Financial Mathematics Practitioners Seminar Autumn 2018

This seminar series, which is hosted by the academic members of staff who are involved with the MSc in Financial Mathematics, presents applied research in financial mathematics and related topics.

Autumn 2018

It aims to bring together mathematicians working in the financial industry and researchers in Mathematical Finance. Attendance is open to the public.

All seminars (unless otherwise stated) will take place on Wednesday from 4.00pm to 5.00pm in Harrie Massey LT, 25 Gordon Street. There will be tea and coffee afterwards in either room 502 or room 606 in the Mathematics Department. If you require any more information on the Financial Mathematics Practitioners Seminars please contact Dr Camilo Garcia Trillos (e-mail: camilo.garcia AT ucl.ac.uk) or Miss Justine Walker (e-mail: justine.walker AT ucl.ac.uk).

24 October 2018 

Speaker: Peter Mitic (Santander Bank)

Title: Easy solutions to difficult problems – sometimes!

The topics covered in this lecture will be those that have been most relevant to our research and routine operations during the past five years. Although we have concentrated on methods to quantify operational risk, we have also pioneered the statistics of sentiment analysis, and made significant progress in areas such as measuring Conduct Risk, Capital Value Adjustment (KVA), and detection of financial trading anomalies.  In this lecture, some principle themes that underpin much of the work that we do are presented in context. They are: sentiment analysis, Bayesian methods, measuring operational risk, visualisations, and fitting curves to data. None of these are without their problems. In some cases it is easy to find an ‘easy solution’, even if the problem is ‘complex’, but not always! Illustrative examples suggest what might be done when ‘solutions’ are not what might be expected.

Bio: Peter is Honorary Professor in the Department of Computer Science at University College London, and Head of Operational Risk Methodology at Santander Bank, UK. In the 1970s, Peter studied mathematics at Oxford University, and later gained a PhD from the Open University, where he researched object-oriented modelling techniques with computer algebra. Following some years as a lecturer in mathematics, he has been working on risk-related projects in major banks in the UK and the Netherlands for more than twenty years. Most recently his main activities have been to develop new statistical techniques in operational risk, and to formulate a framework for measuring and investigating the statistical properties of reputational risk. During the past few years Peter has spoken at conferences numerous times, and has published papers and book chapters on risk-related topics.

14 November 2018 in Room 505, 25 Gordon Street

Speaker: Alexei Kondratyev (Standard Chartered)

Title: Learning Curve Dynamics with Artificial Neural Networks

Our objective is to learn the natural curve shapes with the help of Artificial Neural Networks (ANN). The research aims to improve on curve dynamics predicted by PCA. By running the ANN on datasets of historically observed term structures of forward crude oil prices and interest rate swap rates we learn how the curves evolve over time. This allows us to determine stable natural shapes of the curves as well as to detect statistically significant deviations from the expected, predicted behaviour.

Bio: In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading. He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.  Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.

28 November 2018 

Speaker: Dr. Jian Chen (Morgan Stanley)

Title: A non-parametric approach in liquidity modelling in fx

We are going to have an intuitive review of non-parametric approach and deep neural network followed by its trading applications in liquidity modelling in FX. We will focus on the intraday patterns of FX liquidity and how DNN helps to capture these features in a consistent framework.

Bio: Jian leads the Quantitative Solutions and Innovations (QSI) team, a client facing Quant team, in Morgan Stanley focusing on developing data-driven trading analytics. As a data scientist and mathematician, Jian has extensive experience in market microstructure study, modelling and building real world trading applications. Jian holds a DPhil in computer science and MSc in Mathematical Finance from Oxford University. He has published papers on leading financial journals including Risk Magazine and Quantitative Finance.

12 December 2018 

Speaker: Chris Kenyon (MUFG Securities EMEA plc)

Title: Reinforcement Learning for Marginal KVA Pricing

Financial market regulations require allocation of capital against derivative positions, and KVA is the profit required by a trade to pay for this capital allocation and so break even.  We describe different models of capital creation and consumption and how they affect the marginal KVA required for a new trade.  These models introduce new alternatives to the design of sales margins which we investigate.  Three different solution approaches can be used in KVA pricing (dynamic programming; linear/stochastic programming; and reinforcement learning) and we describe the advantages and disadvantages.

Bio: Dr Chris Kenyon is head of XVA Quant Modelling at MUFG Securities EMEA plc.  Previously he was Head of XVA Quantitative Research at Lloyds Banking Group, head quant for Counterparty Credit Risk at Credit Suisse, and (post-crisis) Head of Structured Credit Valuation at DEPFA Bank Plc.  He is active in XVA research, introducing KVA and MVA, with Andrew Green, in Risk papers 2014-15 and their accounting treatment in 2016-17 as well as double-semi-replication.  He publishes mostly in the Cutting Edge section of Risk magazine (most-cited author 2016, 5th most-published author 1988-present), co-wrote “Discounting, LIBOR, CVA and Funding” (Palgrave 2012) and co-edited “Landmarks in XVA” (Risk 2016). He has a Ph.D. from Cambridge University, and is an author of the open-source software Quantlib