This document summarizes a presentation on risk modeling for managing uncertainty in an increasingly non-deterministic cyber world. The presentation discusses how exponential increases in cyber risks characterize and subsume traditional financial risks. It advocates moving beyond Value at Risk models and the Bayesian vs VaR dilemma to empirical model risk management. The presenter's research over two decades aims to anticipate surprise when prediction of risk is infeasible. He will facilitate discussion on issues regarding the future of finance and risk modeling.
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.
AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
Cornerstones of Trust - Hacking the CEO: Ninja Mind Tricks and other Ruses to...Denim Group
If you’ve ever had your security budget eviscerated, this is your session! CEOs have become more wary of scare tactics. What are the tricks to get more security dollars? Discover how to get inside the CEO’s head to get exec support and how to use pet projects to pry scarce resources from budgets. I will share common data points from interviews with over 40 CISOs that have been there and done that.
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.
AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
Cornerstones of Trust - Hacking the CEO: Ninja Mind Tricks and other Ruses to...Denim Group
If you’ve ever had your security budget eviscerated, this is your session! CEOs have become more wary of scare tactics. What are the tricks to get more security dollars? Discover how to get inside the CEO’s head to get exec support and how to use pet projects to pry scarce resources from budgets. I will share common data points from interviews with over 40 CISOs that have been there and done that.
Presentation for the 2016 National and Chapter Leadership Conference by Bill ...RedZone Technologies
The goal of the Presentation at the AGC of America 2016 National and Chapter Leadership Conference. My goal was to educate about the importance of innovating and applying exponential technologies in IT Security within the organization. Another goal was to share with my audience how to measure risk, and have risk-based conversations that a business person can understand. The audience included many professionals in the construction industry, so it was important for me to be able to convey the importance of cybersecurity in that industry.
The key points in this video not only apply to those in the construction industry, but to industries and businesses of all types. I urge you to watch and discover why cybersecurity should not only be an IT concern, but a business and strategic concern as well.
https://www.youtube.com/watch?v=N1_KWHFNMmI&feature=youtu.be
How Robo Advisers, Fintech Are Revolutionising Wealth ManagementDinis Guarda
How Robo Advisers, Fintech Are Revolutionising Wealth Management. A Reflection and presentation about trends and ideas related with the topic and what is happening in the industry
This presentation is about the use of technology and innovative business models in financial services. It was presented at a conference entitled "Disruptive Innovations in Financial Services" sponsored by the Institute for Financial Services Analytics (IFSA) in the Lerner College of Business and Economics at the University of Delaware on March 3, 2016.
The convergence of non-traditional rivals and heightened global regulation are creating new digital opportunities for banks. To seize the high ground, banks need to think like disruptors and apply modern digital tools, techniques and partnership strategies.
20170118 Presentatie 'Overleven in een wereld van fintechs v1.0' Pascal Spelier
Deze presentatie gaf ik op een nieuwjaarsbijeenkomst voor hoofdzakelijk financieel adviseurs. Welke ontwikkelingen zien we in de wereld van FinTech? Hoe kunnen financieel adviseurs overleven in een snel veranderende omgeving, waarin Fintechs een steeds belangrijker rol gaan spelen? Wil je meer weten over deze presentatie of andere presentaties die ik geef, neem dan contact met mij op: www.finno.nl / pascal(punt)spelier(apedingetje)finno(punt)nl.
Finatix - The Finance Club of IIM Raipur presents
"Atharva - The Annual Finance Magazine 2018" which is the 4th edition of this kind.
The 4th edition is published with theme "Global FInancial Risks".
Magazine has cover story by Ms.Surbhi Agarwal, Director, HSBC (HK) followed by interview with Ms.Lakshmi Iyer, CIO, Kotak Mahindra AMC.
Apart from this, a national level competition - "Atharva - The Article Writing Competition" - is organized every year by Finatix and top 5 articles selected are printed in this magazine.
This magazine also contains insights of the year 2018 in brief.
Building AI Driven Marketing Capabilities: Understand Customer Needs and Deli...Lucky Gods
Unlock the Superpower of AI Marketing: Building AI-Driven Capabilities to Wow Your Customers!
Imagine this:
Reading your customers' minds (well, kind of) , knowing their desires before they even say a word.
Craft campaigns so personalized, they feel like handwritten love letters to each individual.
Turning your marketing into a lean, mean, data-driven machine, crushing your goals .
Ready to ditch the guesswork and embrace the future of marketing?
"Building AI-Driven Marketing Capabilities" is your roadmap to AI mastery. You'll learn to:
Decode the secrets of AI: Demystify algorithms, machine learning, and all the tech lingo.
Become a customer whisperer: Uncover hidden needs and desires with powerful data analysis.
Craft AI-powered campaigns: Design personalized experiences that resonate deeply with your audience.
Boost efficiency and optimize everything: Automate tasks, analyze results, and constantly improve.
Deliver real value: Go beyond engagement metrics and measure true business impact.
No prior AI experience needed! This book starts with the basics and takes you step-by-step through building a rock-solid AI marketing foundation.
So, are you ready to:
Leave your competitors in the dust?
Turn data into dollars?
Experience the magic of AI marketing?
Grab your copy of "Building AI-Driven Marketing Capabilities" today and unleash the power of AI to transform your business!
P.S. Don't miss out on these bonus resources:
Interactive quizzes and exercises to put your learnings into action.
Case studies of successful AI marketing campaigns for inspiration.
A curated list of AI tools and resources to get you started.
Etude PwC/CSFI "Banking Banana Skins" sur les risques dans le secteur bancair...PwC France
http://pwc.to/1fomUfC
Selon la 11ème édition de l’étude biennale du CSFI, Banking Banana Skins, réalisée en association avec PwC, l’excès de réglementation et les interventions politiques sont les deux principaux risques identifiés par les dirigeants du secteur bancaire interrogés, devançant l’environnement économique mondial, identifié comme le principal risque en 2012.
Poonawalla Fincorp and IndusInd Bank Introduce New Co-Branded Credit Cardnickysharmasucks
The unveiling of the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card marks a notable milestone in the Indian financial landscape, showcasing a successful partnership between two leading institutions, Poonawalla Fincorp and IndusInd Bank. This co-branded credit card not only offers users a plethora of benefits but also reflects a commitment to innovation and adaptation. With a focus on providing value-driven and customer-centric solutions, this launch represents more than just a new product—it signifies a step towards redefining the banking experience for millions. Promising convenience, rewards, and a touch of luxury in everyday financial transactions, this collaboration aims to cater to the evolving needs of customers and set new standards in the industry.
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Similar to 2015 Princeton Quant Trading Conference, Future of Finance: Cyber Finance
Presentation for the 2016 National and Chapter Leadership Conference by Bill ...RedZone Technologies
The goal of the Presentation at the AGC of America 2016 National and Chapter Leadership Conference. My goal was to educate about the importance of innovating and applying exponential technologies in IT Security within the organization. Another goal was to share with my audience how to measure risk, and have risk-based conversations that a business person can understand. The audience included many professionals in the construction industry, so it was important for me to be able to convey the importance of cybersecurity in that industry.
