Abstract
We apply an artificial intelligence approach to simulate the impact of financial market regulations on systemic risk—a topic vigorously discussed since the financial crash of 2007–09. Experts often disagree on the efficacy of these regulations to avert another market collapse, such as the collateralization of interbank (counterparty) derivatives trades to mitigate systemic risk. A limiting factor is the availability of proprietary bank trading data. Even if this hurdle could be overcome, however, analyses would still be hampered by segmented financial markets where banks trade under different regulatory systems. We therefore adapt a simulation technology, combining advances in graph theoretic models and machine learning to randomly generate entire financial systems derived from realistic distributions of bank trading data. We then compute counterparty credit risk under various scenarios to evaluate and predict the impact of financial regulations at all levels—from a single trade to individual banks to systemic risk. We find that under various stress testing scenarios collateralization reduces the costs of resolving a financial system, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations. Moreover, the concentration of credit risk does not necessarily correlate monotonically with systemic risk. While the analysis focuses on counterparty credit risk, the method generalizes to other risks and metrics in a straightforward manner.
Highlights
Specialty section: This article was submitted to Artificial Intelligence in Finance, a section of the journal Frontiers in Artificial Intelligence
While the analysis focuses on counterparty credit risk, the method generalizes to other risks and metrics in a straightforward manner
A decade has passed, regulators and industry participants alike failed to arrive at a consensus on: (1) Have the regulations implemented post-crisis reduced systemic risk? (2) How can we predict the impact of a financial regulation before it is implemented? and (3) How can we evaluate which regulation is best to avert yet another “Financial Katrina?” As many governments once again face pressure to rollback far reaching financial legislation, it is necessary to know which regulations promote safety and soundness of the financial system and which add undue burdens on markets
Summary
Predicting the financial crisis is like forecasting the weather, a plethora of variables must converge at just the right moment in just the right way, invariably, leading experts to arrive at wildly conflicting prognostications. 5. Pattern recognition that uses tools, such as natural language processing to classify and interpret data. Pattern recognition that uses tools, such as natural language processing to classify and interpret data What does this methodology tell us about predicting financial disasters or, even more importantly, how to avoid them? A decade has passed, regulators and industry participants alike failed to arrive at a consensus on: (1) Have the regulations implemented post-crisis reduced systemic risk? We evaluate, predict and optimize the amount of collateralization required to mitigate counterparty credit risk at the trade, bank and systemic level. The approach developed enables regulators and industry participants alike to conduct iterative scenario testing and thereby provides a unique opportunity to make informed decisions about the impact of public policy before the crisis strikes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
