Congratulations to the following three members of the SIAM community who received awards at the virtual SIAM Conference on Financial Mathematics and Engineering (FM21) which took place took place June 1-4, 2021. Additional information about each recipient, including Q&As, can be found below.
Jana Hlavinová
Jana Hlavinová, of Vienna University of Economics and Business, is one the 2021 recipients of the SIAM Activity Group on Financial Mathematics and Engineering Conference Paper Prize. The prize was awarded at the SIAM Conference on Financial Mathematics and Engineering (FM21) on June 4, 2021 where finalists presented their papers for the consideration of the prize selection committee. The committee selected two recipients after the session. The prize was awarded to Hlavinová for her paper titled, “Forecast evaluation of quantiles, prediction intervals, and other set-valued functionals.”
The SIAM Activity Group on Financial Mathematics and Engineering awards this prize every two years to recognize outstanding research presented by students and postdocs at the SIAM Conference on Financial Mathematics and Engineering. Up to six finalists are selected to present their work and up to two awards are made at the conference. Each award has equal merit.
Hlavinová received her bachelor’s degree in applied mathematics from the Comenius University in Bratislava, Slovakia, and continued to obtain a master’s degree in quantitative finance from the Vienna University of Economics and Business in Austria. After graduating from the master’s program, she has stayed in Vienna and is currently working as a Ph.D. student at the same university under the supervision of Professor Birgit Rudloff. Her research is, on the one hand, concerned with how to quantitatively measure systemic risk. On the other hand, she is interested in how to statistically assess the quality of such risk measure estimates. What makes her research particularly intriguing is the fact that the risk measures in question are set-valued rather than just scalars.
Q: Why are you excited to receive the award of the SIAG/FME Conference Paper Prize?
A: Receiving the SIAG/FME Conference Paper Prize means a lot to me as it is a great recognition of the work me and my coauthors have done. The prize also enhances the visibility of our paper in the scientific community, which facilitates to disseminate its new structural insights into the field of forecast performance evaluation.
Q: Could you tell us a bit about the research that won you the prize?
A: The awarded paper revolves around the statistical properties of elicitability and identifiability for set-valued functionals. In financial mathematics, set-valued risk measures, in particular measures of systemic risk, have gained importance recently, and there are examples of naturally occurring set-valued functionals in many other areas, such as spatial statistics. In order to manage risks and be prepared for various situations that might come up in the future, statistical methods are used to forecast the values of functionals. An important part of forecasting is the possibility to evaluate the quality of forecasts – to check whether single forecasts are in line with the observations, or to compare and rank different forecasts (this is known as backtesting in finance). The properties of identifiability and elicitability render these tasks possible in a straightforward way. However, they have not been studied extensively for set-valued functionals so far. We define notions of elicitability and identifiability that are appropriate for dealing with set-valued forecasts and we obtain several structural insights about these properties. We furthermore illustrate their usability considering several examples of set-valued functionals, notably Vorob’ev quantiles – the generalization of the concept of a quantile for random sets – and prediction intervals. Moreover, the deduced results are applied to systemic risk measures in a separate paper (published in Finance and Stochastics).
Q: What does your work mean to the public?
A: It sheds light on the ability of assessing the quality of forecasts of set-valued form. Next to the set-valued measures of systemic risk that were our motivation to start this project, our concepts find application in many other areas. They allow for a straight-forward estimation or forecast validation for instance in medicine (tumorous tissue), hydrology (area affected by a flood), or engineering (parts of a system affected by excessive heat), to name just a few examples. In many of these situations, there are two types of error: e.g. misclassifying healthy tissue as tumorous, and the other way round. These two types of errors should in different situations be weighted differently which is also taken into account by our concepts.
Q: What does being a member of SIAM mean to you?
A: The SIAM community is one with broad research interests, and the conferences of the SIAM Activity Group on Financial Mathematics and Engineering, offer a great opportunity to meet and discuss with researchers from various areas that are in one way or the other connected to my research interests.
Gabriela Kováčová
Gabriela Kováčová, of Vienna University of Economics and Business, is one the 2021 recipients of the SIAM Activity Group on Financial Mathematics and Engineering Conference Paper Prize. The prize was awarded at the SIAM Conference on Financial Mathematics and Engineering (FM21) on June 4, 2021 where finalists presented their papers for the consideration of the prize selection committee. The committee selected two recipients after the session. The prize was awarded to Kováčová for her paper titled, “Acceptability Maximization.”
The SIAM Activity Group on Financial Mathematics and Engineering awards this prize every two years to recognize outstanding research presented by students and postdocs at the SIAM Conference on Financial Mathematics and Engineering. Up to six finalists are selected to present their work and up to two awards are made at the conference. Each award has equal merit.
Kováčová finished a bachelor’s in mathematics for Economics and Finance at the Comenius University in Bratislava and a master’s in quantitative finance at the Vienna University of Economics and Business. Afterwards, she stayed in Vienna to continue with the Ph.D. studies at the Institute for Statistics and Mathematics of the Vienna University of Economics and Business under the supervision of Professor Birgit Rudloff. Her research interests lie between financial mathematics and vector optimization, in particular in developing a dynamic programming principle for vector optimization problems like the mean-risk portfolio optimization problem, as well as in developing algorithms to implement this set-valued Bellman principle.
Q: Why are you excited to receive the award of the SIAG/FME Conference Paper Prize?
A: I am very honored to be awarded the SIAG/FME Conference Paper Prize. I would also like to thank the organizers for the amazing conference.