The key points in this video not only apply to those in the construction industry, but to industries and businesses of all types. I urge you to watch and discover why cybersecurity should not only be an IT concern, but a business and strategic concern as well.
https://www.youtube.com/watch?v=N1_KWHFNMmI&feature=youtu.be
How Robo Advisers, Fintech Are Revolutionising Wealth ManagementDinis Guarda
How Robo Advisers, Fintech Are Revolutionising Wealth Management. A Reflection and presentation about trends and ideas related with the topic and what is happening in the industry
This presentation is about the use of technology and innovative business models in financial services. It was presented at a conference entitled "Disruptive Innovations in Financial Services" sponsored by the Institute for Financial Services Analytics (IFSA) in the Lerner College of Business and Economics at the University of Delaware on March 3, 2016.
The convergence of non-traditional rivals and heightened global regulation are creating new digital opportunities for banks. To seize the high ground, banks need to think like disruptors and apply modern digital tools, techniques and partnership strategies.
20170118 Presentatie 'Overleven in een wereld van fintechs v1.0' Pascal Spelier
Deze presentatie gaf ik op een nieuwjaarsbijeenkomst voor hoofdzakelijk financieel adviseurs. Welke ontwikkelingen zien we in de wereld van FinTech? Hoe kunnen financieel adviseurs overleven in een snel veranderende omgeving, waarin Fintechs een steeds belangrijker rol gaan spelen? Wil je meer weten over deze presentatie of andere presentaties die ik geef, neem dan contact met mij op: www.finno.nl / pascal(punt)spelier(apedingetje)finno(punt)nl.
Finatix - The Finance Club of IIM Raipur presents
"Atharva - The Annual Finance Magazine 2018" which is the 4th edition of this kind.
The 4th edition is published with theme "Global FInancial Risks".
Magazine has cover story by Ms.Surbhi Agarwal, Director, HSBC (HK) followed by interview with Ms.Lakshmi Iyer, CIO, Kotak Mahindra AMC.
Apart from this, a national level competition - "Atharva - The Article Writing Competition" - is organized every year by Finatix and top 5 articles selected are printed in this magazine.
This magazine also contains insights of the year 2018 in brief.
Building AI Driven Marketing Capabilities: Understand Customer Needs and Deli...Lucky Gods
Unlock the Superpower of AI Marketing: Building AI-Driven Capabilities to Wow Your Customers!
Imagine this:
Reading your customers' minds (well, kind of) , knowing their desires before they even say a word.
Craft campaigns so personalized, they feel like handwritten love letters to each individual.
Turning your marketing into a lean, mean, data-driven machine, crushing your goals .
Ready to ditch the guesswork and embrace the future of marketing?
"Building AI-Driven Marketing Capabilities" is your roadmap to AI mastery. You'll learn to:
Decode the secrets of AI: Demystify algorithms, machine learning, and all the tech lingo.
Become a customer whisperer: Uncover hidden needs and desires with powerful data analysis.
Craft AI-powered campaigns: Design personalized experiences that resonate deeply with your audience.
Boost efficiency and optimize everything: Automate tasks, analyze results, and constantly improve.
Deliver real value: Go beyond engagement metrics and measure true business impact.
No prior AI experience needed! This book starts with the basics and takes you step-by-step through building a rock-solid AI marketing foundation.
So, are you ready to:
Leave your competitors in the dust?
Turn data into dollars?
Experience the magic of AI marketing?
Grab your copy of "Building AI-Driven Marketing Capabilities" today and unleash the power of AI to transform your business!
P.S. Don't miss out on these bonus resources:
Interactive quizzes and exercises to put your learnings into action.
Case studies of successful AI marketing campaigns for inspiration.
A curated list of AI tools and resources to get you started.
Etude PwC/CSFI "Banking Banana Skins" sur les risques dans le secteur bancair...PwC France
http://pwc.to/1fomUfC
Selon la 11ème édition de l’étude biennale du CSFI, Banking Banana Skins, réalisée en association avec PwC, l’excès de réglementation et les interventions politiques sont les deux principaux risques identifiés par les dirigeants du secteur bancaire interrogés, devançant l’environnement économique mondial, identifié comme le principal risque en 2012.
Poonawalla Fincorp and IndusInd Bank Introduce New Co-Branded Credit Cardnickysharmasucks
The unveiling of the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card marks a notable milestone in the Indian financial landscape, showcasing a successful partnership between two leading institutions, Poonawalla Fincorp and IndusInd Bank. This co-branded credit card not only offers users a plethora of benefits but also reflects a commitment to innovation and adaptation. With a focus on providing value-driven and customer-centric solutions, this launch represents more than just a new product—it signifies a step towards redefining the banking experience for millions. Promising convenience, rewards, and a touch of luxury in everyday financial transactions, this collaboration aims to cater to the evolving needs of customers and set new standards in the industry.
How to get verified on Coinbase Account?_.docxBuy bitget
t's important to note that buying verified Coinbase accounts is not recommended and may violate Coinbase's terms of service. Instead of searching to "buy verified Coinbase accounts," follow the proper steps to verify your own account to ensure compliance and security.
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Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
how can i use my minded pi coins I need some funds.DOT TECH
If you are interested in selling your pi coins, i have a verified pi merchant, who buys pi coins and resell them to exchanges looking forward to hold till mainnet launch.
Because the core team has announced that pi network will not be doing any pre-sale. The only way exchanges like huobi, bitmart and hotbit can get pi is by buying from miners.
Now a merchant stands in between these exchanges and the miners. As a link to make transactions smooth. Because right now in the enclosed mainnet you can't sell pi coins your self. You need the help of a merchant,
i will leave the telegram contact of my personal pi merchant below. 👇 I and my friends has traded more than 3000pi coins with him successfully.
@Pi_vendor_247
Exploring Abhay Bhutada’s Views After Poonawalla Fincorp’s Collaboration With...beulahfernandes8
The financial landscape in India has witnessed a significant development with the recent collaboration between Poonawalla Fincorp and IndusInd Bank.
The launch of the co-branded credit card, the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card, marks a major milestone for both entities.
This strategic move aims to redefine and elevate the banking experience for customers.
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
Flexible Credit Requirements: USDA loans have more lenient credit score requirements, helping those with less-than-perfect credit.
Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
Loan Application: Submit your application, including financial and personal information.
Processing and Approval: The lender and USDA will review your application. If approved, you can proceed to closing.
USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
how to sell pi coins on Bitmart crypto exchangeDOT TECH
Yes. Pi network coins can be exchanged but not on bitmart exchange. Because pi network is still in the enclosed mainnet. The only way pioneers are able to trade pi coins is by reselling the pi coins to pi verified merchants.
A verified merchant is someone who buys pi network coins and resell it to exchanges looking forward to hold till mainnet launch.
I will leave the telegram contact of my personal pi merchant to trade with.
@Pi_vendor_247
how to sell pi coins at high rate quickly.DOT TECH
Where can I sell my pi coins at a high rate.
Pi is not launched yet on any exchange. But one can easily sell his or her pi coins to investors who want to hold pi till mainnet launch.
This means crypto whales want to hold pi. And you can get a good rate for selling pi to them. I will leave the telegram contact of my personal pi vendor below.
A vendor is someone who buys from a miner and resell it to a holder or crypto whale.
Here is the telegram contact of my vendor:
@Pi_vendor_247
Introduction to Indian Financial System ()Avanish Goel
The financial system of a country is an important tool for economic development of the country, as it helps in creation of wealth by linking savings with investments.
