Friday 19th November (ALL TIMES ARE GMT)
- What is ESG and why does it matter to you?
- Key regulations and frameworks financial institutions need to be aware of
- Impacts to the Risk function
Advisory Partner focusing on LIBOR, ESG, Climate Risk & TCFD, HSBC
Navin Rauniar: Advisory Partner focusing on LIBOR, ESG, Climate Risk & TCFD, HSBC
Navin is a Risk Director with 17 years’ experience in advising the sell side on the delivery of prudential regulation such as IBOR Transition, FRTB, IRRBB, Basel III, CRR 2 and CRD V. Navin is currently leading the IBOR workstream for a Tier One bank.
Prior to this, he worked as a Senior Manager at a leading global advisory firm, where he led the analysis of the impact of the IBOR Transition on financial institutions. Additionally, Navin has spent 15 years in the industry working in global run-the-bank and change-the-bank roles for Credit Suisse, RBS, Commerzbank and JP Morgan across Front Office, Risk and Operations.
Navin is a steering committee member of the Professional Risk Managers Association where he represents the Risk Management industry on regulatory initiatives, mentoring of capital markets professionals, and a frequent speaker at banking & thought leadership events.
Head of Model Development, DZ Bank
Christian Fries: Head of Model Development, DZ Bank
Christian Fries is head of model development at DZ Bank’s risk control and Professor for Applied Mathematical Finance at Department of Mathematics, LMU Munich.
His current research interests are hybrid interest rate models, Monte Carlo methods, and valuation under funding and counterparty risk. His papers and lecture notes may be downloaded from http://www.christian-fries.de/finmath
He is the author of “Mathematical Finance: Theory, Modeling, Implementation”, Wiley, 2007 and runs www.finmath.net.
MD, Head of Quantitative Analytics and Quantitative Development, NatWest Markets
Vladimir Piterbarg: MD, Head of Quantitative Analytics and Quantitative Development at NatWest Markets
- Fallback proposal by working groups
- How good/bad is the implicit approximation?
- Non-linear transformation of payoffs and new strikes
- Implicit convexity adjustment embedded in proposals
Managing Partner muRisQ Advisory and Visiting Professor, University College London
Marc Henrard: Managing Partner muRisQ Advisory and Visiting Professor, University College London
Over the last 20 years, Marc has worked in various areas of quantitative finance. Marc’s career includes Head of Quantitative Research at OpenGamma, Global Head of Interest Rate Modeling for Dexia Group, Head of Quantitative Research and Deputy Head of Interest Rate Trading at the Bank for International Settlements (BIS) and Deputy Head of Treasury Risk also at BIS.
Marc’s research focuses on interest rate modeling and risk management. More recently he focused his attention to market infrastructure (CCP and bilateral margin, exchange traded product design, regulatory costs). He publishes on a regular basis in international finance journals, and is a frequent speaker at academic and practitioner conferences. He recently authored two books: The multi-curve framework: foundation, evolution, implementation and Algorithmic Differentiation in Finance Explained.
Marc holds a PhD in Mathematics from the University of Louvain, Belgium. He has been research scientist and university lecturer in Belgium, Italy, Chile and the United Kingdom.
Miquel Noguer Alonso:
Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI
Miquel Noguer Alonso: Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI
Miquel Noguer is a financial markets practitioner with more than 20 years of experience in asset management, he is currently Head of Development at Global AI ( Big Data Artificial Intelligence in Finance company ) and Head on Innovation and Technology at IEF.
He worked for UBS AG (Switzerland) as Executive Director.for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006.
He is professor of Big Data in Finace at ESADE and Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain).
Friday 19th November (ALL TIMES ARE GMT)
- On overview of the main generative models
- Restricted Boltzmann Machines
- Variational Autoencoders
- Generative Adversarial Networks
- Application to Market Data Generation
Managing Director and XVA Lead Quant, Scotiabank
Andrew Green: Managing Director and XVA Lead 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.
Head of Research and Development: MoCaX Intelligence
Mariano Zeron: Head of Research and Development: MoCaX Intelligence
Mariano leads our Research & Development work. He has vast experience in Chebyshev Spectral Decomposition, machine-learning and related disciplines, and their application to quantitative problems in the financial markets. Mariano holds a Ph.D. in Mathematics from Cambridge University.
- Why we need generative models
- Overview of various approaches
- GAN architectures for detecting model issues
Co-founder and CEO, Yields.io
Jos Gheerardyn: Co-founder and CEO of Yields.io
Jos is the co-founder and CEO of Yields.io. Prior to his current role he has been active in quantitative finance both as a manager and as an analyst. Over the past 15 years he has been working with leading international investment banks as well as with award winning start-up companies. He is the author of multiple patents applying quantitative risk management techniques on imbalance markets. Jos holds a PhD in superstring theory from the University of Leuven.
