Quant Insights Conference
27th - 28th October 2021
08:30 - 14:30 EDT / 13:30 - 19:30 BST
Globally Live-Online
8th Conference
Brought to you by
CQF Institute, Fitch Learning and Wilmott

2 Days

14+ Talks

Free Tickets

30 Days Video on Demand

About the Conference

The Quant Insights Conference is back this October for its 8th event. Join talks from Dr. Paul Wilmott, Dr. Robert Litterman, Dr. Katia Babbar, Professor Alexander Lipton, Dr. Jesper Andreasen, and many more to discover the latest quant finance innovations in machine learning, volatility, risk, quantum computing, and more.

Tickets are free for all CQF Institute members and include: access to all talks and panels, breakout and networking activities, plus 30 days of video on demand. Become a CQF Institute member for free to claim your complimentary ticket today.

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Conference Schedule

Day 1 - 27th October 2021

07.30 - 08.30 EDT
Virtual conference doors open - Familiarize yourself with the Hopin platform
08.30 - 08.35 EDT
Welcome Remarks
Dr. Randeep Gug, Managing Director, CQF Institute
08.35 - 09.10 EDT
Joint Calibration: The Case of Bank Regulatory Capital Securities
Dr. Philippe Henrotte, Co-Founder and Partner, ITO33
09.10 - 09.45 EDT
Decentralized Finance, Central Bank Digital Coins, Automated Market Makers and Forex of the Future
Professor Alexander Lipton, CIO, Sila and Professor, The Hebrew University of Jerusalem
09.45 - 10.20 EDT
Cross-Currency Options and the correlated SABR Model
Dr. Katia Babbar, Lecturer, University of Oxford
10.20 - 10.40 EDT
Break and Networking
Breakout Session: 'How to Identify and Mitigate Overfitting' with SigTech
10.40 - 11.15 EDT
American Option Pricing in a Tick - Calibration in a Click
Dr. Jesper Andreasen, Head of Quantitative Research, Saxo Bank
11.15 - 11.50 EDT
Anticipating the Anticipations of Others
Grant Fuller, CEO, Irithmics
11.50 - 12.20 EDT
Break and Networking
Breakout Session: 'AI Powered Traders: Ready or Not?' with NVIDIA
CQF Information Session
12.20 - 13.20 EDT
Panel Discussion: The Origins of Financial Market Volatility
Panel Chair: Michael B. Miller, CEO, Northstar Risk Corp
Professor J. Doyne Farmer, Baillie Gifford Professor of Mathematics, University of Oxford
Dr. Richard Bookstaber, Founder, Fabric RQ
13.20 - 13.55 EDT
Optimal Portfolio Construction and Risk Premia in Options Markets
Dr. Misha Fomytskyi, Co-Founder, Vola Dynamics LLC
13.55 - 14.25 EDT
Fat Tailed Kelly
Steve Schulist, Quantitative Analyst, PIMCO
14.30 - 14.35 EDT
Closing Remarks
Dr. Randeep Gug, Managing Director, CQF Institute
14.35 - 15.35 EDT
End of Day Networking

Day 2 - 28th October 2021

08.00 - 09.05 EDT
Virtual conference doors open - Familiarize yourself with the Hopin platform
09.05 - 09.10 EDT
Welcome Remarks
Dr. Randeep Gug, Managing Director, CQF Institute
09.10 - 09.45 EDT
Managing Climate Risk
Dr. Robert Litterman, Chairman of the Risk Committee, Kepos Capital LP
09.45 - 10.20 EDT
Using Machine Learning Algorithms to Estimate the Functional Form of Optimal Trading Strategies
Graham Giller, Chief Executive Officer, Giller Investments
10.20 - 10.40 EDT
Break and Networking
Breakout Session: 'Deploying an AI-based XVA Platform into Production' with Riskfuel
Breakout Session: 'Robust Options Valuation and Risk Management Workflows with Vola Dynamics Analytics' with Vola Dynamics
10.40 - 11.15 EDT
Alternatives to Deep Neural Networks for Function Approximations in Finance
Dr. Alexandre Antonov, Chief Analyst, Danske Bank
11.15 - 11.50 EDT
Data Driven Market Generators and their Model Governance
Dr. Blanka Horvath, Lecturer in Financial Mathematics, King's College London
11.50 - 12.20 EDT
Break and Networking
Breakout Session: 'Practical Implications of the Anticipations of Others' with Irithmics
Breakout Session: 'ITO33 on Convertible Bonds and Banking Cocos' with ITO 33
12.20 - 13.20 EDT
Panel Discussion: How Can We Be More Ambitious with AI in Finance?
Panel Chair: Tony Guida, Co-Head of Systematic Macro, RAM AI
Dr. Alexandre Antonov, Chief Analyst, Danske Bank
Dr. Alexei Kondratyev, Quantitative Research and Development Lead, Abu Dhabi Investment Authority (ADIA)
Dr. Blanka Horvath, Lecturer in Financial Mathematics, King's College London
13.20 - 13.55 EDT
On Accuracy Guarantees for Machine Learning in Derivatives Pricing
Ryan Ferguson, Founder and CEO, Riskfuel
13.55 - 14.30 EDT
Jensen
Dr. Paul Wilmott, President, CQF Institute
14.30 - 14.35 EDT
Closing Remarks
Dr. Randeep Gug, Managing Director, CQF Institute
14.35 - 15.35 EDT
End of Day Networking

Conference Speakers

Confirmed speakers and panelists.

