Bayesian VaR Models Advancing beyond VaR Model Risks
Exposed by the Global Financial Crisis of 2008-2009

"The only Constant used to be Change... Even it is not Constant anymore...."
- Dr. Yogesh Malhotra, circa 2011 based on published research circa 1993-2008.

Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed).

"Given critical systemic risk related limitations of VaR market risk models underlying the recent financial crisis known to the Basel Committee as early as 2001, financial institutions must advance beyond traditional VaR models to more robust spectral risk measures."
-- Yogesh Malhotra in Measuring & Managing Financial Risks with Improved Alternatives Beyond Value-At-Risk (VaR) at Midtown Manhattan presentation at Fordham University, January 26, 2012.

Risk Magazine

"A review of trading book capital rules, due to be launched in March by the Basel Committee on Banking Supervision, will consider ditching value-at-risk as the main measure on which market risk capital is calculated, sources say - but it may not be easy to find a replacement."
-- Goodbye VaR? Basel to Consider Other Risk Metrics, Risk.Net, 28 Feb 2012.

Bank for International Settlements
-- Basel Committee Proposes Switch from VaR to Expected Shortfall to Better Capture 'Tail Risk', Bank for International Settlements (BIS), May 2012.


Advancing Beyond 'Normal' VaR for Managing Risk & Uncertainty

Dr. Yogesh Malhotra's Market Risk presentation of January 26, 2012, in which he strongly recommended market risk analysts to start looking beyond VaR and seriously considering Expected Shortfall models preceded subsequent "revelation" on February 28, 2012, by Risk.net that the Basel Committee was considering ditching VaR as a means of calculating market risk capital. Risk.Net reports about its February 28, 2012, article that their "February 2012 article broke the news that the Basel Committee was considering ditching VaR as a means of calculating market risk capital in favour of expected shortfall."

His research on managing the risks of black swan like events has been applied by worldwide firms and governments for more than a decade before the term 'black swan' became fashionable among analysts. His research is advancing the execution of SR11-7 and OCC 2011-12 Model Risk Management Guidance of OCC and US Federal Reserve System such as 'anticipation of risks' by 'effective challenge of models'. His presentation also highlighted critical systemic risk concerns about VaR underlying the financial crisis that were plausibly known to the Basel Committee for Banking Supervision as early as 2001.

His prior research into computational mathematical financial engineering models linked to the Global Financial Crisis preceded the Crisis by about 4-5 years culminating in his investigation of VaR and related financial risk models. For instance, his prescient reference to the "most technical, numbers-driven, globally popular area of financial markets" of financial engineering in course of related investigation is available in his invited interview published by the UK management press in 2005. Having affirmed the trajectory of his own post-doctoral quantitative risk modeling and risk management research in 2012 with the Columbia University Professor Emanuel Derman, world's most known expert on Model Risks in his view, who was prior MD and head of Quantitative Strategies group at Goldman Sachs, Dr. Malhotra is optimistic about a more enlightened future of quantitative risk modeling.

Quantitative Finance beyond Model Risks Exposed by the Global Financial Crisis

"If we are to understand the workings of the economic system we must examine the meaning and significance of uncertainty; and to this end some inquiry into the nature and function of knowledge itself is necessary." -- Frank H. Knight in Risk, Uncertainty, and Profit (Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Co), 1921

'Knight Reconsidered': Risk, Uncertainty, and Profit for the Cyber Era: Emerging Future of Finance
(Ithaca, NY: Global Risk Management Network, LLC), 2015.

Future of Finance: Knight meets Derman: Thesis on Model Risk Management for Risk & Uncertainty Management

"Unlike hard sciences such as physics or engineering in financial markets approximating the true underlying model means taking into account, as we formulate our models, how human beings like us actually learn, process information, and make decisions." — Dr. Paul D. McNelis in Neural Networks in Finance: Gaining Predictive Edge in the Market (Elsevier Academic Press Advanced Finance), 2005.