It facilitates the flow of funds form the households (savers) to business firms (investors) to aid in wealth creation and development of both the parties
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
2015 Princeton Quant Trading Conference, Future of Finance: Cyber Finance
1. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[1]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Knight Reconsidered: Risk, Uncertainty, and Profit for the Cyber Era:
Future of Finance: Cyber-Finance?:
Uncertainty Modeling & Model Risk Management
Yogi
Yogesh Malhotra
PhD, MSQF, MSCS, MSNCS, MSAcc, MBAEco
BE, CEng, CISSP, CISA, CEH, CCP/CDP
www.yogeshmalhotra.com (646) 770-7993 dr.yogesh.malhotra@gmail.com
Global Risk Management Network, LLC
757 Warren Road, Cornell Business & Technology Park, Ithaca, NY 14852-4892
http://www.FutureOfFinance.org/Princeton.ppsx
2. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[2]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Knight’s Risk, Uncertainty, and Profit of 1921
- Emergence of the World Wide Web in early-1990s,
- Derman’s Model Risk Management, Goldman Sachs, mid-late-1990s,
- Backlash against quantitative models after Financial Crisis, 2008,
- Basel to Consider Risk Metrics other than VaR, Feb 2012,
- Post-Snowden Cyber era starting May 2013...
Information-based view of Financial risk modeling practices,
- Exponentially increasing Cyber era uncertainty,
- Cyber-Finance, the emerging Future of Finance?
- Risk modeling focus on metaphorical ‘tip’ of the iceberg,
- Significant risks not readily meet the human eye.
- Cyber-risk, the predominant risk… subsumes traditional risks.
3. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[3]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Exponentially increasing tail risks and systemic risks
- Characterize highly systemic, interdependent, correlated Cyber-risks
- Cyber risks in turn characterize financial risks.
Based on research of two-decades
- Starting around the emergence of the WWW (1993), Philosophy of IS
- How to ‘anticipate surprise’ when ‘prediction’ of risk is infeasible,
- Wall Street Chief Risk Officers, Top Investment Bank CxOs, Fed/OCC
- http://www.yogeshmalhotra.com/blackswans.html
- Explore advances in quantitative risk models, statistical methodologies,
and, computational statistical technologies
Facilitate dialog on the above issues of central concern
- Future of Finance
- Future of Risk.
4. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[4]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
SSRN Top-10 Papers: 20 Quantitative Finance-Risk Analytics Top-10 Rankings in recent 11 Weeks:
1. Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds (Malhotra 2014).
2. Risk, Uncertainty, and Profit for the Cyber Era: Model Risk Management of Cyber Insurance Models
Using Quantitative Finance & Advanced Analytics (Malhotra 2015).
3. Markov Chain Monte Carlo Models, Gibbs Sampling & Metropolis Algorithm for High-Dimensionality
Complex Stochastic Problems (Malhotra 2014).
4. Extending Above Observations to High Frequency Trading: FIX, FAST (Beyond ‘Flash Boys’).
5. A Risk Management Framework for Penetration Testing of Global Banking & Finance Networks VoIP
Protocols (Malhotra 2014).
6. Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Interview, Hong Kong Institute of CPAs
(Malhotra 2014).
7. Bitcoin Protocol: Model of ‘Cryptographic Proof’Based Global Crypto-Currency & Electronic Payments
Systems (Malhotra 2013).
8. Cryptology Beyond Shannon's Information Theory: Number Field Sieve Cryptanalysis Algorithms for Most
Efficient Prime Factorization on Composites (Malhotra 2013).
9. Quantum Computing, Quantum Cryptography, Shannon’s Entropy and Next Generation Encryption &
Decryption (Malhotra 2013)… One more reference about Shannon’s Information Theory (Malhotra 2001)
5. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[5]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
How to Manage Risk (After Risk Management Has Failed) Fall 2010 Vol. 52
Bayesian modeling instead of VaR would minimize risk management failures
- Given key role of ‘subjective judgment’ in the Bayesian methodology
Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds (Malhotra 2014)
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2538401
- Subjective judgment … Bayesian priors… key limitation of Bayesian methodology
- Since before the Crisis, non-Bayesian and Bayesian VaR models in Finance practice
- Bayesian vs. VaR dilemma needs to be resolved
- To minimize model specification and estimation errors.
- Model Risk Management is crucial for VaR, Bayesian, and Bayesian VaR
6. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[6]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds, 2014.
Examples of Multi-Portfolio Asset Classes Modeled
Developed Large Equity
Developed Small Equity
Emerging Market Equity
Listed Private Equity
Various Commodities
Government Bonds
Investment Grade Bonds
Inflation-Linked Bonds
High Yield Corporate Bonds
Emerging Market Hard Currency Bonds
Emerging Market Local Currency Bonds
Major Currencies
Statistical Arbitrage Hedge Fund
Event Driven Hedge Fund (HFRIEDI)
Equity Hedge Fund (HFRIEHI)
Merger Arbitrage Hedge Fund
Macro Strategy Hedge Fund
Relative Value Hedge Fund
Advancing upon: Measuring & Managing Financial Risks with Improved Alternatives Beyond
Value-At-Risk (VaR), Jan. 26, 2012. http://www.yogeshmalhotra.com/BeyondVaR_YogeshMalhotra.pdf
7. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[7]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
8. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[8]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
9. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[9]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
10. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[10]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
11. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[11]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
Historical Simulation VaR
Parametric VaR
Modified VaR
TABLE 8 (b) VaR and Expected Shortfall: Optimization Portfolios based upon Minimum Variance
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
VaR95% = -$783,190
VaR95% = -$783,190
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
VaR95% = -$1,284,507
VaR95% = -$1,284,507
30
35
40
45
50
MVaR95% = -$1,884,524
PORT Index AuM ($) 100,000,000
Confidence Level 95%
Critical Value (zα ) 1.645
Monthly VaR95% ($)
-785,392
-783,190
PORT AuM ($) 100,000,000
Variance (Min.) 0.61
St. Dev. 0.78%
Confidence Level 95%
Critical Value (z α ) 1.645
PORT Index AuM ($) 100,000,000
Mean P&L ($) 533,059
12. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[12]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
Parametric VaR
Modified VaR
Expected Shortfall
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
VaR95% = -$1,284,507
VaR95% = -$1,284,507
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
MVaR95% = -$1,884,524
MVaR95% = -$1,884,524
PORT AuM ($) 100,000,000
Variance (Min.) 0.61
St. Dev. 0.78%
Confidence Level 95%
Critical Value (z α ) 1.645
PORT Index AuM ($) 100,000,000
Mean P&L ($) 533,059
St. Dev. P&L ($) 815,251
Confidence Level 95%
PORT Index AuM ($) 100,000,000
Mean P&L ($) 533,059
St. Dev. P&L ($) 815,251
Confidence Level 95%
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
MVaR95% = -$1,681,629
MVaR95% = -$1,681,629
13. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[13]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
14. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[14]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
15. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[15]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
Historical Simulation VaR
Parametric VaR
Modified VaR
TABLE 8 (c) VaR and Expected Shortfall: Optimization Portfolios based upon Maximizing Return
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
VaR95% = -$2,764,562
VaR95% = -$2,764,562
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
VaR95% = -$3,766,260
VaR95% = -$3,766,260
25
30
35
40
45
50
MVaR95% = -$5,733,689
PORT Index AuM ($) 100,000,000
Confidence Level 95%
Critical Value (zα ) 1.645
Monthly VaR95% ($)
-785,392
-783,190
PORT AuM ($) 100,000,000
Variance (Min.) 5.24
St. Dev. 2.29%
Confidence Level 95%
Critical Value (z α ) 1.645
PORT Index AuM ($) 100,000,000
Mean P&L ($) 994,581
16. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[16]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
Parametric VaR
Modified VaR
Expected Shortfall
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
VaR95% = -$3,766,260
VaR95% = -$3,766,260
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
MVaR95% = -$5,733,689
MVaR95% = -$5,733,689
0
5
10
15
20
25
30
35
40
45
50
-22000000
-21000000
-20000000
-19000000
-18000000
-17000000
-16000000
-15000000
-14000000
-13000000
-12000000
-11000000
-10000000
-9000000
-8000000
-7000000
-6000000
-5000000
-4000000
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
11000000
12000000
13000000
14000000
Frequency
Simulated P&L ($)
MVaR95% = -$4,575,377
MVaR95% = -$4,575,377
PORT AuM ($) 100,000,000
Variance (Min.) 5.24
St. Dev. 2.29%
Confidence Level 95%
Critical Value (z α ) 1.645
PORT Index AuM ($) 100,000,000
Mean P&L ($) 994,581
St. Dev. P&L ($) 2,218,136
Confidence Level 95%
PORT Index AuM ($) 100,000,000
Mean P&L ($) 994,581
St. Dev. P&L ($) 2,218,136
Confidence Level 95%
17. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[17]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
18. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[18]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
19. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[19]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
Sophistication and Complexity of models… two-edged sword,
-Simple models preferred… if help understanding the assumptions and limits,
-Complex models increase model risk… if obfuscate understanding and clarity.