Paul Wilmott
Jensen
Jensen

Probably the Best Inequality in the World

Dr. Paul Wilmott, President, CQF Institute
Dr. Paul Wilmott, President, CQF Institute

Paul is the founder of the Certificate in Quantitative Finance and Wilmott.com and he is internationally renowned as a leading expert on quantitative finance. His research work is extensive, with more than 100 articles in leading mathematical and finance journals, as well as several internationally acclaimed books on mathematical modeling and derivatives, including the best-selling Paul Wilmott On Quantitative Finance, published by John Wiley & Sons.

Robert Litterman
Managing Climate Risk
Managing Climate Risk

Economists often distinguish systemic risk (events that can impact the aggregate economy) from specific risks faced by individuals, organizations, or even cities or regions. Very different approaches, and time horizons, are required to address systemic risk.

Dr. Robert Litterman, Chairman of the Risk Committee, Kepos Capital LP
Dr. Robert Litterman, Chairman of the Risk Committee, Kepos Capital LP

Robert Litterman is the Chairman of the Risk Committee at Kepos Capital LP. Prior to joining Kepos Capital in 2010, Litterman enjoyed a 23-year career at Goldman, Sachs & Co. Bob was named a partner of Goldman Sachs in 1994 and became head of the firm-wide risk function. He is the co-developer of the Black-Litterman Global Asset Allocation Model. Dr. Litterman also chaired the CFTC Climate-Related Market Risk Subcommittee, which published its report, “Managing Climate Risk in the U.S. Financial System,” in September 2020. Dr. Litterman earned a Ph.D. in Economics from the University of Minnesota and a B.S. in Human Biology from Stanford University.

Alexander Lipton
Decentralized Finance, Central Bank Digital Coins, Automated Market Makers and Forex of the Future
Decentralized Finance, Central Bank Digital Coins, Automated Market Makers and Forex of the Future

In this talk, we discuss some of the most recent developments in the cryptocurrency ecosystem. Specifically, we review stable coins, their classification, potential applications, and related topics. We also address the emerging field of DeFi (Decentralized Finance), including Automated Market Makers (AMM), yield farmers, and other peculiar concepts. We explain mathematics, economics, and technology behind these developments and elaborate on their pros and cons

Professor Alexander Lipton, CIO, Sila and Professor, The Hebrew University of Jerusalem
Professor Alexander Lipton, CIO, Sila and Professor, The Hebrew University of Jerusalem

Alexander Lipton is Co-Founder and Chief Information Officer of Sila, Partner at Numeraire, Visiting Professor and Dean's Fellow at the Hebrew University of Jerusalem, and Connection Science Fellow at MIT. Alex is a board member of Sila and an advisory board member of several fintech companies worldwide. In 2006–2016, Alex was Co-Head of the Global Quantitative Group and Quantitative Solutions Executive at Bank of America. Earlier, he was a senior manager at Citadel, Credit Suisse, Deutsche Bank, and Bankers Trust. At the same time, Alex held visiting professorships at EPFL, NYU, Oxford University, Imperial College, and the University of Illinois. Before becoming a banker, Alex was a Full Professor of Mathematics at the University of Illinois and a Consultant at Los Alamos National Laboratory. In 2000 Alex was awarded the Inaugural Quant of the Year Award and in 2021 the Buy-side Quant of the Year Award by Risk Magazine. Alex authored/edited eleven books and more than a hundred scientific papers. Alex is an Associate Editor of several journals, including Finance and Stochastics, Journal of FinTech, International Journal of Theoretical and Applied Finance, and Quantitative Finance. He is a frequent keynote speaker at Quantitative Finance and FinTech conferences and forums worldwide.

Alexandre Antonov
Alternatives to Deep Neural Networks for Function Approximations in Finance
Panel Discussion: How Can We Be More Ambitious with AI in Finance?
Alternatives to Deep Neural Networks for Function Approximations in Finance

We propose neo-classical alternatives to NNs for financial applications. Financial applications are often characterized by a limited amount of data to fit, and require explainability (properties of the approximator should be understandable from parameters) and predictability (no local minimum uncertainty). Deep NNs, as a rule, do not satisfy one or more of these requirements. Our solution combines a linear regression against a well-defined set of basis functions derived from the spectral information about the approximated function with a low-dimensional solver. The solver easily extends to higher dimensions and has a controlled guess and bounds. Importantly, our regression basis is efficient for high-dimensional problems.