Lecturer, King’s College London and Researcher, The Alan Turing Institute
Blanka Horvath: Lecturer, King’s College London and Researcher, The Alan Turing Institute
Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.
Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.
Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.
We present a new method for calculating conditional expectations in a model free and data driven way that at the same time is semi-analytic and, thus, fast. It is relevant to many fields of quantitative finance, e.g. we consider the calibration of stochastic local volatility models, pricing of exotic bermudan options in one and multiple dimensions or discuss possible applications to xVA. Pricing of vanilla options with rough stochastic volatility models and rainbow/basket options with high dimensional Heston models serve as illustrating examples.
The method applies statistical learning techniques placed into the quantitative finance setting. The key ingredient, the distribution, is stabilized with a proxy hedge. In our illustrations this leads to time discrete minimal variance delta hedges. The distribution estimation is numeric but does not use kernel estimation and, thus, faces no subtile bandwidth selection, the further calculations for obtaining the delta and the conditonal expectation value are purely analytic. Since the applied methodology is at the same time a generative method simulation wrt the distributions is also possible.
Finally we discuss the challenges for applications in high dimensional settings and techniques for mitigation.
Related but different approaches recently applied are Differential Machine Learning, Q-Learners for financial models or dynamically controlled kernel estimation.
Finciraptor, AcadiaSoft, University of Wuppertal and Cape Town
Jörg Kienitz: Finciraptor, AcadiaSoft, University of Wuppertal and Cape Town
Jörg Kienitz works in Quantitative Finance and Machine Learning at Acadiasoft and the owner of the Finciraptor website (finciraptor.de). He is primarily involved in consulting on the development, implementation and validation of models. Jörg lectures at the University of Wuppertal as an Assistant Professor and is an Adjunct Associate Professor at UCT. He has addressed major conferences including Quant Minds and WBS Quant Conference. Jörg has authored four books “Monte Carlo Object Oriented Frameworks in C++” (with Daniel J. Duffy), “Financial Modelling” (with Daniel Wetterau), “Interest Rate Derivatives Explained I” and “Interest Rate Derivatives Explained II” (with Peter Caspers).
His SSRN author page is https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=744396″
University of Oxford”, Academic Visitor & “QuantBright” Consultant
Katia Babbar: University of Oxford”, Academic Visitor & “QuantBright” Consultant
Friday 19th November (ALL TIMES ARE GMT)
- A (Gaussian) copula approach is the market standard method to price and quote basket tranches and other basket credit derivatives
- At the times of market distress, implied (base) correlation may approach 100%
- Traditional semi-analytical pricing methods become unstable in this case
- A perfectly analytical solution exists for the purely co-monotone copula for certain basket credit payoffs
- We utilize such solution to propose an interpolation-based method of pricing basket credit derivatives in the nearly co-monotone case
Head of Structured Credit QA, Barclays Investment Bank
Andrey Chirikhin: Head of Structured Credit QA, Barclays Investment Bank
Andrey was formerly Head of Modelling and Quantitative Analytics for L1 Treasury, part of a USD 25bn privately held investment vehicle LetterOne. Prior to LetterOne, Andrey was MD and Head of CVA and CCR quantitative Analytics at RBS. There he has created and run the front office cross asset CVA quant team. He also restructured and led the risk-side quant team charged with delivering a new Basel III compliant internal CCR methodology. The system utilizing the newly delivered methodology has won the 2013 Internal System of the year Risk award. In his 20 year career in investment banking, Andrey held several leadership and senior quant positions at Goldman Sachs, HSBC and Dresdner Kleinwort. Andrey Chirikhin holds PhD in Theoretical Statistics from Warwick University (UK), MBA from INSDEAD and MSc in Applied Mathematics from Moscow Institute for Physics and Technology (Phystech).
Since 2018 Andrey runs his own company, Quantitative Recipes, that advises on wide rage of XVA, long-term market modelling for risk and quant infrastructure.
- We explore an alternative counterparty risk backtesting approach using Bayesian statistics.
- It is well know that classical null hypothesis testing which underlies typical backtesting methodologies suffers from conceptual and practical issues.
- Counterparty backtesting in particular suffers from low power and lack of interpretability.
- In this talk we will outline a practical alternative that can provide more intuitive and more meaningful results.
Global head of Counterparty Credit Risk Quantitative Research, J.P. Morgan
Matthias Arnsdorf: Global head of Counterparty Credit Risk Quantitative Research, J.P. Morgan
Since 2012 Matthias has been heading the counterparty credit risk quantitative research team globally.