Regardless of which model seems relatively superior… it may still not be a
good model of the data, but the least worse of the models that are compared.
Evaluation of complex integrals [in denominator of Bayes’ formula] over high
dimensional parameter space… major challenge for actual Bayesian analysis.
- Model with 8 parameters, each with 1E3 values: 8-D parameter space
contains 1E24 combinations of parameter values: computationally complex.
20. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[20]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
21. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[21]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
22. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[22]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
To VaR or Not to VaR? Why VaR & Why ES or EVT?
- Systemic, highly correlated, and interdependent nature of Cyber risks
- VaR not appropriate model given the ‘systemic’ nature of Cyber risks
- VaR doesn’t satisfy ‘subadditivity’ criterion of ‘coherent risk measures.’
Historical Simulation Based VaR: relies upon historical correlations;
MC based VaR: can use any statistical distribution (including normal), relies
on several assumptions about specific statistical distributions chosen and
extensive computing power or statistical computing algorithms (MCMC).
Parametric VaR: relies upon statistical linearity and normality assumptions;
Risk, Uncertainty, and Profit for the Cyber Era: Model Risk
Management of Cyber Insurance Models Using Quantitative Finance
and Advanced Analytics. (Malhotra 2015)
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553547
23. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[23]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Modified VaR:
Expected Shortfall (ES, ETL, T-VaR, CTE):
24. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[24]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Markov Chain Monte Carlo Models, Gibbs Sampling & Metropolis Algorithm
for High-Dimensionality Complex Stochastic Problems. (Malhotra 2014)
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553537
Bayesian inference for doing high dimension parameter space analyses
- Feasible with Markov Chain Monte Carlo statistical computing algorithms
- Metropolis Hastings algorithm and Gibbs Sampling algorithm
MCMC: A common general quantitative method to find approximate
solutions to computationally complex problems in polynomial time…
Polynomial Time O(nk) s.t. k > 1
Exponential Time O(kn) s.t. k > 1
n = length of input
Source:
stackoverflow.com
25. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[25]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Markov Chain Monte Carlo Models, Gibbs Sampling & Metropolis Algorithm
for High-Dimensionality Complex Stochastic Problems.
Gibbs Sampling: Generating random variables from a marginal distribution
indirectly without the need for calculating the distribution density.
-E.g. Solve complex multivariate stochastic model with N parameters (i.e. N-
Dim.) using N univariate (i.e., one-dimensional) conditional distributions.
26. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[26]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Dropping the burn-in sample of first m draws
≈
Metropolis Algorithm :
27. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[27]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Metropolis-Hastings Algorithm :
Metropolis-Hastings Algorithm = Metropolis Algorithm when
Risk, Uncertainty, and Profit for the Cyber Era: Model Risk
Management of Cyber Insurance Models Using Quantitative Finance
and Advanced Analytics. (Malhotra 2015)
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553547
28. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[28]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Cyber risk insurance (CRI) modeling… nascent … sparse research & data.
- VaR, Value-at-Risk, predominant model of choice for CRI modeling
- Model risk related to VaR key factor in the Global Financial Crisis
- Known limitations of VaR in modeling tail risks and systemic risks
- US Federal and OCC issued model risk guidance SR11-7/OCC 2011-12
- Basel Committee stopped reliance on VaR for risk modeling.
Investigation: if current reliance of CRI modeling on VaR entails model risk.
- Benchmark relative levels of tail risks and systemic risks for cyber risks
- Based upon analysis of statistical correlations and dependence, systemic risks
- Finding: Cyber risk entails exponentially higher tail risks and systemic risks
- Hence, VaR unfit as primary risk model for CRI modeling
- Coherent Risk Measures Beyond VaR: T-VaR/ES EVT Power Laws
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553547
29. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[29]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Cyber-Finance-Trust Framework for Cyber Risk Insurance Modeling
Cyber risk inherent in all cyber activities including cyber-finance,-economics.
Just like use of any model entails associated model risk,
Similarly use of cyber activities entails associated cyber risk.
Cyber risk is “risk affecting the confidentiality, availability, integrity,
authentication, non-repudiation, or accessibility of information.”
“Unlike other risks, cyber risk poses a uniquely different set of exposures
as it is intertwined with the medium and the message in the increasingly
global interconnected, distributed, and, networked world of digital
communications powered by universal use and reuse of enabling global
monocultures of information and communication technologies and standard
computing network protocols.”
To VaR or Not to VaR? Why VaR & Why ES or EVT?
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553547
30. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[30]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
“Cyberwarfare is not something theoretical or reserved for conflict in the
distant future, but happening continuously right now… We're doing it all of
the time. So is everybody else…”
- Ability of incapacitating a country’s power grids as early as 1994.
- Ability to disable complete national critical information infrastructure
banking, railroads, airlines, sewage, water and electric power since 1999.
Global financial systems and national financial infrastructures have been
explicitly specified as potential targets of Cyberwarfare by key representatives.
31. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[31]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
In our information based view, all networked information based risks
including market risks, credit risks, currency risks, interest rate risks, etc.,
are subject to cyber risks.
In as much as all these risks are represented in terms of digital information
which can be subject to information based manipulation or hacking, they
are in fact cyber risks.
Banking and Finance is the most information intensive industry given
that most of its products and services, processes, as well as channels of
distribution and consumption are all digital.
Given common and shared platforms, hardware, software, exchanges,
and networks across many of the players in the Finance industry, there is a
greater probability of correlated cyber risks.
Risk, Uncertainty, and Profit for the Cyber Era: Model Risk Management of Cyber
Insurance Models Using Quantitative Finance and Advanced Analytics. (Malhotra 2015)
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553547
32. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[32]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Cyber Finance (information networks based finance) – pretty much
most of post-WWW contemporary finance of this century – all products
(and services), processes, channels (of production, distribution, and
consumption) increasingly more or less information-based, digital, cyber, and
virtual.
Source of cyber risk and cyber loss is uncertain: In contrast to (traditional)
financial risk realm of the finance domain, it is most challenging to even
ascertain the source of cyberattack with certainty.
