for the design and optimization of star-shaped stochastic networks with a central node or hub from which the system, to a certain degree, can be stabilized and controlled.
The framework can be applied in many contexts with inherent instability, including retail operations, where the resources are inventories of goods and backordering is allowed. Financial networks are another example, where resources are cash and other capital assets, and individual nodes—a bank in the network, for instance—may experience cash shortfalls.
In this star-shaped model, the central node—called the central clearinghouse in finance—can manage risk by pooling a portion of the resources of each individual node and underwriting a node that falls below a certain threshold. The central clearinghouse and its resources would therefore serve as insurance against random instability in finance and could help prevent broad financial crisis.
Minca is using stochastic and game theory to model the resources of the central node and individual nodes and to determine the optimal contributions from each node to the pooled central node. She is also calibrating the models with available data. The modeling will provide methodology for safer financial networks, less prone to instability.