Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era: How to Navigate ‘Uncertainty’...When ‘Models’ Are ‘Wrong’...And Knowledge’...‘Imperfect’! Knight Reconsidered Again: Risk, Uncertainty, & Profit Beyond ZIRP & NIRP
Research Presentation at: 2016 Princeton Quant Trading Conference, Princeton University
136 Pages Posted: 25 Apr 2016 Last revised: 10 Jun 2016
Date Written: April 16, 2016
The 2016 invited research presentation at the Princeton Quant Trading Conference proposes two new financial innovations and their interrelationships: ‘Model Risk Arbitrage’ for ‘Open Systems Finance’. It develops the new framework of Model Risk Arbitrage for profit-maximization in the emerging global financial markets characterized by unprecedented uncertainty, complexity, and, rapid discontinuous changes. It develops the new framework of ‘Open Systems Finance’ aligned with George Soros’ Reflexivity Theory based upon empirical practical experience in financial markets as contrasted from ‘Closed Systems Finance’ models characterizing most of classical and academic Finance and Economics theory.
Aligning with George Soros’ Reflexivity Theory and associated Hegelian Dialectic, it characterizes ‘reflexivity’ as the missing link in Finance theory, research, and, practice that can help understand the effect of feedback and feedforward loops across Time and Space in information-based non-deterministic ‘open systems’ Finance. Consistently, it is based on the Hegelian Dialectic characterizing the [profit-maximizing] Black Hat approach as compared with the [risk-optimizing] White Hat approach. The Black Hat approach exploits all vulnerabilities at all levels of all systems to maximize advantage over the White Hat approach. Similarly, Model Risk Arbitrage exploits all model risks at all levels of all systems to maximize advantage over Model Risk Management. Open Systems Finance framework advances beyond ‘silo’ based mindsets characterizing academic theory and practices and thus serves as foundation for developing and executing Model Risk Arbitrage strategies for profit-maximization.
The current presentation advances upon last two decades of our applied and industrial research and global practices on post-WWW era management and modeling frameworks of uncertainty and risk management. It builds upon our research and practices on designing self-adaptive complex systems for high velocity hyper-turbulent environments characterized by high uncertainty. The 2016 invited presentation is the sequel to the 2015 invited presentation at the Princeton Quant Trading Conference which advanced Frank Knight’s (1921) original treatise developing the foundation of Risk, Uncertainty, and Profit for the Cyber Era. The 2015 presentation laid the foundation for examining modeling and management of ‘true uncertainty’ which is distinct from [theoretical] risk and “which forms the basis of a valid theory of profit and accounts for the divergence between actual and theoretical competition” (Knight 1921).
That presentation further advanced upon prior research in collaboration with world’s distinguished cybersecurity experts affiliated with the Air Force Research Lab and Wall Street’s leading risk management experts from top investment banks such as JP Morgan. That work developed the original basis for understanding emerging Cyber Finance practices at the intersection of leading-edge developments in both Finance and Cybersecurity related risk and uncertainty management. In addition, it also developed robust computational quantitative finance modeling foundations for industrywide Cyber Risk Insurance Modeling practices.
Keywords: Princeton Quant Trading Conference, model risk arbitrage, open systems finance, model risk management, global financial markets, Knightian uncertainty & risk management, self-adaptive complex systems, hyper-turbulent environments, black swans, extreme events, Reflexivity Theory, Hegelian Dialectic
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