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World Business StrategiesServing the Global Financial Community since 2000

Friday 26th March 2021

Stream One: Volatility & Options
Smiles Without Tears: Semi-Analytic Short Rate Modelling with Smile and Skew

EST: 08.00
GMT: 13.00
CET: 14.00

  • Comparatively few analytic formulae exist in relation to short rate models.
  • Analytic tractability is sacrificed to fit volatility smile and skew.
  • Idea: instead of using LSV, generate smile and skew through configurable functional dependence of short rate on underlying.
  • Consider spline representations of the short rate with reference to mean-reverting normal or lognormal underlyings.
  • Using perturbation methods, deduce accurate analytic representation of forward rate evolution.

Colin Turfus:

Quantitative Analyst, Deutsche Bank

Colin Turfus: Quantitative Analyst, Deutsche Bank

Colin Turfus has worked for the last twelve years as a financial engineer, mainly analysing model risk for credit derivatives and hybrids. More recently his interest has been in the application of perturbation methods to risk management, finding efficient analytic methods for computing, e.g., CVA, VaR and model risk. He is currently working in Global Model Validation and Governance at Deutsche Bank. He also taught evening courses on C++ and Financial Engineering at City University for seven years. Prior to that Colin worked as a developer consultant in the mobile phone industry after an extended period in academia, teaching applied maths and researching in fluid dynamics and turbulent dispersion.

Stream One: Volatility & Options
Bermudian Optionality

EST: 09.00
GMT: 14.00
CET: 15.00

Peter Carr:

Professor and Dept. Chair of FRE Tandon, New York University

Peter Carr: Professor and Dept. Chair of FRE Tandon, New York University

Dr. Peter Carr is the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering. He has headed various quant groups in the financial industry for the last twenty years. He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Prior to joining the financial industry, Dr. Carr was a finance professor for 8 years at Cornell University, after obtaining his Ph.D. from UCLA in 1989. He has over 85 publications in academic and industry-oriented journals and serves as an associate editor for 8 journals related to mathematical finance. He was selected as Quant of the Year by Risk Magazine in 2003 and Financial Engineer of the Year by IAQF/Sungard in 2010. From 2011 to 2014, Dr. Carr was included in Institutional Investor’s Tech 50, an annual listing of the 50 most influential people in financial technology.

In the 2 years Dr. Carr been FRE dept. chair, applications increased from 1,300 per year to 1,900 per year. The number of FRE Masters students in residence was the highest in any 2-year period. For the incoming 2018 class, current verbal GRE is 169/170 and GPA is 3.82. FRE moved up in Quantnet rankings both years. An online summer course was initiated last summer and an on-campus bootcamp will be initiated this summer. Six electives on machine learning in finance were introduced. The distance learning room will become operational this summer.

Stream One: Volatility & Options
Black Baskets for Spread Options

EST: 09.00
GMT: 14.00
CET: 15.00

Alexandre Antonov:

Chief Analyst, Danske Bank

Alexandre Antonov: Chief Analyst, Danske Bank

Alexandre Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997. He worked for Numerix during 1998-2017 and recently he has joined Danske Bank as the Chief Analyst in Copenhagen.

His activity is concentrated on modeling and numerical methods for interest rates, cross currency, hybrid, credit and CVA/FVA/MVA. AA is a published author for multiple publications in mathematical finance and a frequent speaker at financial conferences.

He has received a Quant of Year Award of Risk magazine in 2016.

Both Streams
Panel: Machine Learning in Quantitative Finance

EST: 11.00
GMT: 16.00
CET: 17.00

  • Unique challenges of using ML in finance
    • Dealing with scarce and non-stationary data
    • Best practices to overcome these challenges
  • How do we deal with model risk in ML?
    • Is there a way to make ML more explainable?
    • Should we trust ML models more than traditional stochastic models chosen because they have an analytical solution?
    • Will the regulators allow ML models for capital or accounting P&L?
  • How much value is ML adding?
    • Classical machine learning (RBMs, SVMs, etc.) vs. classical statistics (linear regression, convex optimization)
    • Deep learning vs. classical machine learning
    • What are the most important machine learning techniques for the future?
  • Where is the greatest potential for ML in systematic trading:
    • What styles of systematic trading can benefit from ML
    • How can ML be applied to systematic trading: sentiment analysis, time series analysis, alternative data use
  • What applications can you list among ML successes in finance?
    • Time series analysis, market generators
    • Derivative valuation
    • Capital and margin optimization
  • What are the global trends in financial services using AI/ML technologies?

