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Quantitative Finance Seminars

Seminars

The Thalesians Quantitative Finance Seminars are a series of talks for dedicated finance professionals to learn about state-of-the-art quantitative finance methodology from seasoned speakers.

We provide a unique opportunity for finance professionals to further their careers by sharing expertise and cross-pollinating new methodology and ideas in the wider context of the finance industry.

Come along to our seminars in Canary Wharf (LDN), Midtown Manhattan (NYC) or Budapest (BUD) and join us for a quantitative finance talk by a guest speaker followed by discussions and often also networking drinks.

Videos

Videos of some of the London talks are available: older videos in Flash format and downloadable in MP4 format for iPod/iTunes. Newer videos are available on this website and on our YouTube site.

Cost

The London seminars charge £15 at the door for events to cover the venue hire and other expenses. Admittance to the IAQF/Thalesians NYC seminars is strictly by online registration only and is $25 for non-IAQF members.

Registration


Forthcoming Seminars for 2019


Thalesians Seminar (London) — Marcos Carreira — Learning interest rate interpolation

Marcos Carreira

Marcos Careira

Date and Time

Tuesday, March 26, 2019, at 6:30 p.m.

Venue

Marriott Hotel, Canary Wharf London, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/259128719/

Abstract

The usual methods for interest rate interpolation consider only the values and time to maturity of spot rates as the inputs, and differ mainly on the continuity of the implied forward rates. We treat the interpolation problem as a replication problem, where a bond (or interest rate future/swap) is priced as a function of the minimum variance replicating portfolio of the traded bonds (or derivatives). In this view, the hedging ratios determined by the interpolation are as important (if not more) than getting the “right” interpolated rate; this is similar to the adjustments to the Black and Scholes delta as a consequence of the joint dynamics of the asset price and volatility in the different volatility models. We show how to learn the parameters of the weight functions and apply this method to the overnight rate indexed interest rates derivatives in Brazil. We then extend the concept from interpolating broken dates to the market references, in order to determine which points are key to the shape and dynamics of the curve and which points can be replicated by these real anchors.

Speaker

Marcos C. S. Carreira, a PhD candidate at École Polytechnique, is the co-author of the book "Brazilian Derivatives and Securities: Pricing and Risk Management of FX and Interest-Rate Portfolios for Local and Global Markets". He was Derivative Products Officer and later Technical Modeling Officer at BM&FBovespa, where he contributed to risk management, derivatives pricing, exchange fees, microstructure and HFT functions. At Credit Suisse Brazil, he was a Managing Director in charge of the FX and IR Options desk, after being the Risk Manager responsible for Market, Counterparty and Liquidity Risks. Marcos holds an engineering degree from Instituto Tecnológico de Aeronáutica (ITA) and a Masters in Economics at Insper. Marcos also lectured for the MECAI Professional Masters course in Mathematical Finance at ICMC-USP and is a regular speaker at quantitative finance conferences.


Thalesians Seminar (New York) — Terry Benzschawel — Financial Applications of Machine Learning

Terry Benzschawel

Terry Benzschawel

Agenda

Monday, April 8, 2019:

Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023

Registration


Abstract

In this talk, I describe a variety of machine learning models that I have built and applied to problems in business and finance. I begin with an historical introduction to neural networks, including brief descriptions of the perceptron, and methods of gradient descent, backpropagation and regularization. I then describe single hidden-layer perceptrons built in the early 1990s to detect fraud on credit card portfolios, identify customers who will give up their credit cards, and later, for trading US Treasury bonds. I then describe recent work with deep learning networks that predict spread changes for corporate bonds, price moves from trade flows, and a natural language processing model that predicts market moves from sentiment data. Finally, I provide some thoughts on how artificial intelligence/machine learning is changing the fixed income trading business.


Speaker

Terry Benzschawel has recently left a thirty-year career on Wall Street to start his own firm. Prior to that, Terry was a Managing Director in Citigroup's Institutional Clients Business. Terry headed the Quantitative Credit Trading group which developed quantitative tools and strategies for credit market trading and risk management, both for Citi's clients and for in-house applications.

Terry received a Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). His Ph.D. thesis concerned development of a neural network model of the human visual system. Terry has done post-doctoral fellowships in Optometry at the University of California at Berkeley and in Ophthalmology at the Johns Hopkins University School of Medicine. He also was a visiting scientist at the IBM Thomas J. Watson Research Center prior to embarking on a career in finance. He currently serves on the steering committees of the Masters of Financial Engineering (MFE) Programs at the University of California at Berkeley and serves there as an Executive in Residence.

In 1988, Terry began his financial career at Chase Manhattan Bank, training genetic algorithms to predict corporate bankruptcy. In 1990, he was hired by Citibank to build neural network models to detect fraudulent card transactions and to predict credit card attrition. In 1992 he moved to investment banking at Salomon Brothers where he built models for proprietary trading for Salomon's Fixed Income Arbitrage Group. In 1998, he moved to the fixed income strategy as a credit strategist with a focus on client-oriented solutions across all credit markets and has worked in related roles since then. Terry was promoted to Managing Director at Citi in 2008.