Panel Discussion: How Can We Be More Ambitious with AI in Finance?

Machine Learning, a subset of Artificial Intelligence, has gained increasing adoption as an important tool in finance over the last decade. The question arises, however, whether a narrow categorization of ML as a ‘tool’ means that quants are ignoring the potential of Artificial Intelligence as a paradigm. Rather than viewing AI as a means to perpetuate finance in its present form, how might operating in the AI paradigm change finance itself? With the promise of the Quantum era is it too ambitious for finance researchers to focus on modeling an “investment consciousness”?

Dr. Alexandre Antonov, Chief Analyst, Danske Bank
Dr. Alexandre Antonov, Chief Analyst, Danske Bank

Alexandre Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997. His activity is concentrated on modeling and numerical methods for interest rates, cross currency, hybrid, credit, XVA, as well as Machine Learning for finance. Alexandre is a published author for multiple publications in mathematical finance and a frequent speaker at financial conferences. He received a Quant of Year Award from Risk magazine in 2016.

Alexei Kondratyev
Panel Discussion: How Can We Be More Ambitious with AI in Finance?
Panel Discussion: How Can We Be More Ambitious with AI in Finance?

Machine Learning, a subset of Artificial Intelligence, has gained increasing adoption as an important tool in finance over the last decade. The question arises, however, whether a narrow categorization of ML as a ‘tool’ means that quants are ignoring the potential of Artificial Intelligence as a paradigm. Rather than viewing AI as a means to perpetuate finance in its present form, how might operating in the AI paradigm change finance itself? With the promise of the Quantum era is it too ambitious for finance researchers to focus on modeling an “investment consciousness”?

Dr. Alexei Kondratyev, Quantitative Research and Development Lead, Abu Dhabi Investment Authority (ADIA)
Dr. Alexei Kondratyev, Quantitative Research and Development Lead, Abu Dhabi Investment Authority (ADIA)

Alexei Kondratyev is Quantitative Research and Development Lead at Abu Dhabi Investment Authority (ADIA). Prior to joining ADIA in July 2021, he held quantitative research and data analytics positions at Standard Chartered, Barclays Capital and Dresdner Bank. Alexei holds MSc in Theoretical Physics from Taras Shevchenko National University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine. He was the recipient of 2019 Risk magazine Quant of the Year award.

Blanka Horvath
Data Driven Market Generators and their Model Governance
Panel Discussion: How Can We Be More Ambitious with AI in Finance?
Data Driven Market Generators and their Model Governance

Techniques that address sequential data have been a central theme in machine learning research in the past years. More recently, such considerations have entered the field of finance-related ML applications in several areas where we face inherently path dependent problems: from (deep) pricing and hedging (of path-dependent options) to generative modelling of synthetic market data, which we refer to as market generation. We revisit Deep Hedging from the perspective of the role of the data streams used for training and highlight how this perspective motivates the use of highly accurate generative models for synthetic data generation. From this, we draw conclusions regarding the implications for risk management and model governance of these applications, in contrast torisk-management in classical quantitative finance approaches. Indeed, financial ML applications and their risk-management heavily rely on a solid means of measuring and efficiently computing (smilarity-)metrics between datasets consisting of sample paths of stochastic processes. Stochastic processes are at their core random variables with values on path space. However, while the distance between two (finite dimensional) distributions was historically well understood, the extension of this notion to the level of stochastic processes remained a challenge until recently. We discuss the effect of different choices of such metrics while revisiting some topics that are central to ML-augmented quantitative finance applications (such as the synthetic generation and the evaluation of similarity of data streams) from a regulatory (and model governance) perpective. Finally, we discuss the effect of considering refined metrics which respect and preserve the information structure (the filtration) of the marketand the implications and relevance of such metrics on financial results.

Panel Discussion: How Can We Be More Ambitious with AI in Finance?

Machine Learning, a subset of Artificial Intelligence, has gained increasing adoption as an important tool in finance over the last decade. The question arises, however, whether a narrow categorization of ML as a ‘tool’ means that quants are ignoring the potential of Artificial Intelligence as a paradigm. Rather than viewing AI as a means to perpetuate finance in its present form, how might operating in the AI paradigm change finance itself? With the promise of the Quantum era is it too ambitious for finance researchers to focus on modeling an “investment consciousness”?

Dr. Blanka Horvath, Lecturer in Financial Mathematics, King's College London
Dr. Blanka Horvath, Lecturer in Financial Mathematics, King's College London

Blanka Horvath is a Financial Mathematician whose latest research evolves around faithful models of financial markets with a special focus on a new generation of stochastic models called Rough Volatility and using a number of deep learning methods holds for their modelling and pricing and hedging. She holds a position as Assistant Professor at the Technical University of Munich and is a member of the Munich Data Science Institute as well as The Alan Turing Institute in London. As a lecturer in Financial Mathematics at Kings College London, she was awarded the Rising Star Award of Risk Magazine in 2020. Prior to her position at King's College, Blanka held positions at Imperial College London and ETH Zurich and has ongoing collaborations with the financial industry. She graduated from the University of Bonn and holds a PhD in Financial Mathematics From ETH Zurich.

