Dr. Yogesh Malhotra: LinkedIn: Risk Analytics Beyond 'Prediction' to 'Anticipation of Risk': Princeton University Presentations on the Future of Finance

Who's Who in America^{®},Who's Who in the World^{®},Who's Who in Finance&Industry^{®},Who's Who in Science & Engineering^{®}

2015 & 2016 Princeton Quantitative Trading Conference: Invited Presentations: Computational Quantitative Research,

2015-2016: 33 SSRN Top-10 Research Rankings: Computational Quantitative, Risk Analytics, & Algorithms Research,

2008: AACSB International Impact of Research Report: Named among Black-Scholes, Harry Markowitz & Bill Sharpe

Projects Goldman Sachs JP Morgan Wall Street Hedge Funds Princeton Presentations Model Risk Arbitrage Cyber Finance Cyber Risk Insurance Ventures

Bayesian vs. VaR Markov Chain Monte Carlo Models Mobile Trust Models Pen Testing Frameworks Bitcoin Cryptanalytics NFS Cryptanalytics Algorithms

Research Impact Future of Finance Beyond VaR Model Risk Management SR11-7 OCC2011-12 Future of Risk Cyber Risk SSRN Google Scholar Publications

2016 Princeton Quant Trading Conference, Princeton University, April 16, 2016

Model Risk Arbitrage for Open Systems Finance:

How to Navigate ‘Uncertainty’... When ‘Models’ Are ‘Wrong’... and ‘Knowledge’... ‘Imperfect’!

Knight Reconsidered Again: Risk, Uncertainty, & Profit beyond ZIRP & NIRP

Yogesh Malhotra, PhD

Global Risk Management Network, LLC

Cornell Business and Technology Park, Ithaca, New YorkThe 2016 invited 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.

Analytics & Frameworks Helping the World Model and Manage "True" Uncertainty for Over 20 Years'"It is this

"true" uncertainty, and not risk, as has been argued, whichforms the basis of a valid theory of profitandaccounts for the divergence between actual and theoretical competition... It is aworld of changein which we live, and aworld of uncertainty...If we are to understand the workings of theeconomic systemwe must examine themeaning and significance of uncertainty; and to this end someinquiry into the nature and function of knowledgeitself is necessary."

--Frank H. KnightinRisk, Uncertainty, and Profit

(Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Co), 1921.Computational Finance & Cybersecurity Risk Analytics: Computational Statistics Algorithms

Related Research & Applications:Enterprise Risk Management to Model Risk Management: Open Systems Dynamics View

Enterprise Risk Management, Model Risk Management, FinTech & Cyber Risk Management

Computational Statistics & Algorithms Research invited for Presentations at Princeton University

Publications List: Post-Doc Research, Books, Papers, Keynotes, Editorial Boards, Expert Panels

Computational Quantitative Analytics-Finance-Risk Management Projects

Research Impact in Business Press, Industry Surveys, & Scientific Studies:

AACSB Impact of Research among Finance Nobel Laureates such as Black-Scholes

Scientific Impact Studies Rankings among IT Nobel Laureates and Harvard Professors

CNet Networks Corporate Computing Award for Most Influential Research

Academy of Management Best Reviewer Award for Quantitative Structural & Statistical Models

2015-2016: 35 SSRN Top-10 Rankings in Computational Quantitative Risk Analytics-Econometrics-Algorithms

05/2016

Risk Management eJournal

Risk Management & Analysis in Financial Institutions eJournal

Econometrics: Econometric & Statistical Methods - Special Topics eJournal

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics02/2016

Corporate Governance: Disclosure, Internal Control, & Risk-Management eJournal

01/2016

CGN: Risk Management Practice

CGN: Risk Management, Including Hedging & Derivatives

Corporate Governance Practice Series eJournal

IRPN: Innovation & Cyberlaw & Policy

ISN: Property Protection05/2015

Econometrics: Mathematical Methods & Programming eJournal

Computational Techniques

Information Systems & Economics eJournal

04/2015

Econometrics: Mathematical Methods & Programming eJournal

Computational Techniques

03/2015

Econometric Modeling: Capital Markets - Risk eJournal

Econometric Modeling: Risk Management eJournal

Econometric Modeling: Capital Markets - Risk eJournal

Operations Research Network eJournal

OPER Subject Matter eJournal

Systemic Risk

Econometrics: Mathematical Methods & Programming eJournal

02/2015

Stochastic Models eJournal

Computational Techniques

OPER: Analytical

Other Econometrics: Mathematical Methods & Programming

Econometric & Statistical Methods - Special Topics eJournal

Microeconomics: Decision-Making under Risk & Uncertainty eJournal

VaR Value-at-Risk

Uncertainty & Risk Modeling

Econometric & Statistical Methods

01/2015

Econometric Modeling: Capital Markets - Risk eJournal

Microeconomics: Decision-Making under Risk & Uncertainty eJournal

Uncertainty & Risk Modeling

VaR Value-at-Risk.