How Human Beings actually Learn, Process Information, & Make Decisions: Pioneering Knowledge Management
(Interview with the UK Management Press, 2005, Preview to 'Knight Reconsidered': Rethinking Risk & Controls for Managing Knightian Uncertainty & Complexity)
[Among other Pioneers of Knowledge Management Global Research & Practices]

Model Risk Management: Leading the Industry Leaders & Learning from Them

Risk Management and Asset Valuations in Quantitative Finance are primarily about information and how people use information for decision-making regarding risks, returns, and, valuations. Finance experts such as Emanuel Derman and Paul Wilmott have been quite vocal about how related key assumptions underlying the quantitative finance models for risk management and asset pricing deserve particular attention. They underscore the critical need for advancing global Quantitative Finance models to effectively deal with the real '(Mis)behaviour of Markets', as noted Yale mathematician-economist Benoit B. Mandelbrot would have said, and, with 'Thinking, Fast and Slow' as psychologist-economist Daniel Kahneman would have observed. In the above context, risk management and quantitative modeling expert Yogesh Malhotra's focus on quantitative finance models is anchored in his fundamental research and his applied practices in Computational Quantitative Finance-Risk Management for Top Wall Street Investment Banks with $1 Trillion AUM as well as how people and organizations use information and systems for decision-making to manage uncertainty. His fundamental research and applied practices on Model Risk Management are known for guiding worldwide corporations, governments, and institutions. His applied focus is on advancing computational and mathematical quantitative finance models for risk management, asset valuation, risk arbitrage, and, trading and hedging strategies beyond model risks exposed by the global financial crisis. Such advanced models and strategies are critical for pre-empting and managing risks resulting from increasing recurrence of radical discontinuous changes, popularly known as 'extreme events' and 'black swans' in the aftermath of the Global Financial Crisis that perhaps represented the most visible failure of global financial systems in recent history.

"The only Constant used to be Change... Even it is not Constant anymore..." — Yogesh Malhotra

Advancing Beyond Limitations of Quantitative Finance Models

"As far as the propositions of mathematics refer to reality they are not certain, and so far as they are certain, they do not refer to reality." — Albert Einstein (1879-1955) U. S. physicist, born in Germany.

“The models, according to finance experts and economists, did fail to keep pace with the explosive growth in complex securities, the resulting intricate web of risk and the dimensions of the danger. But the larger failure, they say, was human — in how the risk models were applied, understood and managed... If the incentives and the systems change, the hard data can mean less than it did or something else than it did…The danger is that the modeling becomes too mechanical….The miss by Wall Street analysts shows how models can be precise out to several decimal places, and yet be totally off base… Indeed, the behavioral uncertainty added to the escalating complexity of financial markets help explain the failure in risk management. The quantitative models typically have their origins in academia and often the physical sciences. In academia, the focus is on problems that can be solved, proved and published — not messy, intractable challenges. In science, the models derive from particle flows in a liquid or a gas, which conform to the neat, crisp laws of physics. Not so in financial modeling. To confuse the model with the world is to embrace a future disaster driven by the belief that humans obey mathematical rules.

--- 'In Modeling Risk, the Human Factor Was Left Out' - The New York Times, November 5, 2008

Modeling the 'Human Factor' Underlying Behavior & Performance: Empirical Research Papers

“I began to believe it was possible to apply the methods of physics successfully to economics and finance, perhaps even to build a grand unified theory of securities….After twenty years on Wall Street I’m a disbeliever. The similarity of physics and finance lies more in their syntax than their semantics. In physics you’re playing against God, and He doesn’t change His laws very often. In finance you’re playing against God’s creatures, agents who value assets based on their ephemeral opinions.

--- Dr. Emanuel Derman, Columbia University Professor, ex-Goldman Head of Quantitative Trading, and author of the book My Life as a Quant in his new book Models Behaving Badly: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life, 2011.