Graham Giller
Using Machine Learning Algorithms to Estimate the Functional Form of Optimal Trading Strategies
Using Machine Learning Algorithms to Estimate the Functional Form of Optimal Trading Strategies

A framework for the solution of stochastic programming problems relevant to trading with an alpha is given. The solution is expressed in terms of a 'holding function' which represents the optimal policy for a trader to follow at any given decision point based on their alpha and their current position. The use of Monte Carlo simulation and machine learning methods to investigate the optimal form of the holding functions is developed as a concept with several simple examples presented.

Graham Giller, Chief Executive Officer, Giller Investments
Graham Giller, Chief Executive Officer, Giller Investments

Graham Giller is one of Wall St.’s original data scientists, having started his career as a researcher and PM as an early member of the PDT group at Morgan Stanley. There he ran the Futures Group while also working on the theoretical aspects of trading strategy. He has managed his own, friends and family, investment fund for over a decade as well as having the roles of Chief Data Scientist, New Product Development, and Head of Data Science Research at JP Morgan and Head of Primary Research at Deutsche Bank as well as working at Bloomberg LP in Data Science and Quant Research leadership roles. He is the author of “Adventures in Financial Data Science,” in 2020, and the upcoming book “Essays on Trading Strategy,” to be published in World Scientific’s series on Finance.

Grant Fuller
Anticipating the Anticipations of Others
Anticipating the Anticipations of Others

The anticipations of investors influences how their views and expectations develop which, in turn, shape how they choose to allocate portfolio capital and risk, collectively impacting price formation, volatility and liquidity. In this talk, I discuss how epidemiology and AI expose common features of these collective choice dynamics, and how these are used by portfolio managers and activists to anticipate the anticipations of others.

Grant Fuller, CEO, Irithmics
Grant Fuller, CEO, Irithmics

Grant co-founded Irithmics, an artificial intelligence research and technology firm, in 2012 as part of an EU backed academic research call in public health and epidemiology. The technology's capability to analyse and anticipate complex behaviours, relationships and dynamics has subsequently been successfully applied to financial markets. Previously part of Ernst & Young's risk advisory practice, before which Grant led Bloomberg’s successful hedge fund trading and analytics (AIM) business in European and Asian markets. Prior to Bloomberg, Grant was part of RiskMetrics, establishing their European fund and asset management analytics and consulting capabilities. He holds a BSc in Chemistry from the University of St Andrews where he remained to undertake a PhD applying neural networks in carbohydrate chemistry, after which he joined academic research at Cambridge University, and began applying neural networks to public health and epidemiology.

J. Doyne Farmer
Panel Discussion: The Origins of Financial Market Volatility
Panel Discussion: The Origins of Financial Market Volatility

Volatility is at the core of option pricing. Unexpected volatility has the potential to destroy financial institutions and bring the global economy to its knees. But where does volatility come from? What drives extreme changes in market volatility? Most of us, as quants, think of volatility only in mathematical terms. If we want to do a better job at pricing securities and managing risk, we need to go deeper, we need to understand the origins of financial market volatility.

Professor J. Doyne Farmer, Baillie Gifford Professor of Mathematics, University of Oxford
Professor J. Doyne Farmer, Baillie Gifford Professor of Mathematics, University of Oxford

J. Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor in the Mathematical Institute at the University of Oxford, and an External Professor at the Santa Fe Institute. His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to UBS in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology.

Jesper Andreason
American Option Pricing in a Tick - Calibration in a Click
American Option Pricing in a Tick - Calibration in a Click

Andersen, Lake and Offengenden (2016) present a staggeringly fast and accurate method for the pricing of American options under constant parameter Black-Scholes assumptions. In this talk, we discuss this method, a few extensions including negative interest rates and prices, and its practical implementation and application to the estimation of implied dividends and volatility smiles from American option prices. Issues around absence of arbitrage and asynchronous data is also discussed.

Dr. Jesper Andreasen, Head of Quantitative Research, Saxo Bank
Dr. Jesper Andreasen, Head of Quantitative Research, Saxo Bank

Dr. Jesper Andreasen is head of the quantitative research department at Saxo Bank. His career spans almost 25 years and he has previously headed quant teams at Danske Bank, Bank of America, Nordea and General Re Financial Products. Jesper co-received Risk Magazine’s quant of the year award in 2001 and 2012 and their in-house risk system of the year in 2015. He is an honorary professor of mathematical finance at the University of Copenhagen and he holds a phd in the same subject from Aarhus University.

