Measuring and Managing the Complexity of the Financial System

Saturday, February 16, 2013
Room 202 (Hynes Convention Center)
Andrew W. Lo , Massachusetts Institute of Technology, Cambridge, MA
The recent financial crisis has underscored the complexity of the financial system and the need for a fundamentally different approach to the supervision and regulation of financial institutions. Technological advances in electronic market-making, algorithmic trading, telecommunications, and computing power have created a more efficient financial industry but one that is highly nonlinear, densely interconnected, and tightly coupled. Consequently, shocks to one node of the financial network can now propagate quickly and unpredictably to other parts of the network.

Although new regulatory agencies have been given the mandate to measure and manage systemic risk in the financial industry, their main purview does not cover the "shadow banking system," financial institutions such as hedge funds, money market funds, and insurance companies that are not banks but which provide many of the same functions. One of the biggest challenges to regulators is the lack of transparency with respect to financial holdings and risk exposures of institutions in this shadow banking system. However, forcing these institutions to provide greater disclosure may have the unintended consequence of reducing their incentives to innovate.

Recent advances in the application of cryptography to create different levels of privacy may provide an ideal compromise between the need for risk transparency and the protection of proprietary information. Using encryption methods that are homomorphic with respect to certain systemic risk analytics, regulators can obtain the information they need to measure and manage systemic risk exposures while maintaining the privacy of financial institutions and their clients. By eliminating the possibility that proprietary information is revealed, these methods will facilitate greater collaboration among financial institutions to promote systemic stability.

Several examples of privacy-preserving systemic risk measurement involving big data applications in the consumer finance and banking sectors will be provided.