Financial Econometrics and Empirical Market Microstructure
Adaptive Stress Testing: Amplifying Network Intelligence by Integrating Outlier Information (Draft 16)
Alan Laubsch
The future is already here. It’s just not evenly distributed yet.
(William Gibson)
Abstract This essay examines lessons from systemic breakdowns, and presents a framework for Adaptive Stress Testing to proactively manage systemic risks. The framework is inspired by evolutionary ecosystems, including ecology, economics, technology, psychology, and sociology. Adaptive Stress Testing harnesses network intelligence to integrate early warning signals. We pre-diagnose systemic fragilities by tapping into the marketplace of ideas, and then identify key metrics to monitor market-based early warning signals. We apply the Technology Adoption Lifecycle model to develop a theory of social diffusion of disruptive information in financial markets. We start by taking a macro view of risk in its hidden potential form, and then focus on phase transition signals as risk becomes visible. This process allows us to better understand key systemic risks, and to more effectively sense and respond to emerging risks.
Keywords Behavioral economics • Black Swan • Complexity • Disruptive innovation • Dragon King • Early warning • Eco-centric risk management • Financial cartography • Financial network analysis • Foreshocks • Groupthink • HeavyTails™ • Integral theory • Network theory • Network visualization • Outliers • Phase transitions • Polarity management • Risk culture • Social adoption • Social market hypothesis • Stress indices • Stress testing • Stress testing • StressGrades™ • Subprime crisis • Systemic risk • VaR backtesting
A. Laubsch (H)
Financial Network Analytics (FNA), London, U. K. e-mail: alaubsch@gmail. com
© Springer International Publishing Switzerland 2015
A. K. Bera et al. (eds.), Financial Econometrics and Empirical Market
Microstructure, DOI 10.1007/978-3-319-09946-0_____ 11
The sudden onset and severity of crises catch most by surprise. Unpredictable events emerge and quickly escalate out of control. The 2008 US subprime crisis was but the latest of such systemic breakdowns. But not all were equally surprised. Structural fragilities built up over years, and early warning signals escalated from 2006 to 2007. A prescient few foresaw the inevitable bust, mitigated their risk, alerted regulators, and even issued public warnings (largely ignored, unfortunately). Indeed, one might argue that the biggest surprise was the extent of risk myopia despite an abundance of information. Why did some perceive risks that most were blind to? And what can the rest of us learn from them? This essay proposes a methodology to amplify social intelligence in the risk management community.
