Abstracts
Keynote Presentations
09:30–10:00
In December 2017, the Prudential Regulation Authority brought forward a consultation document highlighting themes around effective model management further to initial communications in the 2017 annual concurrent stress tests.
The consultation document outlines four principles, which address the need for a comprehensive governance framework, including clear identification of models and purpose, an appropriate governance structure, defined roles of stakeholders, model developers, model owners and control functions. For model life cycle management, key themes include development, validation, independent review, use of judgement, implementation and the use of models supported by adequate documentation, IT systems and appropriate levels of reporting to senior management.
In this talk, Diederick will discuss the four principles on model risk management for stress testing outlined in the consultation paper, as well as offer thoughts on model risk management and governance structures more generally.
Diederick Potgieter, The Bank of England
10:00–10:30
In 2018, financial institutions face significant demands to generate a multitude of models for managing risk and generating returns across diverse activities and markets. Furthermore, internal reviewers and external regulators are scrutinizing the associated model governance and management processes ever more closely. In this context, overall time to implementation has become a point of competitive advantage.
This talk describes how MATLAB® is being used to reduce end-to-end model development time and risk through:
- Supporting smooth data preparation and modelling workflows from exploration to automation
- Enabling the practical application of machine learning and optimisation techniques
- Combining automatically generated model documentation with expert insights and feedback
- Bundling data, models, and documentation for internal and external review
- Simplifying the deployment of models into production
- Orchestrating the ongoing validation of models in service
David Sampson, MathWorks
Customer Presentations
11:00–11:30
Giles Spungin will discuss recent work on improving and extending HSBC’s Pillar 2 operational risk model from a desktop-based process to a dynamically scalable cloud-based solution. He will examine how the model was scaled, first in serial by translating the legacy model into MATLAB® and then in parallel running the application on the cloud, reducing the model execution time from days to hours. He will provide his thoughts on the opportunities that cloud implementation can offer financial
Giles Spungin, HSBC
12:00–12:30
Swiss Re is the world's
For a decade, Swiss Re has used MATLAB® to implement its internal risk model, ICAM (Internal Capital Adequacy Model). Dynamic and increasingly complex internal and regulatory requirements create a challenging development environment, where MATLAB proved to be the perfect development platform to quickly react to changing requirements. In 2017, Swiss Re concluded a major project to overhaul its internal risk model with the key goals being transparency, flexibility for future developments, speed, and precision of risk measures.
This presentation demonstrates how MATLAB is used within Swiss Re's internal risk model, including organizing and processing data with the table data structure in MATLAB; running fast algorithms that are in some cases accelerated by MATLAB Distributed Computing Server™; building graphical user interfaces, and visualizing data.
Dr. Daniel Meier, Swiss Re
14:15–14:45
The aim of this presentation is to introduce online lecture construction using recent developments in MATLAB®, such as live scripts, video recording, and graphical applications. Its focus is on advanced quantitative topics, such as stochastic modelling and Monte Carlo simulation, and it is intended for an audience with an interest in financial applications. The merits of the proposed blend of theory and practice by means of various examples is highlighted.
14:15–14:45
Simulated macro-stress scenarios have become an important part of the analytical toolkit central banks and regulators use to assess vulnerabilities on the balance sheets of financial institutions. Jaromir from GPM Network presents a MATLAB® based toolkit that integrates and streamlines the main tasks, from macroeconomic shock identification through macro-financial feedback, to aggregate balance sheets and regulatory indicators. The toolkit is used in our technical assistance projects in different parts of the world.
Jaromir Benes, GPM Network
16:00–16:30
This talk considers improved financial forecasting in possibly nonlinear dynamic settings, with high dimension and many predictors (“big data” environments). To overcome the curse of dimensionality and manage data and model complexity, deep learning algorithms is examined. In an application to forecast equity returns, the proposed approach captures nonlinear dynamics between equities to enhance forecast performance. It offers a significant improvement over current univariate and multivariate models in terms of trading simulation.