Q: Could you tell us a bit about the research that won you the prize?
A: We study an optimal investment problem, where the objective is to maximize the performance measured by a coherent acceptability index – an acceptability maximization problem. Acceptability is known to be connected to risk. We utilize this to propose an algorithm for solving the problem in a static setting. We also consider the problem in the dynamic setting, where the question of time consistency becomes important. Applying the connection between acceptability and risk again, we uncover an interesting dichotomy: if both the objective and the market have certain recursiveness properties, then the problem reduces to a static one and the maximal acceptability turns out to be constant. On the other hand, we study some special cases that do not have this recursiveness properties, but where the objective is a ratio (typically some sort of gain over risk) and the related bi-objective gain-risk problem satisfies the set-valued Bellman principle developed for the mean-risk problem in another paper of ours published in Operations Research. Then, one can solve the time inconsistent acceptability maximization problem by solving the (in the set-valued sense) time consistent related bi-objective gain-risk problem and use the backward recursion of its set-valued value function.
Q: What does your work mean to the public?
A: Our work adds to the rich literature on optimal portfolio selection and optimization of performance. While the static case is now relatively well understood, the acceptability maximization problem in a dynamic setup appears to be an interesting research area, with many open problems, primarily due to the time-inconsistent nature of such problems. The results show that the newly developed set-valued Bellman principle can help to solve some scalar time inconsistent problems that are closely connected to time consistent multivariate problems. I hope that our work will contribute to our understanding of the issues of time-(in)consistency.
Q: What does being a member of SIAM mean to you?
A: SIAM organizes excellent conferences. I hope we will soon be able to attend them in person again.
Ariel Neufeld
Ariel Neufeld, of Nanyang Technological University, is the 2021 recipient of the SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize. The prize was presented at the SIAM Conference on Financial Mathematics and Engineering (FM21) on June 2, 2021. The prize was awarded to Neufeld for outstanding contributions to utility maximization and hedging under model uncertainty and to modern numerical methods for finance and insurance.
The SIAM Activity Group on Financial Mathematics and Engineering awards this prize at the SIAM Conference on Financial Mathematics and Engineering to one individual in their early career for distinguished contributions to mathematical modeling in finance in the three calendar years preceding the award year.
Ariel Neufeld obtained his Ph.D. in mathematics from ETH Zurich under the supervision of Professor Martin Schweizer of ETH Zurich and Professor Marcel Nutz of Columbia University, spending half of the time of his Ph.D. at Columbia University in New York. After his Ph.D., he returned to ETH Zurich working as a postdoc in financial mathematics, insurance mathematics, and financial engineering involving machine learning techniques. In 2019, he joined the Nanyang Technological University (NTU) in Singapore, having been awarded as Nanyang Assistant Professor in mathematics.
Q: Why you are excited to receive the award of the SIAG/FME Early Career Prize.
A: I am deeply pleased and honored to receive the SIAG/FME Early Career Prize. This award recognizes the work of my whole research group here at NTU Singapore. I am very thankful for having such a great team here which enabled me to achieve a lot of results over the past two years since I joined NTU. I am also very thankful for having been able to work and learn a lot from all my collaborators over the past years. I also would like to thank everyone who supported me in my career, it means a lot to me!
Q: Could you tell us a bit about the research that won you the prize?
A: The selection committee awarded me with the SIAG/FME Early Career Prize “for outstanding contributions to utility maximization and hedging under model uncertainty and to modern numerical methods for finance and insurance.” Under model uncertainty, one understands the concept that the real-world measure of the financial market under consideration is not known to a financial agent or institution who has to price financial derivatives or has to optimize portfolios. There are two approaches to tackle this problem. One is to consider a set of probability measures, where each element of this set represents a possible candidate (“possible scenario”) for the real-world measure, and then to price and hedge or to optimize portfolios under the worst-case scenario. The other approach is to price and hedge model-free, i.e. without making any probabilistic assumptions on the underlying market.
During my Ph.D. and Postdoc, I was focusing on identifying optimal portfolios as well as to price financial derivatives both under the worst-case scenario as well as model-free.
In the last years I have been interested in developing numerical methods which allow to price and hedge financial derivatives purely based on financial data, i.e. only on information available from the market, without imposing any probabilistic structures on it. Using, for example, cutting plane methods from optimization or machine learning methods involving neural networks allow to build algorithms which can efficiently price high dimensional financial derivatives purely based on data available from the market.
Q: What does your work mean to the public?
A: I think it is important for us as financial mathematicians to develop both theoretical results as well as practical algorithms which help the financial and insurance industry to price and hedge financial derivatives, to optimize portfolios, and to reduce risk.
The goal of my research is to develop algorithms, together with their theoretical justification, which allow a financial agent or institution to efficiently price and hedge financial derivatives and to optimize portfolios purely based on data which is observable, without imposing any probabilistic model assumptions. This reduces the risk of wrongly price financial derivatives or to wrongly allocate portfolios caused by wrong model assumptions on the financial market.
Q: What does being a member of SIAM mean to you?
A: It is great for the mathematical community to have an organization like SIAM which is dedicated to the development of mathematics and their applications.
The regular conferences and meetings allow researchers from all over the world and to share their ideas, which helps to develop new results in the future.
I appreciate particularly the generous support SIAM is offering to young researchers by for example offering Travel Awards to attend the conferences organized by SIAM. This helps the young researchers find their place within the community and to build connections with established researchers within their research field.