His main responsibilities include the development & support of J.P. Morgan’s suite of credit exposure models which are used for valuation and risk management as well as credit capital.
Prior to his work in credit risk, Matthias headed the market risk capital modelling effort in EMEA for two years. Matthias started his career in finance in 2002 as a credit derivatives quantitative researcher at UBS and J.P.Morgan.
Matthias holds a PhD in Quantum Gravity from Imperial College London and has spent two years as a post-doctoral researcher at the Niels Bohr Institute in Copenhagen prior to his move to quantitative finance.
The presentation details the xVA desks learnings over the covid-19 pandemic from a modelling perspective.
Studying the limitations of a popular model used to generate proxy credit spreads for counterparties with no liquid CDS (often referred to as the cross-section method) over February-April 2020 resulted in some tweaks which gave clearer and more intuitive deltas and therefore better and more efficient hedging.
In the presentation I will discuss the analysis behind these model tweaks as well as some numerical testing results using the data covering the aforementioned period. Also I would discuss how proxy hedge could work under future FRTB CVA regulation from a desk perspective.
Senior Quantitative Analyst, XVA Trading Desk, Nordea
Shengyao Zhu: Senior Quantitative Analyst, XVA Trading Desk, Nordea
Shengyao currently works as a senior quantitative analyst at Nordea XVA trading desk. Before this, Shengyao worked in different banks in Europe and Asia as quantitative analyst for market risk and counterparty credit risk. Shengyao hold a master degree of mathematical modelling from Technical University of Denmark and a Bachelor degree from Central University of Finance and Economics, Beijing China.
We consider the model risk born of adverse selection, within the available models, of those leading to high purchase prices (as necessary for competitiveness on the market), even if this means alpha-leakage, but with corresponding losses that are more than offset in the short-and medium-term by gains on the hedging side of the position. At least, this happens until a financial crisis reveals the erroneous nature of the model used, forcing the bank to liquidate its position and its hedge at the cost of heavy losses. This “Darwinian” model risk is directional (related to a long-term moment of order one of), likely to stay unnoticed from traditional risk systems, which are focused on shorter-term moments of order two and beyond. One possible approach to detect it consists of long-term, large-scale simulations, revealing the consequences of using various models in extreme scenarios. The erroneous models are then discarded while the admissible models can be combined within a Bayesian robust approach.
Based on joint works with Claudio Albanese (Global Valuation Ltd) and Stefano Iabichino (JP Morgan).
Professor of Mathematics Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation
Stéphane Crépey: Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM)
Stéphane Crépey is the Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM). Formerly professor at the Mathematics Department of University of Evry (France), head of Probability and Mathematical Finance and head of the Engineering and Finance branch (M2IF) of the Paris-Saclay Master Program in Financial Mathematics. His research interests are financial modeling, counterparty and credit risk, numerical finance, as well as related mathematical topics in the fields of backward stochastic differential equations and partial differential equations. He is the author of numerous research papers and two books: “Financial Modeling: A Backward Stochastic Differential Equations Perspective” (S. Crépey, Springer Finance Textbook Series, 2013) and “Counterparty Risk and Funding, a Tale of Two Puzzles” (S. Crépey, T. Bielecki and D. Brigo, Chapman & Hall/CRC Financial Mathematics Series, 2014).
He is an associate editor of SIAM Journal on Financial Mathematics, International Journal of Theoretical and Applied Finance, and a member of the scientific council of the French financial markets authority (AMF). Stéphane Crépey graduated from ENSAE and he holds a PhD in applied mathematics from Ecole Polytechnique and INRIA Sophia Antipolis.
Manager, Quantitative Risk, U.S. Federal Reserve Board
Michael Pykhtin: Manager, Quantitative Risk, U.S. Federal Reserve Board
Michael Pykhtin is a manager in the Quantitative Risk section at the U.S. Federal Reserve Board. Prior to joining the Board in 2009 as a senior economist, he had a successful nine-year career as a quantitative researcher at Bank of America and KeyCorp. Michael has edited “Counterparty Risk Management” (Risk Books, 2014) and “Counterparty Credit Risk Modelling” (Risk Books, 2005). He is also a contributing author to several recent edited collections. Michael has published extensively in the leading industry journals; he has been an Associate Editor of the Journal of Credit Risk since 2007. Michael is a two-time recipient of Risk Magazine’s Quant of the Year award (for 2014 and 2018). Michael holds a Ph.D. degree in Physics from the University of Pennsylvania and an M.S. degree in Physics and Applied Mathematics from Moscow Institute of Physics and Technology.
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.