Ali Habibnia, London School of Economics and Political Science
16:00–16:30
Robust, realistic, and efficient modelling of profitability and risk are essential to maintaining a competitive edge in the global reinsurance market. In this talk, Paul Bassan describes how and why Aspen Re perform frequency severity simulations and how they are implemented, serving hundreds of users with seconds-level responsiveness. He describes Aspen Re’s MATLAB® based development, debug and test processes, as well as their MATLAB Production Server™ infrastructure deployments using Excel® over Citrix.
Paul Bassan, Aspen Re
MathWorks Presentations
11:30–12:00
Machine and deep learning are increasingly prominent technologies, causing a media sensation and challenging many technical disciplines, invigorating financial quantitative modelling and driving FinTech. Learn how to get started quickly with machine learning and deep learning techniques in MATLAB® and how such techniques can support facilitate time-series forecasting, stock classification, and risk management model selection workflows. The talk also outlines how machine and deep learning facilitates image, computer vision, and audio processing capabilities in MATLAB, relevant to cheque and currency identification, cashpoint monitoring, and market abuse regulatory compliance (e.g. voice recordings).
Dr. Alexander Diethert, MathWorks
13:30–14:15
This presentation discusses and demonstrates key new features for risk management, time-series modelling, instrument pricing, and financial data connectivity. Highlights include recent foundation capabilities, such as the table and timetable data types, and focus on key applications, such as credit scorecard modelling, value-at-risk, expected shortfall backtesting, instrument pricing with stochastic volatility, and a demonstration of time-series modelling with the Econometric Modeler app.
13:30–14:15
As the size and complexity of your MATLAB® application increases, you want make sure to structure software projects well, ensuring users can run code without encountering unexpected behaviour or errors, for example. In this talk, you will learn about relevant advanced MATLAB software development capabilities, including error handling, object-oriented programming (OOP), unit testing, version control, and change tracking.
Paul Peeling, MathWorks
15:15–16:00
Sentiment scores, derived from text, such as newsfeeds and social media, offer important information to determine portfolio positions and trading signals, while text analytics can offer opportunities to identify misconduct. However, a document’s sentiment is often a weak signal surrounded by a large amount of noise. Extracting that signal requires a variety of techniques for working with data both in text and numeric formats, as well as machine learning techniques for automating the sentiment scoring process on large quantities of data.
Learn how to use text analytics capabilities in MATLAB® to build your own sentiment analysis tools. This presentation covers the entire sentiment scoring workflow, including importing social media feed data into MATLAB, preprocessing and cleaning up the raw text, converting text to a numeric format, and applying machine learning techniques to derive sentiment scores.
Liliana Agapito de Sousa Medina, MathWorks
15:15–16:00
This presentation describes best practices for how you can make MATLAB® applications enterprise-ready, scale models appropriately no matter your IT infrastructure, and incorporate analytics into production systems. It outlines how MATLAB can interact with data lakes and data streaming environments.
Diederick Potgieter
The Bank of England
Diederick is a risk specialist at the Prudential Regulation Authority, The Bank of England. His responsibilities include ICAAP assessments, stress testing and capital management technical reviews. He holds a Ph.D. in Mathematical Statistics and his specialities include credit risk, operational risk, concentration risk, stress testing and economic capital frameworks. Before joining the FSA/PRA in 2011 he was Director of Capital Modelling at Barclays bank.
David Sampson
MathWorks
David Sampson is a principal engineer with MathWorks Consulting Services. In this role, he applies methods from the fields of mathematical modelling, optimisation, control, and software development across a range of industries. David is a mechanical engineer by training. David holds a Ph.D. from the University of Cambridge, U.K., and a bachelor of engineering from the University of Sydney, Australia.
Dr. Ioannis Kyriakou
Cass Business School
Ioannis Kyriakou is a senior lecturer in Actuarial Finance at Cass Business School. He holds a B.Sc. in actuarial science from Cass Business School and an M.Sc. in risk and stochastics from London School of Economics and Political Science. He completed his Ph.D. in finance at Cass Business School and joined as a lecturer at the Faculty of Actuarial Science and Insurance in 2011. In 2016, he was also appointed as a visiting professor at the Università del Piemonte Orientale. Previously, he worked for Lloyd’s Treasury and Investment Management on Lloyd’s Investment Risk Model for measuring the market and credit risks under the Solvency II Directive.