Katia Babbar
Cross-Currency Options and the correlated SABR Model
Cross-Currency Options and the correlated SABR Model

Assuming EURUSD and USDJPY each follow a SABR process, under some mild assumption on correlations, a EURJPY consistent cross-smile can be inferred. Such models require a 4-factor Monte Carlo simulation and are too slow for calibration. Here a Neural Network is trained on a set of data generated by Conditional Monte Carlo in order to speed up Calibration of a range of Cross-Smiles. This allows to more practically explore the dynamics implied by the model and to contrast such dynamics against cross-smiles observed in the market.

Dr. Katia Babbar, Lecturer, University of Oxford
Dr. Katia Babbar, Lecturer, University of Oxford

Katia has held a number of diverse roles in her 20 years career within FX. She was Managing Director for FX Algo Trading and Head of FX Options Quant Research at Lloyds, having previously worked at Citi and UBS. Katia currently lectures at the University of Oxford, is an Advisor to Morpho.Best, a Decentralised Finance Protocol and a Consultant for a crypto derivatives exchange. Katia holds a BSc in Mathematics from UCL and a PhD from Imperial College.

Michael Miller
Panel Discussion: The Origins of Financial Market Volatility
Panel Discussion: The Origins of Financial Market Volatility

Volatility is at the core of option pricing. Unexpected volatility has the potential to destroy financial institutions and bring the global economy to its knees. But where does volatility come from? What drives extreme changes in market volatility? Most of us, as quants, think of volatility only in mathematical terms. If we want to do a better job at pricing securities and managing risk, we need to go deeper, we need to understand the origins of financial market volatility.

Michael B. Miller, CEO, Northstar Risk Corp
Michael B. Miller, CEO, Northstar Risk Corp

Michael B. Miller is the CEO of Northstar Risk Corp., a risk management software and consulting firm based in New York City. Before that he was the Chief Risk Officer for Tremblant Capital, and before that the Head of Quantitative Risk Management at Fortress Investment Group. Mr. Miller is the author of 'Quantitative Financial Risk Management, Mathematics and Statistics for Financial Risk Management', and, along with Emanuel Derman, 'The Volatility Smile'. He is also an adjunct professor at Columbia University and the co-chair of GARP’s Research Fellowship Committee. Before starting his career in finance, Mr. Miller studied economics at the American University of Paris and the University of Oxford.

Misha Fomytskyi
Optimal Portfolio Construction and Risk Premia in Options Markets
Optimal Portfolio Construction and Risk Premia in Options Markets

We will discuss how to create optimal options portfolios given forecasts in different risk factors and the relationship between equity, volatility, and skew risk premia. In addition to theoretical results, we will show practical examples using Vola analytics.

Dr. Misha Fomytskyi, Co-Founder, Vola Dynamics LLC
Dr. Misha Fomytskyi, Co-Founder, Vola Dynamics LLC

Dr. Misha Fomytskyi is an expert in derivatives trading, risk management, and volatility modeling. Prior to co-founding Vola Dynamics LLC, he spent 12 years working in the derivatives space in trading and quant roles, including head of the options trading team at Getco LLC, as portfolio manager at JD Capital Management, and the founder and CEO of Mivol LLC. Having a deep understanding of trading, modeling, and technology allowed him to be a driving force in adopting quantitative techniques in valuation, risk management and trading analysis in these businesses. He received his Ph.D. in physics from The University of Texas at Austin in 2004.

Philippe Henrotte
Joint Calibration: The Case of Bank Regulatory Capital Securities
Joint Calibration: The Case of Bank Regulatory Capital Securities

Jointly calibrating all securities related to the same issuer with a parsimonious model should be a prerequisite for any risk management exercise, yet it is seldom achieved or even discussed. We use the example of the many regulatory securities issued by a bank (AT1 and Tier 2 bonds), together with the various CDS term structures written on the bank, to derive a robust measure of implied bail-in risk.

Dr. Philippe Henrotte, Co-Founder and Partner, ITO33
Dr. Philippe Henrotte, Co-Founder and Partner, ITO33

Philippe Henrotte is one of the founding partners of ITO33, a company which designs sophisticated derivatives pricing software for financial institutions. Philippe Henrotte is an Affiliate Professor at the Finance Department of HEC Paris. He holds a PhD in Finance from the Graduate School of Business, Stanford University. His research interests focus on risk management and the hedging and pricing of derivatives in incomplete markets. Philippe Henrotte is a director of Equinox Russian Opportunities Fund Limited, a hedge fund targeting the Russian capital markets.

Dr. Randeep Gug

Dr. Randeep Gug, CQF Institute, Managing Director
Dr. Randeep Gug, CQF Institute, Managing Director

Dr. Randeep Gug is the Managing Director of the CQF Institute and a lecturer on the Certificate in Quantitative Finance (CQF). Prior to joining Fitch Learning, Randeep worked in a variety of roles. He spent five years working in the Equities division at Salomon Smith Barney and later traded futures and options on the Indian National Stock Exchange (NSE). More recently he has spent time teaching mathematics at all levels. He is a qualified teacher, holds a 1st class honours degree and a PhD for research in semiconductor physics. He is a CQF Alumnus, achieving a distinction on the programme and his current interests are based around improving and promoting the teaching and learning of Quant Finance.