“The complex financial models that got us into this mess too often mask human nature behind false limitations of risk ...Financial theory has tried hard to emulate physics and discover its own elegant, universal laws. But finance and economics are concerned with the human world of monetary value. Markets are made of people who are influenced by events, by their feelings about events, and by their expectations of other people's feelings about events...Financial theories written in mathematical notation - aka models - imply a false sense of precision. Good modelers know that... Financial markets are alive. A model, however beautiful, is an artifice. To confuse the model with the world is to embrace a future disaster in the belief that humans obey mathematical principles.”

--- Dr. Emanuel Derman, and, Dr. Paul Wilmott in Financial Models Must Be Clean and Simple, Business Week, Bloomberg, December 31, 2008.

Of course, assets are not really geometric Brownian motions with constant volatility…”“Of course, stock price movements are much more complicated than indicated by the binomial asset-pricing model…”“Of course, the actual probability for the occurrence of any particular [stock price] path is zero…”

--  Dr. Steve E. Shreve and co-authors in Stochastic Calculus for Finance II: Continuous-Time Models, Springer, 2010; Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, Springer, Jun 28, 2005; Brownian Motion and Stochastic Calculus, Springer, 1991.

“We are now in a position to introduce a very important principle in the pricing of derivatives known as risk-neutral valuation. This states that, when valuing a derivative, we can make the assumption that investors are risk-neutral. This assumption means investors do not increase the expected return they require from an investment to compensate for increased risk. A world where investors are risk-neutral is referred to as a risk-neutral world... The world we live in is, of course, not a risk-neutral world. The higher the risks investors take, the higher the expected returns they require.”

-- Dr. John C. Hull in Options, Futures, and Other Derivatives, Prentice-Hall, 2011.

2015-2018: 63 SSRN Top-10 Research Rankings: Top-2% SSRN Authors:
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Recent Research Presentations and Research Reports
*2018 Princeton FinTech & Quant Conference: Invited Research Presentation: AI-Machine Learning-Deep Learning MRM.
*2018 MIT Sloan-Computer Sc. & AI Lab AI-Machine Learning Executive Guide including RPA & Cognitive Automation
*2018 Journal of Operational Risk, March: Toward 'Cyber-Finance’ Cyber Risk Management Frameworks of Practice.
*2017 National Association of Insurance Commissioners, June , Advancing Cyber Risk Insurance beyond VaR Models.
*2017 IUP Journal of Computer Sciences, April, Quantitative Modeling of Trust and Trust Management Protocols.
*Stress Testing for Cyber Risks: Cyber Risk Insurance Modeling beyond Value-at-Risk: Risk, Uncertainty, & Profit.
*Toward Integrated Enterprise Risk Management, Model Risk Management & Cyber-Finance Risk Management.
*Bridging Networks, Systems and Controls Frameworks for Cybersecurity Curricula & Standards Development.
*Advancing Cognitive Analytics Using Quantum Computing for Next Generation Encryption.
*Invited Princeton Quant Trading Presentations: 'Rethinking Finance' for the Era of Global Networked Digital Finance.
*2016 Princeton Quant Trading Presentation: Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era.
*2015 Princeton Quant Trading Presentation: Future of Finance Beyond 'Flash Boys': Managing Uncertainty.
Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics, Operations, &, Intelligence: ERM to MRM.
*A Risk Management Framework for Penetration Testing of Global Banking & Finance Networks VoIP Protocols.
*CyberFinance: Why Cybersecurity Risk Analytics Must Evolve to Survive 90% of Emerging Cyber Financial Threats.
*Beyond 'Bayesian vs. VaR' Dilemma: How to Manage Risk (After Risk Management Has Failed) for Hedge Funds.
Measuring & Managing Financial Risks with Improved Alternatives Beyond Value-at-Risk (VaR).
*Markov Chain Monte Carlo Models for High-Dimensionality Complex Stochastic Problems in Network Security.
*Risk, Uncertainty, & Profit for the Cyber Era: 'Knight Reconsidered': Model Risk Management in Cyber Risk Insurance.
*Cyber-Finance Risk Management: Strategies, Tactics, Operations, &, Intelligence: ERM to Model Risk Management.
*Number Field Sieve Cryptanalytic Algorithms for Most Efficient Prime Factorization on Composites: Beyond RSA 1024.
*Future of Bitcoin & Statistical Probabilistic Quant Methods: Global Financial Regulation: Hong Kong Institute of CPAs.
*Bitcoin Protocol & Bitcoin Block Chain: Model of 'Cryptographic Proof' based Global Crypto-Currency Payment Systems.
*2015-2018 63 SSRN Top-10 Rankings: Computational Quant & Risk Analytics Algorithms Machine Learning Research.
*2008 AACSB International Impact of Research Report: Among Black-Scholes, Markowitz, Sharpe, Modigliani & Miller.

