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, Professor Helyette Geman, 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 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
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. 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.

Helyette Geman
Oil Storage and Price Discovery

Professor Helyette Geman, Director of the Commodity Finance Centre, Birkbeck - University of London
Professor Helyette Geman, Director of the Commodity Finance Centre, Birkbeck - University of London

Helyette Geman is the Director of the Commodity Finance Centre at Birkbeck - University of London and a Research Professor at Johns Hopkins University. She is a graduate of Ecole Normale Supérieure in Mathematics and holds a PhD in Probability from the University Pierre et Marie Curie and a PhD in Finance from the University Pantheon Sorbonne. Professor Geman has been a scientific advisor to major financial institutions and energy companies for the last 21 years, covering the spectrum of interest rates, electricity, crude oil, metals and cryptocurrencies She was the PhD adviser of Nassim Taleb and has published more than 145 papers in top finance Journals and was the first President of the Bachelier Finance Society, featuring Paul Samuelson and Robert Merton. Professor Geman is one of the authors of the CGMY model, a pure jump Lévy process widely used in finance and insurance and has been since 2007 on the Board of the Bloomberg Commodity Index. Her book ‘Commodities and Commodity Derivatives’ is the reference in the field.

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
The S&P500 and VIX Option Conundrum
The S&P500 and VIX Option Conundrum

The quest of constructing a model that simultaneously fits observed S&P and Vix options prices has received a lot of attention over the past years. In this talk, we discuss why the problem is both relevant and challenging. We discuss the latest advances in modeling methodologies such as rough volatility and machine learning, and present some new ideas on the numerical implementation.

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

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

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.

Conference Tickets

27th - 28th October 2021

Tickets are now available and are free for CQF Institute Members.
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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
Affiliate Sponsor
Wind
Academic Partners
Gaodun Education
COST Action FinAI