Dr. Laura Ballotta
Cass Business School
Laura Ballotta's research interests are in the areas of mathematical finance, risk management, and financial engineering, with particular focus on problems of practical relevance in current financial markets conditions, such as counterparty credit risk (CCR) valuation and collateral design, and development of realistic models for the dynamics of the relevant risk drivers which also recognize the interdependence in place between them. Laura obtained her Ph.D. in mathematical and computational methods for economics and finance from the Università degli Studi di Bergamo (Italy). She has previously held positions at Università Cattolica del Sacro Cuore, Piacenza (Italy), and Department of Actuarial Science and Statistics, City University London (UK). Laura graduated with a B.Sc. in economics from Università Cattolica del Sacro Cuore, Piacenza (Italy), and an M.Sc. in financial mathematics from the University of Edinburgh, jointly awarded with Heriot-Watt University (UK).
Dr. Gianluca Fusai
Cass Business School
Gianluca Fusai is a reader in mathematical finance and holds a position in financial mathematics at the Università del Piemonte Orientale. He holds a Ph.D. in finance from Warwick Business School, an M.Sc. in statistics and operational research from the University of Essex and a B.Sc. in economics from Bocconi University. His research interests focus on financial engineering, numerical methods for finance, portfolio selection, and energy markets. He has published extensively on these topics in Mathematical Finance, Finance and Stochastics, Quantitative Finance, Journal of Banking and Finance, Journal of Computational Finance, Risk, Annals of Applied Probability, and the International Journal of Theoretical and Applied Finance. Gianluca has co-authored the textbook Implementing Models in Quantitative Finance (Springer Finance) and has worked as a consultant in the public and private sectors.
Jaramir Benes
GPM Network
Jaromir Benes has been in the macroeconomic modelling profession for more than 15 years, working for several central banks and international organizations. He works on macro-financial projects for the International Monetary Fund and the Global Projection Model network. He is the author of IRIS, a MATLAB based toolbox for macromodeling.
Ali Habibnia
London School of Economics and Political Science
Ali Habibnia is working towards a Ph.D. in statistics (time series and statistical learning) at the London School of Economics and Political Science. He received his M.Sc. in quantitative finance from Cass Business School, and he also holds an M.Sc. and a B.A. in economics from the University of Tehran. His research focuses on the intersection of machine learning and big data econometrics, with a particular interest in the high-dimensional nonlinear time-series analysis and their applications in financial forecasting. Ali worked as a trader and portfolio strategist for four years and developed different trading strategies and algorithms. He has been appointed as an assistant professor of big data economics at Virginia Tech for the upcoming academic year.
Paul Bassan
Aspen Re
In 2015, Paul joined Aspen Re as an actuarial analyst to implement a new MATLAB based reinsurance pricing system. He obtained a B.Sc. in physics at the University of Warwick, followed by an M.Sc. and Ph.D. in physics at the University of Manchester. His doctoral thesis was on the interaction of radiation with scattering particles. Paul then went on to become a post-doctoral researcher at the Manchester Institute of Biotechnology where he focused on algorithm development and machine learning for a next generation cancer detection system.
Liliana Agapito de Sousa Medina
MathWorks
Liliana Agapito de Sousa Medina is a software developer specializing in text analytics. Before joining MathWorks, Liliana worked as a data scientist on applications for automated data collection, information extraction, and predictive modelling, with a focus on text data and NLP. Liliana holds an M.Sc. in electrical and computer engineering from the Instituto Superior Técnico of the University of Lisbon.
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Diederick Potgieter
The Bank of England
Diederick is a risk specialist at the Prudential Regulation Authority, The Bank of England. His responsibilities include ICAAP assessments, stress testing and capital management technical reviews. He holds a Ph.D. in Mathematical Statistics and his specialities include credit risk, operational risk, concentration risk, stress testing and economic capital frameworks. Before joining the FSA/PRA in 2011 he was Director of Capital Modelling at Barclays bank.