Top Wall Street Investment Banks Quantitative Finance Projects & FinTech Ventures
Princeton: Future of Finance: 'Rethinking Finance' for Era of Global Networked Digital Finance
2017 National Association of Insurance Commissioners: Expert Paper: Cyber Risk Insurance Modeling
2016 Princeton Quant Trading Conference: Invited Research Presentation: Model Risk Arbitrage
2015 Princeton Quant Trading Conference: Invited Research Presentations: Future of Finance
Quantitative Finance Risk Analytics Modeling Wall Street Investment Banks & VC Projects
Model Risk Management: Risk Management Analytics from 'Prediction' to 'Anticipation of Risk'
Quantitative Finance Risk Analytics, Econometric Analytics, Numerical Programming Models
Quantitative Finance Model Risk Management for Systemic-Tail Risks in Cyber Risk Insurance
JP Morgan Portfolio Optimization, VaR & Stress Testing: 17-Asset Class Portfolio
JP Morgan Portfolio Liquidity Risk Modeling Framework for $500-600Bn Portfolio
Bayesian VaR Beyond Value-At-Risk (VaR) Model Risks Exposed by Global Financial Crisis
Goldman Sachs Alumnus Asset Manager Large-Scale Data High Freq Econometric Models
Quantitative Finance, Risk Modeling, Econometric Modeling, Numerical Programming
Technologies of Computational Quantitative Finance & Risk Analytics and Risk Management
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Cybersecurity, Financial Protocols & Networks Protocols Analysis, and, Penetration Testing
Impact: Quantitative Finance, Quantitative Risk Analytics & Risk Management Projects
Digital Social Enterprise Ventures Creating Trillion $ Practices for Hundreds of Millions

Named among FinTech Finance & IT Nobel laureates for Real World Impact of Research
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63 SSRN Top-10 Rankings: AI, Algorithms & Machine Learning; Cybersecurity; Computational Quant Finance
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Model Risk Management of Cyber Risk Insurance Models & Quantitative Finance Analytics
Thesis on Ongoing Convergence of Financial Risk Management & Cyber Risk Management
U.S. Federal Reserve & Office of the Comptroller of the Currency Model Risk Guidance
Bayesian VaR Beyond Value-At-Risk (VaR) Model Risks Exposed by Global Financial Crisis
Markov Chain Monte Carlo Models & Algorithms to Enable Bayesian Inference Modeling
OCC Notes Cybersecurity Risk & Cyber Attacks as Key Contributor to Banks' Financial Risk
Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Global Financial Regulation
Models Validation Expert Panels: IT, Operations Research, Economics, Computer Science

Global, National, & Enterprise CxO Level FinTech-Cyber-Risk Analytics Ventures
CxO Think Tank that pioneered 'Digital' Management of Risk, Uncertainty, & Complexity
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Air Force Research Laboratory Commercialization Academy Venture: Building the Future of Artificial General Intelligence.
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CFA Society: Invited Keynote: JP Morgan & Goldman Sachs Practices Case Studies: Model Risk Management with Auto-ML.
RISK.Net: Bridging Networks, Systems and Controls Frameworks for Cybersecurity Curriculums and Standards Development.
NAIC: National Association of Insurance Commissioners: Pre-empting the Forthcoming Global Cyber Risk Insurance Crisis.
AFCEA C4I & Cyber Conference: Cybersecurity Risk & Uncertainty Management: AI-ML and Risk Management Controls.
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