“However you read it, this sort of evidence is circumstantial at best. It's easy
to fake, and it's even easier to interpret it wrong.”
33. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[33]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Related examples include FIX (Financial Information eXchange) and
FAST (FIX Adapted for STreaming) protocols that form the backbone of
buy- and sell-side trading or SWIFT (Society for Worldwide Interbank
Financial Telecommunication) protocol that forms the backbone of
worldwide banking transactions and messaging.
Regulated & Controlled Risks… Application Layer L7: Accounting &
Auditing irregularities, Insider trading, Repo 105, LIBOR fixing, FOREX
fixing, Credit ratings manipulations, Wash sales (High Frequency Trading), …
Unregulated & Uncontrolled Risks… Network Layers L3-6: Same or
similar impacts on specific information but through cyber manipulations
and cyber attacks… at the Network Layer, Transport layer, related Security
Protocols…
Such cyber risk ‘losses’ remain substantially unaccounted & unreported.
- SEC Corp Fin ‘materiality’ criteria guidance for self-reporting by firms.
34. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[34]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Source: microsoft.com
Source: indigoo.com
MODELS
RISKS
Increasing Knightian
Uncertainty
Model Risk Management
Critical
35. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[35]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
“The Heartbleed Bug is a serious vulnerability in the popular OpenSSL cryptographic
software library. This weakness allows stealing the information protected, under normal
conditions, by the SSL/TLS encryption used to secure the Internet. This allows
attackers to eavesdrop communications, steal data directly from the services
and users and to impersonate services and users.”
“Half a million sites are vulnerable, including my own.”
“Basically, an attacker can grab 64K of memory from a server. The attack leaves no
trace, and can be done multiple times to grab a different random 64K of memory.
This means that anything in memory -- SSL private keys, user keys, anything -- is
vulnerable. And you have to assume that it is all compromised. All of it.”
“The real question is whether or not someone deliberately inserted this bug into
OpenSSL, and has had two years of unfettered access to everything.”
“"Catastrophic" is the right word. On the scale of 1 to 10, this is an 11.”
www.schneier.com
36. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[36]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Source: Retained Search Model Risk Management Job Spec for Managing Director/Executive
Director Role of a Top Wall Street Investment Bank, April 2014.
“[T]he approaches to mitigate operating risk associated with the use of models need to
evolve to reflect recent trends in the Finance Industry. In particular there are a number
of new areas where it is not possible for the "human eye" to necessarily detect
material flaws: in the case of models operating over very small time scales in high
frequency algorithmic trading, or for portfolio risk measurement models where outputs
lack interpretability due to high-dimensionality and complex interactions in
inputs, the periodic inspection of predicted versus realized outcomes is unlikely to be
an effective risk mitigate. These situations require a holistic validation framework of the
system focused on identifying and mitigating potential failures, taking into account the
models’ objectives, their implementation including the joint interaction of software
and hardware, their response to potential input shocks in real time and the fail-
safe mechanisms.” “As much as $600 million in assets changed hands in the 7
milliseconds before traders in Chicago could learn of the Fed's ‘no
taper’ decision made in Washington DC on Sep. 18, 2014.” - CNBC
37. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[37]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
MODELS
RISKS
FIX AND XML: FIXML (fixprotocol.org)
Source: Canadian Securities Exchange vendor
Source: b2bits.com
38. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[38]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
A Risk Management Framework for Penetration Testing of Global Banking & Finance
Networks VoIP Protocols, May 8, 2014.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2555098
“A vulnerability inside all current
Cisco IP phones allows hackers to
take complete control of the devices…
It’s relatively easy to penetrate any
corporate phone system, any
government phone system… All
current Cisco IP phones, including the
ones seen on desks in the White House
and aboard Air Force One, have a
vulnerability that allows hackers to
take complete control of the devices.”
Malhotra, Y. A Risk Management Framework for
Penetration Testing & Security of
Global Banking & Finance networks
Voice Over Internet Protocols (May 3, 2014), WWW:
Columbia University and Palindrome Technologies.
39. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[39]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Interview, Hong Kong Institute
of CPAs, A+, January 20, 2014. http://yogeshmalhotra.com/Future_of_Bitcoin.html
“Recently, such probabilistic, statistical, and numerical methods related concerns
are in globally popular press related to cybersecurity controls and compliance.
Earlier, similar probabilistic, statistical, and numerical methods related concerns were in
the global popular press in the context of the global financial crisis. Future questions
focused on the underlying assumptions and logic may focus on related implications for
compliance, controls, valuation, risk management, etc. Likewise, recent developments
about mathematical entropy measures shedding new light on apparently greater
vulnerability of prior encryption mechanisms may offer additional insights for
compliance and control experts. For instance, given related mathematical, statistical and
numerical frameworks, analysis may also focus on potential implications for pricing,
valuation and risk models. The important point is that many such fundamental
assumptions and logic underlying widely used probabilistic, statistical, and
numerical methods may not as readily meet the eye.”
40. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[40]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Bitcoin Protocol: Model of ‘Cryptographic Proof’ Based Global Crypto-Currency &
Electronic Payments System, December 04, 2013.
http://yogeshmalhotra.com/BitcoinProtocol.html
“Money is an interesting construct that continues to occupy the fancy of
many ranging from economists to quantum physicists... The future of money
becomes “entangled” with future of money laundering when focus is not
on privacy and anonymity alone, but also lack of traceability... Situated
somewhere along the trajectory between real money and quantum money,
virtual crypto-currencies based upon ‘cryptographic proof’ represent a
natural stage in the evolution of global finance... The future of money,
whatever form it may take – virtual or quantum, will quite likely be
"entangled" with the future evolution of ‘cryptographic proof of work.’”
41. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[41]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Bitcoin Protocol: Model of ‘Cryptographic Proof’ Based Global Crypto-Currency & Electronic
Payments System, December 04, 2013. http://yogeshmalhotra.com/BitcoinProtocol.html
SHA 256
ECDSA (ECDRBG)
42. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[42]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Cryptology Beyond Shannon's Information Theory: Preparing for When the ‘Enemy Knows
the System’ with Technical Focus on Number Field Sieve Cryptanalysis Algorithms for
Most Efficient Prime Factorization on Composites, May 3, 2013.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553544
Number Field Sieves: Most powerful family of factoring algorithms
1970: 20-digit becoming feasible
1977: RSA “40 quadrillion years” challenge by R
1980: 50-digit commonplace, 1984: 2251 – 1 (300 yr. ago…)
1990: 116-digit quadratic sieve QS… Pomerance
1994: 129-digit RSA challenge won… within 17 years!
1996: 130-digit NFS … Pollard, 15% time of QS
2003: 174-digit RSA-576 NFS number field sieve
2005: 193-digit RSA-640 NFS
2009: 232-digit RSA-768 NFS
309-digit RSA-1024 Major security implications! $100K.
2012: SNFS Factorization of Mersenne number, 21061 – 1
Size of composite
of prime factors
being factored.