Richard Bookstaber
Panel Discussion: The Origins of Financial Market Volatility
Panel Discussion: The Origins of Financial Market Volatility

Volatility is at the core of option pricing. Unexpected volatility has the potential to destroy financial institutions and bring the global economy to its knees. But where does volatility come from? What drives extreme changes in market volatility? Most of us, as quants, think of volatility only in mathematical terms. If we want to do a better job at pricing securities and managing risk, we need to go deeper, we need to understand the origins of financial market volatility.

Dr. Richard Bookstaber, Founder, Fabric RQ
Dr. Richard Bookstaber, Founder, Fabric RQ

Rick Bookstaber is a noted expert in financial risk management, and is the author of The End of Theory (Princeton, 2017), and A Demon of Our Own Design (Wiley, 2007). He is the founder of Fabric RQ, a platform for providing risk management to wealth managers and asset owners. His career has spanned chief risk officer roles on both the buy side at Moore Capital and Bridgewater, and on the sell side at Morgan Stanley and Salomon. From 2009 to 2015 Rick served in the public sector at the SEC and the U.S. Treasury, drafting the Volcker Rule, building out the risk management structure for the Financial Stability Oversight Council, and developing an agent-based model to assess financial vulnerabilities. Most recently he was the Chief Risk Officer in the Office of the CIO for the University of California, with oversight across its $160 billion pension and endowment portfolios. His various roles have put him at the center of the critical crises of the last three decades – working with portfolio insurance during the 1987 Crash while at Morgan Stanley, overseeing risk at Salomon during the 1998 failure of Long-Term Capital Management (dubbed “Salomon North”), and with the aftermath of the 2008 Crisis while in the regulatory sphere. A black belt in Brazilian jiu jitsu, Rick can be found training at the Renzo Gracie Academy in his free time. Rick received a Ph.D. in economics from MIT.

Ryan Ferguson
On Accuracy Guarantees for Machine Learning in Derivatives Pricing
On Accuracy Guarantees for Machine Learning in Derivatives Pricing

Riskfuel pioneered the use of machine learning to produce excellent analytic approximations of traditional derivatives pricing models, achieving performance improvements of more than a million-fold without compromising accuracy. In this talk, I will describe some of the techniques Riskfuel uses to validate its models across the entire domain of approximation.

Ryan Ferguson, Founder and CEO, Riskfuel
Ryan Ferguson, Founder and CEO, Riskfuel

Ryan Ferguson is Founder and CEO of Riskfuel, a company providing ultra-fast AI-based pricing technology for structured capital markets products. Before founding Riskfuel, he had a 17-year career as senior trader and quant at Scotiabank and TD Bank. His most recent role was Managing Director and Head of Securitization, Credit Derivatives and XVA. Ryan successfully managed a credit derivatives book through the global financial crisis. He has a PhD in Physics from Imperial College London, and a MASc in Electrical Engineering from University of Waterloo.

Steve Schulist
Fat Tailed Kelly
Fat Tailed Kelly

Asset returns have fat tails. Traditional mean-variance analysis, however, assumes that asset returns are normally distributed, with thin tails. A natural extension of the normal distribution is the α-stable distribution, which has fat tails, skewness—and infinite variance, which makes traditional mean-variance optimization impossible. In this talk, I review the data, examine the investment implications, and discuss the math of fat tailed, α-stably distributed asset returns.

Steve Schulist, Quantitative Analyst, PIMCO
Steve Schulist, Quantitative Analyst, PIMCO

Steve Schulist is a Quantitative Analyst at PIMCO. Steve has spent his career valuing bonds and building fixed income risk systems.

Tony Guida
Panel Discussion: How Can We Be More Ambitious with AI in Finance?
Panel Discussion: How Can We Be More Ambitious with AI in Finance?

Machine Learning, a subset of Artificial Intelligence, has gained increasing adoption as an important tool in finance over the last decade. The question arises, however, whether a narrow categorization of ML as a ‘tool’ means that quants are ignoring the potential of Artificial Intelligence as a paradigm. Rather than viewing AI as a means to perpetuate finance in its present form, how might operating in the AI paradigm change finance itself? With the promise of the Quantum era is it too ambitious for finance researchers to focus on modeling an “investment consciousness”?

