Newsletter of the Bachelier Finance Society
Volume 12, Number 4, October 2020
The aim of these postings is to create a forum for the dissemination of information on academic and industrial positions related to mathematical finance, across different disciplines and different geographical regions. Please submit any job advertisements you are aware of to firstname.lastname@example.org, preferably in plain text and sending the link to the website containing all the information. Updates and new items appear continuously at: bachelierfinance.org/forum/jobs
Deadline: early application encouraged
University of Padova
Deadline: October 14, 2020
Deadline: October 20, 2020
Southern University of Science and Technology, China
Deadline: October 25, 2020
Carnegie Mellon University
Deadline: November 15, 2020
Carnegie Mellon University
Deadline: December 31, 2020
Associate Editor/Editor (m/f/d) Mathematics
Call for Submissions – Nicola Bruti Liberati Prize
Theses defended in 2019 and 2020 must be submitted no later than February 1, 2021.
Call for Submissions – SIAM Conference on Financial Mathematics and Engineering (FM21)
Discussion website – Referendarius.com
Discounts on various publications by World Scientific Publishing
Interview with Claudia Küppelberg
Would you like to tell us about your youth? How did you develop your interest in mathematics?
As a late student who got her A-levels at evening school, I was about 5 years older than my fellow students when I took the decision to study mathematics with a minor in business administration. As mathematics was my easiest topic at school I chose to study it in this naive way many young people chose their topic at that time; I started in 1976. As one of very few female students aiming at a diploma (there were more female students wanting to become a teacher) it helped that at least one of the tutors in my first two years was female. Of course, I did not meet any female professors during my whole studies.
Where did you get your PhD from and on what topic?
From the University of Mannheim with a thesis on “subexponential distributions”, a class of heavy-tailed distributions, which play a role in ruin theory (as claim size distributions), also in branching processes (as lifetime distributions), and in queueing theory (as service time distributions).
What were the next steps in your academic career?
I started as a postdoc at the University of Mannheim, and continued at ETH Zürich working with Paul Embrechts there. These were interesting times, Mathematical Finance was emerging as a topic of Mathematics. Experiencing this in an environment like ETH was not only exciting, but also highly motivating.
In the course of your career you have worked on different topics such as Extreme Value Theory, Insurance and Mathematical Finance, Risk Management and Econometrics. Looking back in perspective, what is the common thread among your diverse research interests?
The common mathematical and statistical thread is the tail behaviour of distributions and understanding extremal events in dependent dynamic models as in stochastic processes and, in particular, statistical time series (in discrete and continuous time), then in multi-parameter and space-time models. More recently I have been investigating extreme dependence in networks.
The common applications theme is risk modelling, measuring, and estimation. I find it extremely interesting how risk affects all aspects of life. As a research topic it leads you into such diverse areas as Insurance and Finance, Environmental Science and Climate Change, Engineering, …, and Pandemics.
What are the research topics that you found most fascinating?
I always find most fascinating the topic I am working on. This is at the moment graphical models and causality. In the end you want to know the culprit; i.e., cause and effect. Sometimes this is obvious, but not always. And to have objective statistical tools available is essential for instance for regulators or when reporting to your boss or the public. Models of directed graphs identify cause and effect of risk. Most graphical models to date rely on linear multivariate normal distributions, and we were the first to propose a max-linear graphical model, which is in combination with multivariate extreme value distributions an appropriate causal risk model.
What are the key differences between insurance mathematics and financial mathematics? What do they have in common?
This question would call for a substantial review paper to give a complete answer. My impression is that in certain aspects the two areas have converged. Let me give two examples. On the one hand, Finance started with the Black-Scholes model, i.e. a normal model, whereas Insurance always used heavy-tailed Pareto models for catastrophic insurance. With the econometric GARCH models, based on detailed data analyses of financial data, Finance acknowledged the heavy tails in their data. Consequently, tail risk measures based on Pareto tails (or more general, regularly varying tails) like Value-at-Risk and Expected Shortfall are now used in both areas. On the other hand, considering the dependence structures in both worlds are different. Contagion exists in both worlds, but in Finance it is rather endogenous (contagion is systemic), whereas in Insurance it is exogenous (contagion originates in an exogenous insurance event, which affects several companies). Consequently, network models for contagion modelling are different in both areas.
In which directions do you see mathematical finance evolving in the future?
My own research always focused on risk management in many areas including Finance. Hence, I may not be the person with the grand overview of the whole field of mathematical finance. Risk management has reacted to the financial crisis in 2007/8 by changing from risk research for single institutions to risk research for a banking network. Statistical data becomes more and more available, and the advancement of the field of data science will certainly contribute immensely to the understanding of financial network mechanisms in the world wide banking network.
Amongst other topics, you are currently working on Bayesian Networks. Can you sketch the main ideas and applications?
Our new Bayesian Network model is motivated by extreme value theory and aims at risk assessment in a network. It is a max-linear model on a directed acyclic graph, hence modelling cause and effect of risk. I had to learn a lot of new mathematics like graph theory, tropical algebra, and conditional independence in graphical models. This was not easy but great fun, and we got some help from experts in these fields. Applications are vast including also environmental high risk applications. At the moment we develop an algorithm to fit the Danube river network data. The main issue is to get the edge directions for the full network correct, and we just had a breakthrough in getting all but two edge directions correct. These data provide an excellent test case as directions are given by the water flow. But getting the network right by statistical methods means that we can also apply our algorithm to perhaps not so clear risk data to find cause and effect in high risk.
Which other topics are you currently working on?