Terry is a frequent speaker at industry conferences and events and has lectured on credit modelling at major universities. In addition, he has published over a dozen articles in refereed journals and has authored two books: CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS and CREDIT MODELING: ADVANCED TOPICS. In addition, Terry has been the instructor for courses in credit modelling for Incisive Media, the Centre for Finance Professionals, the Machine Learning Institute and has taught in UCLA’s MFE program last Fall. Finally, Terry has taught a course on credit modelling at Russia's Sberbank in Moscow.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


Acknowledgements

Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.


Thalesians Seminar (London) — Blanka Horvath — Deep Learning Volatility

Blanka Horvath

Blanka Horvath

Date and Time

Wednesday, April 24, 2019, at 6:30 p.m.

Venue

Marriott Hotel, Canary Wharf London, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/257969639/

Abstract

We present a powerful neural network based calibration method for a number of volatility models including the rough volatility family. The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling: The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several model families (such as rough volatility models) within the scope of applicability in industry practice. As customary for machine learning, the form in which information from available data is extracted and stored is crucial for network performance. With this in mind we discuss how our approach addresses the usual challenges of machine learning solutions in a financial context (availability of training data, interpretability of results for regulators, control over generalisation errors). We present specific architectures for price approximation and calibration and optimize these with respect different objectives regarding accuracy, speed and robustness. We also find that including the intermediate step of learning pricing functions of (classical or rough) volatility models before calibration significantly improves the generalisation performance compared to the performance of deep calibration networks that are trained directly on data.

Speaker

Blanka Horvath is a Lecturer at King's College London in the Financial Mathematics group, and an Honorary Lecturer in the Department of Mathematics at Imperial College London.

Blanka holds a PhD in Financial Mathematics from ETH Zurich, a postgraduate degree (Diplom) in Mathematics from the University of Bonn, and an MSc in Economics from The University of Hong Kong. In her research she lays a particular emphasis on the applicability of her research and maintains close collaborations with the industry, including: JP Morgan, Deutsche Bank, Zeliade Systems and AXA.

Her research interests are in the area of Stochastic Analysis and Mathematical Finance. They include (but not limited to):

  • Numerical methods as well as machine learning techniques for option pricing, forecasting and simulation.
  • Laplace methods on Wiener space and heat kernel expansions.
  • Smile asymptotics for local- and stochastic volatility models with a particular emphasis on rough volatility models and also SABR-type models.

Past Seminars for 2019


Thalesians Seminar (New York) — Yixiao (Ethan) Jiang — Semiparametric Estimation of a Credit Rating Model

Yixiao (Ethan) Jiang

Yixiao (Ethan) Jiang

Agenda

Tuesday, February 12, 2019:

Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023

Registration


Abstract

This paper develops a semiparametric, ordered-response model of credit rating in which ratings are equilibrium outcomes of a stylized cheap-talk game. The proposed model allows the assigned rating probability to be an unknown function of multiple indices permitting flexible interaction, non-monotonicity, and non-linearity in marginal effects. Based on Moody's rating data, I use the estimated model to examine credit rating agencies' (CRAs) incentive to bias ratings when the CRA's shareholders invest in bond issuers. I find the degree of Moody's rating bias varies significantly for both rating categories as well as the institutional cross-ownership between Moody's and the bond issuer. To obtain the statistical significance of these results, I prove a U-statistics equivalence result that is important for showing asymptotic normality for a large class of semiparametric models.

Speaker

Yixiao (Ethan) Jiang is currently a Ph.D. Candidate in Economics at Rutgers University, where he also completed his B.A. in Economics and Mathematics in 2013. Jiang’s research interest lies at the interface of finance and econometrics, with a current focus on estimating and testing credit risk and volatility models. His work has been presented at seminars at Vanguard, Research Affiliates, and various academic conferences, including the ASSA Annual Meeting, Financial Management Association Annual Meeting, and Econometrics Society meetings.

Jiang will join Christopher Newport University as a tenure-track Assistant Professor in August 2019.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


Thalesians Seminar (New York) — Joy Zhang — Agency MBS Prepayment Model using Neural Networks

Joy Zhang

Joy Zhang

Agenda

Tuesday, January 15, 2019:

Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023

Registration


Abstract

We apply deep neural networks, a type of machine learning method, to model agency MBS 30 year fixed rate pool prepayment behaviors. The neural networks model (“NNM”) is able to produce highly accurate model fits to the historical prepayment patterns, as well as accurate sensitivities to risk factors. These results are comparable with model results and intuition obtained from a traditional agency pool level prepayment model built via many iterations of trial and error of many months and years. This example shows NNM can process large data sets efficiently, capture very complex prepayment patterns, and can model large group of risk factors that are highly non-linear, and interactive. We also examine various potential shortcomings of this approach, including non-transparency/”blackbox” issue, model overfitting, and regime shift issues.

Speaker

Joy Zhang is an Executive Director and Head of Non-Agency Securitization Research at MSCI. Previously, Joy was a Director at Credit Suisse, responsible for mortgage collateral and regulatory modeling for securitized products trading. She also has worked as a senior developer at Goldman Sachs responsible for developing a firm-wide risk management system. Joy has an M.S. in Computational Finance from the Carnegie Mellon University and a Ph.D. in Chemistry from University of Kansas.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


Past Seminars

Seminars from 2018

Seminars from 2017

Seminars from 2016

Seminars from 2015

Seminars from 2014

Seminars from 2013

Seminars from 2012

Seminars from 2011

Seminars from 2010

Seminars from 2009

Our old events page is here