Tony Guida, Co-Head of Systematic Macro, RAM AI
Tony Guida, Co-Head of Systematic Macro, RAM AI

Tony Guida joined RAM AI in 2019 as a Senior Quantitative Researcher and became Co-head of Systematic Macro in 2021. His work focuses primarily on extracting market inefficiencies from different sources from traditional fundamentals, market signals, alternative data, and machine learning. Tony started his career at Unigestion in the quantitative equity low volatility team. In 2015, he moved to Edhec Risk Scientific Beta as a Senior Consultant for Risk allocation and factor strategies before going to a major UK pension fund in 2016 to build the in-house systematic equity, co-managing 6 billion GBP as a senior quantitative portfolio manager.

Breakout Sessions

Angana Jacob
How to Identify and Mitigate Overfitting
How to Identify and Mitigate Overfitting

Join SigTech’s Angana Jacob in conversation with Saeed Amen, from Cuemacro, as they discuss how to identify and mitigate overfitting in a systematic strategy.

Angana Jacob, Head of Product Management, SigTech
Angana Jacob, Head of Product Management, SigTech

Angana is Head of Product Management at SigTech. Prior to joining SigTech, Angana spent more than a decade in indices and quantitative strategies at Deutsche Bank, S&P Dow Jones Indices and State Street. Her roles have spanned research, development and marketing across asset classes as well as building out State Street's ESG analytics. Angana has a degree in Computer Science from Madras University and an MBA from IIM Ahmedabad.

Saeed Amen
How to Identify and Mitigate Overfitting
How to Identify and Mitigate Overfitting

Join SigTech’s Angana Jacob in conversation with Saeed Amen, from Cuemacro, as they discuss how to identify and mitigate overfitting in a systematic strategy.

Saeed Amen, Founder, Cuemacro
Saeed Amen, Founder, Cuemacro

Saeed Amen is the founder of Cuemacro. Over the past fifteen years, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is the coauthor of The Book of Alternative Data (Wiley). Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England and Federal Reserve Board. He is also a visiting lecturer at Queen Mary University of London and a co-founder of the Thalesians.

Grant Fuller
Practical Implications of the Anticipations of Others
Practical Implications of the Anticipations of Others

Join Grant Fuller for a discussion on some of the practical implications (both positive and negative) of anticipating the anticipations of others. More specifically, we will discuss how investors’ anticipating the ‘Green Short’ might affect corporates, their executive management and boards, fund and risk managers.

Grant Fuller, CEO, Irithmics
Grant Fuller, CEO, Irithmics

Grant co-founded Irithmics, an artificial intelligence research and technology firm, in 2012 as part of an EU backed academic research call in public health and epidemiology. The technology's capability to analyse and anticipate complex behaviours, relationships and dynamics has subsequently been successfully applied to financial markets. Previously part of Ernst & Young's risk advisory practice, before which Grant led Bloomberg’s successful hedge fund trading and analytics (AIM) business in European and Asian markets. Prior to Bloomberg, Grant was part of RiskMetrics, establishing their European fund and asset management analytics and consulting capabilities. He holds a BSc in Chemistry from the University of St Andrews where he remained to undertake a PhD applying neural networks in carbohydrate chemistry, after which he joined academic research at Cambridge University, and began applying neural networks to public health and epidemiology.

Misha Fomytskyi
Robust Options Valuation and Risk Management Workflows with Vola Dynamics Analytics
Robust Options Valuation and Risk Management Workflows with Vola Dynamics Analytics

We give an overview of a modern options valuations framework, discuss common challenges with discount rates, borrow, dividends, and time conventions and illustrate how they can be easily addressed with the Vola Dynamics analytics library.

Dr. Misha Fomytskyi, Co-Founder, Vola Dynamics LLC
Dr. Misha Fomytskyi, Co-Founder, Vola Dynamics LLC

Dr. Misha Fomytskyi is an expert in derivatives trading, risk management, and volatility modeling. Prior to co-founding Vola Dynamics LLC, he spent 12 years working in the derivatives space in trading and quant roles, including head of the options trading team at Getco LLC, as portfolio manager at JD Capital Management, and the founder and CEO of Mivol LLC. Having a deep understanding of trading, modeling, and technology allowed him to be a driving force in adopting quantitative techniques in valuation, risk management and trading analysis in these businesses. He received his Ph.D. in physics from The University of Texas at Austin in 2004.

Pradeep-Gupta
AI Powered Traders: Ready or Not?
AI Powered Traders: Ready or Not?

AI and GPU computing are changing the art of possible in financial services. Traders, whether human or algorithmic, will have to adapt to the ever-changing AI and ML landscape. The growing capability of AI algorithms coupled with the increase in total available data and super powerful computers, means that firms without an AI strategy will soon be surpassed. Discretionary traders who fail to augment their human traders with teams of AI assistants will find themselves late and trailing the performance of their more data savvy peers. Systematic traders will find themselves in a war being fought on two fronts – total intelligence will relate to how much AI and machine learning can be brought to bear across algorithm development, and faster intelligence will be a measure of how much intelligence can be squeezed into a target trading window – the shorter the window, the more challenging it will be to execute large AI-driven models. In this talk, I will be presenting updates on the trader of tomorrow with recent work done in last year mainly covering areas NLP, HPC and AI, Synthetic data and advancements in computing technologies.