Number Field Sieve (NFS)
Special Number Field Sieve (SNFS)
General Number Field Sieve (GNFS)
Quadratic Sieve (QS)
Rational Sieve (RS)
43. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[43]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Cryptology Beyond Shannon's Information Theory: Preparing for When the ‘Enemy Knows
the System’ with Technical Focus on Number Field Sieve Cryptanalysis Algorithms for
Most Efficient Prime Factorization on Composites, May 3, 2013.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553544
“First, based on available evidence, it is not improbable that the current
officially ‘recommended’ most widely used global standard of
encryption [1024-bit RSA] may have already been compromised. Second,
it would not really be a ‘surprise’ given that the infamous ‘40 quadrillion
years’ challenge for an earlier version of the standard was unraveled in mere 17
years. Third, given recent multi-billion dollar global Finance deals blown by
compromise of such technologies, it is increasingly critical to recognize the
exponentially increasing cybersecurity risk among other Financial Risks.”
– Number Field Sieve Cryptanalysis Algorithms for Most Efficient Prime Factorization on
Composites presentation, May 1, 2013, Presentation 15 miles from AFRL.
44. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[44]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Congruent Squares: Legendre’s Congruence: Prime Factors p & q
45. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[45]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
46. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[46]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
1. Polynomial Selection
Find f(x) irreducible over ℤ[x]
with root m modulo n, f(x) ϵ ℤ[x].
2. Finding Factor Bases
Choose size for factor bases and set up:
Rational Factor Base, RFB
Algebraic Factor Base, AFB
Quadratic Character Base, QCB
3. Sieving → Set S of relations (a, b)
Find pairs of integers (a, b) with properties:
gcd(a, b) = 1 a, b are relative primes
a + bm is smooth over RFB
bdeg(f)f(a/b) is smooth over AFB
Pairs (a, b) with above properties: relation.
47. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[47]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
4. Solving Linear Equations using Matrix
Filter sieving results: remove duplicates and relations containing a prime ideal
not present in other relations.
Put relations into relation-sets.
Construct very large sparse matrix over GF(2) 2 = pm .
Reduce the matrix resulting in some dependencies
Elements which lead to a square modulo n.
5. Calculating Square Roots in Number Fields
Rational square root, y: y2 = 𝑎,𝑏 ϵ 𝑆 (𝑎 − 𝑏𝑚)
Algebraic square root, x: x2 = 𝑎,𝑏 ϵ 𝑆 (𝑎 − 𝑏α)
where α = root of f(x)
p is found by gcd(n, x-y) and gcd(n, x+y).
48. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[48]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Cryptology Beyond Shannon's Information Theory: Preparing for When the ‘Enemy
Knows the System’ with Technical Focus on Number Field Sieve Cryptanalysis Algorithms
for Most Efficient Prime Factorization on Composites, May 3, 2013.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553544
Expert Systems for Knowledge Management: Crossing The Chasm Between Information
Processing and Sense Making. Journal of Expert Systems with Applications (Malhotra, 2001).
http://www.brint.org/expertsystems.pdf
Entropy increases with a larger repertoire of symbols.
Entropy increases when meanings detached from symbols.
Complex Systems & Cybernetics: Ashby's Law of Requisite Variety
49. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[49]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Quantum Computing, Quantum Cryptography, Shannon’s Entropy and Next Generation
Encryption & Decryption, November 2013. (Invited Presentation)
Information entropy of 27-char. language ~ 4.8 bits per char.
Information entropy of 5,000-char. language ~ 12.3 bits per char.
Entropy increases with a larger repertoire of symbols.
Entropy increases when meanings detached from symbols.
Quantum computer: qubits… can be 0, 1, or any superposition of
both. n-qubit system: superposition of up to 2n states
simultaneously. 2k dimensional vector (a, b, c, d, e, f, g, h)…
complex values: |a|2 + |b|2 + …+ |h|2 = 1,
|x|2 is probability amplitude of respective state. Phase between any
two states (complex-valued coefficients )… meaningful.
50. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[50]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Quantum Cryptography, Shor's algorithm, and Quantum Money
Integer Factorization of large primes and Discrete Logarithm problem.
Quantum computer efficiently find such factors using Shor's algorithm.
Decrypt many critical cryptographic systems in polynomial time:
RSA, secure Web pages, encrypted email, many other types of data.
“For a 1024-bit number, Shor's Algorithm requires on the order of 10243, about one
billion, operations. If each quantum operation took one second, our factorization would
last 34 years. If a quantum computer could run at the speed of today's electronic
computers (100 million instructions per second and up) then factorization of the 1024-
bit number would be a matter of seconds.”
IEEE
51. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[51]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
N-dimensional
Hilbert Space
+
OTHER BOOKS
52. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[52]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Knight’s Risk, Uncertainty, and Profit of 1921
- Emergence of the World Wide Web in early-1990s,
- Derman’s Model Risk Management, Goldman Sachs, mid-late-1990s,
- Backlash against quantitative models after Financial Crisis, 2008,
- Basel to Consider Risk Metrics other than VaR, Feb 2012,
- Post-Snowden Cyber era starting May 2013...
Information-based view of Financial risk modeling practices,
- Exponentially increasing Cyber era uncertainty,
- Cyber-Finance, the emerging Future of Finance?
- Risk modeling focus on metaphorical ‘tip’ of the iceberg,
- Significant risks not readily meet the human eye.
- Cyber-risk, the predominant risk… subsumes traditional risks.
CONCLUSION
53. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[53]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Exponentially increasing tail risks and systemic risks
- Characterize highly systemic, interdependent, correlated Cyber-risks
- Cyber risks in turn characterize financial risks.
Based on research of two-decades
- Starting around the emergence of the WWW (1993), Philosophy of IS
- How to ‘anticipate surprise’ when ‘prediction’ of risk is infeasible,
- Wall Street Chief Risk Officers, Top Investment Bank CxOs, Fed/OCC
- Explore advances in quantitative risk models, statistical methodologies,
and, computational statistical technologies
Facilitate dialog on the above issues of central concern
- Future of Finance
- Future of Risk.
CONCLUSION
54. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[54]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
SSRN Top-10 Papers: 20 Quantitative Finance-Risk Analytics Top-10 Rankings in recent 11 Weeks:
1. Beyond ‘Bayesian vs. VaR’Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds (Malhotra 2014).
2. Risk, Uncertainty, and Profit for the Cyber Era: Model Risk Management of Cyber Insurance Models
Using Quantitative Finance & Advanced Analytics (Malhotra 2015).
3. Markov Chain Monte Carlo Models, Gibbs Sampling & Metropolis Algorithm for High-Dimensionality
Complex Stochastic Problems (Malhotra 2014).
4. Extending Above Observations to High Frequency Trading: FIX, FAST (Beyond ‘Flash Boys’).
5. A Risk Management Framework for Penetration Testing of Global Banking & Finance Networks VoIP
Protocols (Malhotra 2014).
6. Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Interview, Hong Kong Institute of CPAs
(Malhotra 2014).
7. Bitcoin Protocol: Model of ‘Cryptographic Proof’Based Global Crypto-Currency & Electronic Payments
Systems (Malhotra 2013).
8. Cryptology Beyond Shannon's Information Theory: Number Field Sieve Cryptanalysis Algorithms for Most
Efficient Prime Factorization on Composites (Malhotra 2013).
9. Quantum Computing, Quantum Cryptography, Shannon’s Entropy and Next Generation Encryption &
Decryption (Malhotra 2013)… One more reference about Shannon’s Information Theory (Malhotra 2001).