Giles Spungin
HSBC
Giles Spungin is responsible for the development and imbedding of advanced analytics strategy for Operational Risk and Regulatory Compliance functions globally for HSBC. Activity examples include bank-wide predictive risk management solutions, automation and behavioural analytics, surveillance analytics, and capital modelling. Giles chairs the Model Risk Oversight Committee. Prior to joining HSBC, Giles worked at a number of leading financial institutions in highly quantitative roles, including Goldman Sachs and Deutsche Bank. Giles has an algorithmic trading development background.
Dr. Daniel Meier
Swiss Re
Daniel Meier has been a senior risk modeler at Swiss Re since 2008, mainly focusing on life and health risks. He has led the core development of a major project to overhaul Swiss Re's internal risk model. He holds a diploma/M.Sc. in mathematics and a Ph.D. in computer science, both from University of Konstanz, Germany. Daniel is a Swiss
Dr. Alexander Diethert
MathWorks
Dr. Alexander Diethert is a senior application engineer at MathWorks in Munich, Germany. He and his team focus on data analytics and computational finance, serving academic and commercial customers across Europe. Prior to joining MathWorks, Alexander worked as a consultant in the financial services area. Alexander holds a diploma in mathematics and a Ph.D. in physics.
Kevin Shea
MathWorks
Kevin Shea is a principal software engineer and manager for the Computational Finance Development team responsible for the development of financial instruments modeling and analysis functionality in MATLAB. He was previously a consultant at MathWorks where he worked primarily with customers in the financial services industry. He is a CFA®
Stuart Kozola
MathWorks
Stuart Kozola leads product management for Computational Finance and FinTech at MathWorks. He has over 15 years of experience in data analytics, quantitative finance and risk management, simulation, and designing and implementing large-scale computational system. Stuart is a sustaining member of PRMIA and holds the FRM designation from GARP.
Paul Peeling
MathWorks
Paul Peeling is a senior technical consultant at MathWorks, specialising in data analytics and signal processing. He works with customers to apply machine learning and data mining techniques to solve engineering problems; use robust software development techniques for writing code and graphical user interfaces; and optimise, verify, and deploy signal processing algorithms on embedded platforms. Prior to joining MathWorks in 2011, Paul worked at Featurespace, applying pattern recognition techniques to detect and combat online fraud. Paul has a Ph.D. in statistical signal processing from the University of Cambridge.
Rory Adams
MathWorks
Rory Adams is a senior consultant engineer specializing in data analysis, software development, and application deployment. He works with customers to understand and resolve their technical and business challenges with a focus on mathematical modeling, application development, parallel computing, and physical modeling. Rory holds a Ph.D. in theoretical physics and an M.Sc. in applied mathematics from the University of Cape Town, South Africa.
Sylvain Lacaze
MathWorks
Sylvain Lacaze is a consultant engineer who specializes in design optimization, reliability analysis, application deployment, and adaptive machine learning techniques. He works with customers to build predictive models using machine learning; deploy MATLAB algorithms as part of web, database, desktop, and enterprise applications; and access and analyze big data in MATLAB. He holds a B.Eng. from the French Institute of Advanced Mechanics, a master’s degree in mechanical engineering from Blaise Pascal University, and a Ph.D. in aerospace and mechanical engineering with a minor in statistics from the University of Arizona.
Dr. Gianluca Fusai
Cass Business School
Gianluca Fusai is a reader in mathematical finance. He holds a Ph.D. in finance from Warwick Business School, an M.Sc. in statistics and operational research from the University of Essex, and a B.Sc. in economics from Bocconi University. His research interests focus on financial engineering, numerical methods for finance, portfolio selection, and energy markets. He has published extensively on these topics in Mathematical Finance, Finance and Stochastics, Quantitative Finance, Journal of Banking and Finance, Journal of Computational Finance, Risk, Annals of Applied Probability, and the International Journal of Theoretical and Applied Finance. Gianluca has co-authored the textbook ‘Implementing Models in Quantitative Finance’ (Springer Finance) and has worked as a consultant in the public and private sectors. Gianluca also holds a position in financial mathematics at the Università del Piemonte Orientale.
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