Some years ago we started a project using a bipartite graph to assess risk in an insurance market, where – in contrast to the endogenous credit risk in the financial network – risks are exogenous events, and portfolio risks may be distributed among agents, in this application insurance or reinsurance companies. In our first publication we computed first order asymptotic risk measures for single agents in such a market or of groups of agents, which provide an assessment of market risk. We have continued this work in several directions, assessing higher order dependence structures beyond first order asymptotic independence. We also introduced a dynamic structure into the risky objects.
Which topics do you consider most important for future research?
High-dimensional problems have arrived at our doorstep, simply because of the massive amount of data available and have to be investigated in probability and mathematical statistics. Data science will develop further, in particular the algorithmic side. Networks will provide an effective modelling tool. To me it is important that mathematicians and statisticians help to gain a deeper understanding in using and developing mathematical tools and algorithms.
You have supervised a large number of PhD students who have successfully pursued careers in academia and in the industry. What advice would you give to young mathematicians wishing to pursue a career in mathematics?
Well, I have always profited from the interplay of mathematical methods based on pure and applied math. So I have encouraged all my students to take advanced analysis courses like functional analysis and PDE besides courses like Stochastic Analysis, Time Series Analysis, Multivariate Statistics, etc. I have always believed that it is typical for a mathematician to understand an applied problem in a structured way. Moreover, when you work on some applied problem, then you need a well-filled toolbox, and you need the skills to find the right tool for your problem at hand.
What I find also important is to connect to other (young) scientists and get an overview of the relevant research in and around your specific research project. This can be achieved by sharing and testing your research ideas, attending seminars and conferences where you present your work. Build your own network! Some of these young research colleagues will accompany you through your professional life, be it in industry or in the academic world.
Any particular advice you would give to young women?
Academic life is good fun, also for women. I have enjoyed almost every day. To work with young clever people is a singular privilege. And get involved! To give an example, when I was a postdoc there were plans in the Bernoulli Society for Mathematical Statistics and Probability to start a newsletter, and I became its founding editor. In this function I got to know many scientists, women and men, who became long term friends. Now I am the President of the Bernoulli Society and support our community in this office. Many female scientists, young and old, are involved in running and promoting this society. I am sure that also the Bachelier Finance Society offers many possibilities to get involved; use these chances. Women are good at this!
Do you think it is more difficult to pursue an academic career in mathematics as a woman? Has this changed over the years, and if so, can you describe how?
This is difficult to answer as there are several aspects to it. My own department has changed substantially over the past 20 years. For instance, when I started there in 1997, I was the first and only female full professor and there was one female associate professor. Now my department has 39 professors and 11 of them are female (without counting me as I am retired). The Technical University of Munich and the Mathematics Department has appointed many excellent female professors and is now the Mathematics Department in Germany with the most female professors.
On the other hand, when I am on a Search Committee for some position or on the Scientific Board of some conference selecting invited speakers, then it is still often my role to propose female colleagues. Hence, it is important to guarantee female colleagues on every board.
Would you like to tell us your opinion / the pros and cons of interactions with companies and regulators?
My experiences have been very mixed as presumably everybody’s are who interacts with companies and regulators. After my appointment at the Technical University of Munich, my department started a Diploma in Financial and Economic Mathematics with great success. And of course as Munich has headquarters of such important insurance companies like Munich Re and Allianz, and at that time also the HypoVereinsbank was an important German bank, I contacted them hoping for some fruitful collaboration. The HypoVereinsbank was captivated by the new Diploma we had introduced and decided to support a Chair for Financial Mathematics substantially for 10 years. Insurance companies supported some Master Theses, also gave some modest stipends for PhD students, but most importantly, some of their employees lectured actuarial courses to our diploma and later master students. Those lectures are credited by the German Association of Actuaries, hence important for students pursuing such a career.
Do you think there will be new research directions in insurance math, financial math, econometrics, etc. due to SARS-CoV-2?
Of course, the large amount of data collected worldwide throughout the SARS-CoV-2 crisis, will be analysed from every possible aspect. However, new research directions will presumably rather emerge through the advancement of statistics in data science for all kinds of data including SARS-CoV-2 data. They are of course most important when setting up pandemic models in the insurance business.
The interview was conducted by Natalie Packham (Berlin School of Economics and Law) and Antonis Papapantoleon (National Technical University of Athens).
BOOKS & JOURNALS
The Society maintains a list of books, book reviews and journals at: https://www.bachelierfinance.org/publications. Members that would like to have their books added to the website, should please let us know.
Recently published books
Tomasz R. Bielecki, Jacek Jakubowski, Mariusz Niewȩgłowski
Structured Dependence between Stochastic Processes
Cambridge University Press (2020), ISBN 9781107154254
Wolfgang Doeblin (author); Marc Yor, Bernard Bru (Eds.)
Œuvres Complètes—Collected Works
Springer (2020), ISBN 978-3-319-41880-3
Une équation jamais n’abolira le hasard
Interview in French with Nicole el Karoui (professor emerita at University Pierre-et-Marie-Curie, Paris) on Radio France (France Culture)
This list contains conferences related to mathematical finance that take place in the next several months. A full list is available at https://www.bachelierfinance.org/conferences and also on https://www.bachelierfinance.org/webinars.
Please let us know of conferences we are not aware of and include a URL for the event.
Many conferences are cancelled around the world due to the corona-virus pandemic. Please check the respective webpages for further information.
The 3rd Asian Quantitative Finance Seminar (AQFS)
October 17, 2020
Swissquote Conference 2020 on Finance and Technology
October 30, 2020
Quant Insights Conference
November 12–13, 2020
10th General AMaMeF Conference
June 22–25, 2021
6th Symposium on Quantitative Finance and Risk Analysis (QFRA 2021)
June 23–25, 2021
Crete Island, Greece
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