55. Future of Finance Beyond Flash Boys
Risk Modeling for Managing Uncertainty
in an Increasingly Non-Deterministic Cyber World
www.FutureOfFinance.org
[55]
Copyright, Yogesh Malhotra, PhD, 2015www.yogeshmalhotra.com
Princeton Quant Trading Conference 2015 @ , April 04, 2015
Conference sponsors include:
Knight Reconsidered: Risk, Uncertainty, and Profit for the Cyber Era:
Future of Finance: Cyber-Finance?:
Uncertainty Modeling & Model Risk Management
Yogi
Yogesh Malhotra
PhD, MSQF, MSCS, MSNCS, MSAcc, MBAEco
BE, CEng, CISSP, CISA, CEH, CCP/CDP
www.yogeshmalhotra.com (646) 770-7993 dr.yogesh.malhotra@gmail.com
Global Risk Management Network, LLC
757 Warren Road, Cornell Business & Technology Park, Ithaca, NY 14852-4892
http://www.FutureOfFinance.org/Princeton.ppsx
Editor's Notes
Parallels between… Derman – Physics theory of Financial Markets…
Information theory of Global Financial Systems of which Financial Markets are an example… Design of fail-safe self-adaptive systems based on complexity theory… for dynamic, radically changing environments… characterized by what later became popular as black swans and extreme events.
Focus on Cyber goes to when the WWW started with the first WWW browser… while I was a PhD student… not planned but on my way from Hong Kong to Australia designing global financial systems for global banks… While pursuing my research… in terms of practice… in fact what were ad hoc experiments with WWW in my spare time… all of us can relate to spare time as graduate students and PhD students... Build up a Web site… then top-3 ranked search engine… and top-10 social networks… before anyone had heard of Google, LinkedIn or Facebook. After PhD I was a professor of quantitative methods doing research on risk modeling in research academia having nightmares about being left behind in being involved in first hand in the fast evolving world of financial exchanges and financial markets… speeding by at the speed of light… with the competition for microseconds and nanoseconds… yearning to get back to the applied world of practice… that my prior experiments with WWW had contributed to in advancing as I came to know from written accounts of visionary CEOs such as Microsoft Bill Gates to the top CIOs and commanders of Army, Navy, and Air Force. I happened to consult for big tech firms such as Intel, had clients and patrons such as Goldman Sachs, Google and IBM, and was invited to advise NSF, UN, and US and World Governments… but I yearned for full immersion in leading-edge applied research and practice... I would have the opportunity of doing so soon… leading quantitative finance and risk modeling practices of Wall Street investment banks managing $1 trillion such as JP Morgan… and advancing that research in collaboration with top research scientists including physicists, mathematicians, and computer scientists leading USA’s cybersecurity and information assurance practice.
While pursing research in quantitative risk modeling… became interested in physics based quantitative risk models few years before the global financial crisis… trying to understand to what extent can physics based models… about which I had studied as a mechanical engineer in prior life… help fathom the complexity and riskiness of the rapidly evolving sociotechnical world of WWW underlying the global financial systems such as financial markets and financial exchanges. Developed foundational risk management practices for large scale complex systems applied by worldwide national and regional governments and many of the top IT and Banking and Finance corporations worldwide. Trying to fathom how the financial engineering models relate to the rapidly evolving radical complexity and change in aftermath of the hyperconnectivity and hypervelocity of information in aftermath of the WWW... Observing recent focus of DARPA in controlling cyber risk with primary focus on Physics based automation… the jury is still out on whether the intrinsically sociotechnical world of cyberspace can be managed by Physics alone… based on Derman’s conclusion about financial markets.
* Cyber risk models -- To VaR or Not to VaR: Why Not to VaR & Why To ES: *
Our dialog was inspired by the topical discussion on Cyber risk models -- To VaR or Not to VaR. Based upon our shared understanding of systemic, highly correlated, and interdependent nature of risks characterizing Cyber risk models as well as sparse data on cyber risks, we probed the following specific topics.
In direct response to the question of To VaR or Not to VaR, we determined that VaR was not the appropriate model for the specific risks given the 'systemic' nature of the risks - specifically given that VaR does not satisfy the 'subadditivity' criterion of 'coherent risk measures.' This topic is further explained in my paper titled Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management: How to Manage Risk (After Risk Management Has Failed) for Hedge Funds accessible at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2538401. See, in particular, the discussion on page 17 on why VaR is not appropriate, and why ES (for instance, as an example of coherent risk measure) is more appropriate for modeling systemic risks. To know more about the 'coherent risk measures', see for instance the cited work of Artzner et al., 1999: there is more updated research in this stream on which I had advised the JPM ED and his reporting team of MDs and PMs, which I shall be pleased to share in further dialog if there is interest.
* If VaR, while ignoring systemic risks: *
In specific cases however, where you can rule out the stringent need for your risk models to satisfy the need for the 'subadditivity' criterion, i.e., cases in which 'diversity' of different Cyber risks (analogous to assets in a portfolio) tends to not significantly increase systemic risk in case of Cyber risks, you _may_ use specific variation of VaR and hence judiciously lessen the impact of not considering the 'subadditivity' criterion of 'coherent risk measures.' Then the question may become which VaR variation is the most appropriate model, Historical Simulation Based VaR (p. 12-13) that is reliant upon historical correlations; Parametric VaR (p. 13-14) reliant upon the statistical linearity and normality assumptions; Monte Carlo (MC) Simulation based VaR that can use any statistical distribution (including normal distribution) but is reliant upon your several key assumptions about the specific statistical distributions that you may choose to model as well as extensive computing power (p. 14-15) [How to address the issue of computational complexity to find solvable models within manageable processing times, please see the paper on Bayesian Inference and related Markov Chain Monte Carlo models and related algorithms titled Markov Chain Monte Carlo (MCMC) Models, Gibbs Sampling & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems accessible at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553537]; or Modified VaR (p. 15-16).
Based upon your interest in stochastic models based on MC (and MCMC), the following references may be relevant:
Glasserman, P. (2004) Monte Carlo Methods in Financial Engineering, Springer; and,
Michael Steele, J. (2010) Stochastic Calculus and Financial Applications (Stochastic Modelling and Applied Probability), Springer.
* Beyond VaR and ES to Extreme Events and Extreme Non-Normal Events: *
We also briefly discussed the issues of more complex statistical distributions, such as EVT and related Catastrophic Distributions and Power Law distributions that may be relevant when the cyber risk phenomena being modeled may include more significant probability or likelihood of extreme events and black swans such as in specific long tails and fat tails, a topic we briefly discussed.
* Potential Data Source(s) for Cyber Risk Models: Example: *
In response to your team's question about the data sources that may be potentially relevant to the analysis of cyber risk loss analysis, listed below is one example: SAS® OpRisk Global Data, reportedly the world’s largest repository on publicly reported operational losses in excess of $100,000, with each loss categorized as per Basel event and effect classification standard with 50 fields of descriptive information for each loss event.
* Firewalls, IDS/IPS - Following your brief discussion on firewalls and IDS/IPS, particularly at Transport & Network Layer level analysis of TCP/IP / OSI network protocol stacks: Lot of advances are occurring in the area of Networks and Telecommunication Engineering with ever evolving vulnerability and threat vector evolution relevant to ERM and ORM, see for instance the following and related technical updated research and practice articles:
http://csrc.nist.gov/publications/nistpubs/800-94/SP800-94.pdf
http://csrc.nist.gov/publications/nistpubs/800-41-Rev1/sp800-41-rev1.pdf
Data in Rest, Data in Motion
Logic at Rest, Logic in Motion
Assumptions at Rest, Assumptions in Motion
Flash Boys:
FIX & by extension FAST Protocol:
https://tools.ietf.org/html/rfc6274#section-4.1
UDP: It must be noted that an attacker could intentionally exploit collisions of IP Identification numbers to perform a DoS attack, by sending forged fragments that would cause the reassembly process to result in
a corrupt datagram that either would be dropped by the transport protocol or would incorrectly be handed to the corresponding application. This issue is discussed in detail in Section 4.1 ("Fragment
Reassembly"). During the last few decades, IP fragmentation and reassembly has been exploited in a number of ways, to perform actions such as evading NIDSs, bypassing firewall rules, and performing DoS attacks.
http://www.howtogeek.com/190014/htg-explains-what-is-the-difference-between-tcp-and-udp/
Ditching TCP’s error correction helps speed up the game connection and reduce latency.