Pradeep Gupta, Global Senior Director of Solutions Architecture and Engineering, NVIDIA
Pradeep Gupta, Global Senior Director of Solutions Architecture and Engineering, NVIDIA

Pradeep Gupta is Global Sr. Director of the Solutions Architecture and Engineering team at NVIDIA. He is responsible for running technical customer engagements for all AI industries with a dedicated focus on financial services, autonomous driving, healthcare, energy, retail and telecoms where AI is transforming industry solutions. His focus is on building production-grade AI that can be deployed in life-critical systems. Previously, Pradeep worked in areas like high-performance computing, computer vision, mathematical library development, and data center technologies. He received a master's degree in research from the Indian Institute of Science (IISc), Bangalore. His research focused on developing compute-efficient algorithms.

Ryan-Ferguson
Deploying an AI-based XVA Platform into Production
Deploying an AI-based XVA Platform into Production

Riskfuel CEO Ryan Ferguson and Andrew Green, lead XVA quant at Scotiabank, discuss lessons learned deploying AI valuation models into Scotiabank's XVA platform.

Ryan Ferguson, Founder and CEO, Riskfuel
Ryan Ferguson, Founder and CEO, Riskfuel

Ryan Ferguson is Founder and CEO of Riskfuel, a company providing ultra-fast AI-based pricing technology for structured capital markets products. Before founding Riskfuel, he had a 17-year career as senior trader and quant at Scotiabank and TD Bank. His most recent role was Managing Director and Head of Securitization, Credit Derivatives and XVA. Ryan successfully managed a credit derivatives book through the global financial crisis. He has a PhD in Physics from Imperial College London, and a MASc in Electrical Engineering from University of Waterloo.

Andrew Green
Deploying an AI-based XVA Platform into Production
Deploying an AI-based XVA Platform into Production

Riskfuel CEO Ryan Ferguson and Andrew Green, lead XVA quant at Scotiabank, discuss lessons learned deploying AI valuation models into Scotiabank's XVA platform.

Andrew Green, Managing Director and lead XVA Quant, Scotiabank
Andrew Green, Managing Director and lead XVA Quant, Scotiabank

Andrew Green is a Managing Director and lead XVA Quant at Scotiabank in London. Prior to joining Scotiabank, Andrew held roles as a quantitative analysis in several different banks in London. He is the author of XVA: Credit, Funding and Capital Valuation Adjustments, published by Wiley.

Serge Kouyoumjian
ITO33 on Convertible Bonds and Banking Cocos
ITO33 on Convertible Bonds and Banking Cocos

After a short presentation of ITO33 and of its history and achievements over the last twenty years, this Session will be used to present the services ITO33 offers on both Banking Regulatory Capital securities and Convertible Bonds, two of our main areas of expertise. Our team of experts will take questions following Philippe Henrotte’s presentation at Quant Insights the day before. If time allows, we will also cover aspects of Opscore, ITO33’s best selling product for Convertible Bonds.

Serge Kouyoumjian, Co-Founder and Partner, ITO 33
Serge Kouyoumjian, Co-Founder and Partner, ITO 33

Serge graduated from ESSEC, the French business school, in 1986, before leading a long and successful banking career in Paris, London and New-York as a fixed-income specialist, for names like CCF, UBS and Credit Suisse. He gained significant experience in market-making, trading, and risk-management. Serge is a co-founder of ITO 33, and the one in charge of our business and commercial endeavors. He delights in the possibility that ITO 33 may grow to captivate the financial services community as a company with a grand vision and lofty aspirations, while being grounded in its research, engineering and hands-on spirit. His happier moments include when he feels he has earned a new client's trust and support.

Conference Tickets

27th - 28th October 2021

Watch conference recordings

Conference Organizers

Quant Insights is presented by the CQF Institute & Wilmott
CQF Instiute

Promoting the highest standard in practical financial engineering, the CQF Institute, part of Fitch Learning, is a global membership organization dedicated to educating and building the quantitative finance community. The CQF Institute is also the awarding body for the Certificate in Quantitative Finance (CQF) the world’s largest professional qualification in quantitative finance.

Fitch Learning

Part of the Fitch Group, Fitch Learning partners with businesses to help develop the future leaders of the financial services industry. Alongside centers in established financial hubs, Fitch Learning utilizes a best-in-class technology platform to deliver blended learning solutions that maintain the personal element of development.

Wilmott

Wilmott is the leading resource for the quant finance community, comprised a website and discussion forum and Wilmott magazine.

Conference Sponsors

Platinum Sponsors
Riskfuel
Vola Dynamics
Irithmics
ITO 33
Gold Sponsor
SigTech
NVIDIA
Affiliate Sponsor
Wind
The Brokerage
Academic Partners
Gaodun Education
COST Action FinAI