Moderator:

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Friday 26th March 2021

Stream Two: Portfolio & Trading Strategies
Fourier-based methods for the management of complex insurance products

EST: 08.00
GMT: 13.00
CET: 14.00

Abstract:  We propose a framework for the valuation and the management of complex life insurance contracts, whose design can be described by a portfolio of embedded options, which are activated according to one or more triggering events. These events are in general monitored discretely over the life of the policy, due to the contract terms. The framework is based on Fourier transform methods as they allow to derive convenient closed analytical formulas for a broad spectrum of underlying dynamics. Multidimensionality issues generated by the discrete monitoring of the triggering events are dealt with efficiently designed Monte Carlo integration strategies. We illustrate the tractability of the proposed approach by means of a detailed study of ratchet variable annuities, which can be considered a prototypical example of these complex structured products.

Laura Ballotta:

Reader, Financial Mathematics, Cass Business School

Laura Ballotta: Reader, Financial Mathematics, Cass Business School

Dr Ballotta works in the areas of quantitative finance and risk management. She has written on topics including stochastic modelling for financial valuation and risk management, numerical methods aimed at supporting financial applications, and the interplay between finance and insurance.

Recent major contributions have appeared in Journal of Financial and Quantitative Analysis, European Journal of Operational Research and Quantitative Finance among others.
She serves as associate editor and referee for a number of international journals in the field.

Laura Ballotta obtained her PhD in Mathematical and Computational Methods for Economics and Finance from the Università degli Studi di Bergamo (Italy), following her BSc in Economics from Università Cattolica del Sacro Cuore, Piacenza (Italy), and MSc in Financial Mathematics from the University of Edinburgh – jointly awarded with Heriot-Watt University (UK). Laura has previously held positions at Università Cattolica del Sacro Cuore, Piacenza (Italy), and Department of Actuarial Science and Statistics, City University London (UK).

Stream Two: Portfolio & Trading Strategies
Using ML for fundamental and sentiment data with applications to trading strategies

EST: 09.00
GMT: 14.00
CET: 15.00

Artur Sepp:

Head of Research, Quantica Capital AG

Artur Sepp: Head of Research, Quantica Capital AG

Artur Sepp is Director of Research at Quantica Capital AG in Zurich focusing on development of systematic data-driven trend-following and asset allocation strategies. Prior, Artur worked at Julius Baer in Zurich as Senior Quant Strategist developing algorithmic solutions and investment strategies for the trading and portfolio advisory. Before, Artur has worked in leading roles as a Front Office Quant Strategist for equity and credit derivatives trading at Bank of America Merrill Lynch in London and Merrill Lynch in New York since 2006. Artur holds a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA in Mathematical Economics with distinction from Tallinn University of Technology. Artur’s research and expertise are on econometric data analysis, statistical machine learning and computational methods along with designing of research and trading infrastructure and technology with applications for quantitative trading, asset allocation and wealth management. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur is known for his contributions to stochastic volatility and credit risk modelling with an H-index of 15. He is a member of the editorial board of the Journal of Computational Finance.

Stream Two: Portfolio & Trading Strategies
Researching systematic FX options trading strategies with Python

EST: 10.00
GMT: 15.00
CET: 16.00

  • We will discuss the considerations when designing a software library to develop systematic trading strategies for FX options
  • We shall give examples of FX options trading strategies and show how to implement them using the open source finmarketpy library

Saeed Amen

Founder: Cuemacro

Saeed Amen: Founder: Cuemacro

Saeed has a decade of experience creating and successfully running systematic trading models at Lehman Brothers and Nomura. He is the founder of Cuemacro, Cuemacro is a company focused on understanding macro markets from a quantitative perspective. He is the author of ‘Trading Thalesians – What the ancient world can teach us about trading today’ (Palgrave Macmillan), and graduated with a first class honours master’s degree from Imperial College in Mathematics& Computer Science.

Both Streams
Panel: Machine Learning in Quantitative Finance

EST: 11.00
GMT: 16.00
CET: 17.00

  • Unique challenges of using ML in finance
    • Dealing with scarce and non-stationary data
    • Best practices to overcome these challenges
  • How do we deal with model risk in ML?
    • Is there a way to make ML more explainable?
    • Should we trust ML models more than traditional stochastic models chosen because they have an analytical solution?
    • Will the regulators allow ML models for capital or accounting P&L?
  • How much value is ML adding?
    • Classical machine learning (RBMs, SVMs, etc.) vs. classical statistics (linear regression, convex optimization)
    • Deep learning vs. classical machine learning
    • What are the most important machine learning techniques for the future?
  • Where is the greatest potential for ML in systematic trading:
    • What styles of systematic trading can benefit from ML
    • How can ML be applied to systematic trading: sentiment analysis, time series analysis, alternative data use
  • What applications can you list among ML successes in finance?
    • Time series analysis, market generators
    • Derivative valuation
    • Capital and margin optimization
  • What are the global trends in financial services using AI/ML technologies?

Moderator:

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

  • Discount Structure
  • Super early bird discount
    30% until 22nd January 2021

  • Early bird discount
    15% until 19th February 2021

  • Special Offer
    When two colleagues attend the 3rd goes free!

  • 70% Academic Discount
    (FULL-TIME Students Only)

Only five days left to claim this discount!

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