Only one drill in the world to drill across Susqu(ku)ehanna river for laying the fiber… costs millions to rent… at that time in Brazil…
Across all continents, not only US model of exchanges and markets has been exported, but also the high frequency trading and algos…
Current regulation in the US… bounded by national jurisdictions…
In foreseeable future one can see all these HFT algos talking across fiber and other hyperspeed information infrastructure across national jurisdictions… across the countries and continents…. With advent of new developments such as Bayesian Modeling, MCMC, and Quantum Computing… rapid computation and complex trading of baskets of various assets will be done across the world at lightning speed… all of it taking advantage of fragmented regulations across diverse jurisdictions… as we see from BTC…
Also, given networked global financial systems of worldwide exchanges and markets, we can look forward to exposure across the main body of the iceberg across the whole world…
Flash Boys & FIX Protocol Security Measures:
What do I get for my $14 million in assorted fees and expenses… Two glass fibers, one in each direction.
What happens if the line’s cut by a backhoe… We get it up and running in 8 hours.
Where is the backup if your line goes down, Sorry there’s none (Microwave towers).
When can you supply us with five years of audited statements (Um, in five years.)
00000 Integer Factorization GNFS thesis.pdf
BOOK_Yan_An Introduction to Formal Languages and Machine Computation.pdf
X2 - 1 is reducible over rationals , X2 + 1 is irreducible.
In mathematics, a polynomial is said to be irreducible if it cannot be factored into the product of two or more non-trivial polynomials whose coefficients are of a specified type. Thus in the common context of polynomials with rational coefficients, a polynomial is irreducible if it cannot be expressed as the product of two or more such polynomials, each of them having a lower degree than the original one. For example, while is reducible over the rationals, is not.
MathNumberFieldNotes.txt
X2 - 1 is reducible over rationals , X2 + 1 is irreducible.
A pair (a, b) with these properties is called a relation. The purpose of
the sieving stage is to collect as many relations as possible (at least
one larger than the elements in all of the bases combined). The
sieving step results in a set S of relations.
Definition 2.27 Galois Field
The finite field Fsub-n with n elements, where n = p^m for some prime p, is written
GF(n).
IT IS NOT THE FORMULA (MODEL/THEORY), NOR THE RIGHT ANSWER TO THE FORMULA FOR ANY SPECIFIC PRE-DETERMINED QUESTION…
BUT HOW THE FORMULA HELPS US THINK BETTER ABOUT THE REAL WORLD THAT WE ARE TRYING TO UNDERSTAND AND IMPROVE...
AND IN THAT PROCESS IMPROVE THE WORLD AS WELL AS THE FORMULA (MODEL/THEORY).
Advancing Beyond Binary Computer Science and Binary Statistics.
Classical digital computer: bits … must be either 0 or 1
k-bit register: 2k states: say, 3-bit: 8 states: 000, 001, 010, 011, 100, 101, 110, 111
Deterministic computer: is in exactly one of the 2k states.
Probabilistic computer: any one of 2k states: A=P(000), B=P(001)…, H=P(111)
A + B +…+ H = 1
- n-qubit classic system: in only one of the 2n states at any one time.
e.g. 500 qubits too large to simulate with classical computer
Will require 2500 complex values (2501 bits) to store.
Quantum computer similar to NTM and PTM.
Ability to be in more than one state simultaneously.
Solve certain problems much more quickly
E.g. integer factorization using Shor's algorithm.
Gardner_PublicKeyCrypto8-1977.pdf
The three men responsible for this remarkable breakthrough are
Whitfield Diffie and Martin E. Hellman, both electrical engineers at
Stanford University, and Ralph Merkle, then an undergraduate at the
University of California, Berkeley. Their work was partly supported by
the National Science Foundation in 1975 and was reported by Diffie and
Hellman in their 1976 paper "New Directions in Cryptography". In it
Diffie and Hellman show how to create unbreakable ciphers that do not
require advance sending of a key or even concealment of the method of
encoding. The ciphers can be efficiently encoded and decoded, they can
be used over and over again and there is a bonus: The system also provides an "electronic signature" that, unlike a written signature, cannot
be forged. If Z receives a "signed" message from A, the signature
proves to Z that A actually sent the message. Moreover, A's signature
cannot be forged by an eavesdropper or even by Z himself!
Parallels between… Derman – Physics theory of Financial Markets…
Information theory of Global Financial Systems of which Financial Markets are an example… Design of fail-safe self-adaptive systems based on complexity theory… for dynamic, radically changing environments… characterized by what later became popular as black swans and extreme events.
Focus on Cyber goes to when the WWW started with the first WWW browser… while I was a PhD student… not planned but on my way from Hong Kong to Australia designing global financial systems for global banks… While pursuing my research… in terms of practice… in fact what were ad hoc experiments with WWW in my spare time… all of us can relate to spare time as graduate students and PhD students... Build up a Web site… then top-3 ranked search engine… and top-10 social networks… before anyone had heard of Google, LinkedIn or Facebook. After PhD I was a professor of quantitative methods doing research on risk modeling in research academia having nightmares about being left behind in being involved in first hand in the fast evolving world of financial exchanges and financial markets… speeding by at the speed of light… with the competition for microseconds and nanoseconds… yearning to get back to the applied world of practice… that my prior experiments with WWW had contributed to in advancing as I came to know from written accounts of visionary CEOs such as Microsoft Bill Gates to the top CIOs and commanders of Army, Navy, and Air Force. I happened to consult for big tech firms such as Intel, had clients and patrons such as Goldman Sachs, Google and IBM, and was invited to advise NSF, UN, and US and World Governments… but I yearned for full immersion in leading-edge applied research and practice... I would have the opportunity of doing so soon… leading quantitative finance and risk modeling practices of Wall Street investment banks managing $1 trillion such as JP Morgan… and advancing that research in collaboration with top research scientists including physicists, mathematicians, and computer scientists leading USA’s cybersecurity and information assurance practice.
While pursing research in quantitative risk modeling… became interested in physics based quantitative risk models few years before the global financial crisis… trying to understand to what extent can physics based models… about which I had studied as a mechanical engineer in prior life… help fathom the complexity and riskiness of the rapidly evolving sociotechnical world of WWW underlying the global financial systems such as financial markets and financial exchanges. Developed foundational risk management practices for large scale complex systems applied by worldwide national and regional governments and many of the top IT and Banking and Finance corporations worldwide. Trying to fathom how the financial engineering models relate to the rapidly evolving radical complexity and change in aftermath of the hyperconnectivity and hypervelocity of information in aftermath of the WWW... Observing recent focus of DARPA in controlling cyber risk with primary focus on Physics based automation… the jury is still out on whether the intrinsically sociotechnical world of cyberspace can be managed by Physics alone… based on Derman’s conclusion about financial markets.