Fields-GRI Mini Symposium on Systemic Risk
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In the decade that followed the 2008 financial crisis, systemic risk emerged as a central topic of research in quantitative finance. In this mini-symposium, the Global Risk Institute partners with the Fields Institute Centre for Financial Industries to explore recent developments in the study of stability of banking networks, presenting the insights from leading academic researchers and regulators working in this area.
The four Ls of systemic risk are well-known to macroprudential regulators: loss, leverage, liquidity and linkages. Adding three more aspects of the job of a systemic risk engineer to the list: lore, likelihood and light – corresponding to data collection issues, probabilistic approaches and insights to guide policy – brings the total to seven. Through the lens of these alliterative seven pillars we take a high-level tour of the modelling tools we use to monitor and measure systemic risk. In each of the stages of a financial crisis: the build-up of imbalances, the resilience of the system when a shock occurs, and the aftermath in which interconnections can transmit ]and amplify the initial shock, different models provide different perspectives and insights. Interactive Jupyter notebooks coded in python and R, hosted on Azure, demonstrate each model. Examples include: back-testing early warning indicators, network construction and analysis, statistical stress tests, stock flow consistent and agent-based economic models, sentiment analysis and the role of machine learning. A cloud-based repository of executable notebooks will be made available for experimentation in a web browser.
Banking system crises are complex events that in a short span of time can inflict extensive damage to banks themselves and to the external economy. The crisis literature has so far identified a number of distinct effects or channels that can propagate distress contagiously both directly within the banking network itself and indirectly, between the network and the external economy. These contagious effects can explain most aspects of past crises, and are thought to be likely to dominate future financial crises. This talk will describe a stylized model for financial systemic risk that includes all the important contagion channels identified in the literature. In such a model one can hope to understand the dangerous spillover effects that are expected to dominate future crises.
I develop a methodology to assess residual risk exposures in derivatives central counterparties (CCPs) relative to the coverage suggested in the CPSS-IOSCO Principles for Financial Market Infrastructures. These risk exposures can be used to evaluate the systemic risk contributions of CCPs and the effectiveness of regulations mandating central clearing. The proposed technique decomposes residual risk exposures into a quantity-based component, driven by individual trading decisions that lead to crowded trades, and more traditional price-based components, determined by the volatility and comovement of asset returns. Empirical results based on data from the Canadian Derivatives Clearing Corporation (CDCC) show that aggregate residual risk exposures reached record levels during the financial crisis, when price-based components were at their highest levels. However, the quantity-based component peaked six months prior to the crisis; thus, suggesting that trade crowdedness could serve as a leading indicator of the financial cycle and should be considered when designing risk management policies.
We develop a framework to analyze the consequences of alternative designs for interbank networks, in which a failure of one bank may lead to others. Earlier work had suggested that, provided shocks were not too large (or too correlated), denser networks were preferred to more sparsely connected networks because they were better able to absorb shocks. With large shocks, especially when systems are non-conservative, the likelihood of costly bankruptcy cascades increases with dense networks. Governments, worried about the cost of bailouts, have proposed bail-ins, where banks contribute. We analyze the conditions under which governments can credibly implement a bail-in strategy, showing that this depends on the network structure as well. With bail-ins, government intervention becomes desirable even for relatively small shocks, but the critical shock size above which sparser networks perform better is decreased; with sparser networks, a bail-in strategy is more credible.