Seminar on Financial and Actuarial Mathematics

The seminar is organized by Prof. Biagini, Prof. Czado, Prof. Klüppelberg, Prof.  Meyer-Brandis, Prof. Scherer, Prof. Svindland and Prof. Zagst.  The venue of the seminar changes on a regular basis between the  TUM (Garching, Business Campus, Parkring 11) and the Mathematical Institute of the LMU (München, Theresienstraße 39).

Currently the Seminar takes place at the LMU München, Theresienstraße 39-B, Room 349  (on Mondays, 14:15 to 17:00)

The dates of the Graduate Seminar in Financial and Actuarial Mathematics (SoSe 2019) are:

  • May 13, 2019
  • June 24, 2019
  • July 15, 2019

Upcoming talks

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Previous talks

15.07.2019 14:15 Matteo Burzoni, ETH Zürich: Risk measures and benchmark distributions

Risk measures and benchmark distributions Abstract In this talk we discuss how the commonly used risk measures fail to control the probability of exceeding given level of losses. In order to address this aspect of tail risk we propose new classes of law invariant risk measures that generalize Value at Risk and Expected Shortfall. The key ingredient is a benchmark function which allows to specify admissibility constraints based on the whole tail of the distribution. As an interesting particular case, stochastic dominance constraints can be used in order to define acceptability. We present the main finance theoretical and statistical properties of this new class of risk measures and for the convex case we show its dual representation. Merits and drawbacks are discussed and applications to capital adequacy, portfolio risk management and catastrophic risk are presented. Based on joint works with Cosimo Munari, Valeria Bignozzi and Ruodu Wang.
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15.07.2019 15:00 Lukas Gonon, University of St. Gallen: Random neural networks, reservoir computing and hedging by deep learning techniques

This talk presents our recent work on hedging derivatives under market frictions by deep learning techniques and approximation error bounds for random recurrent neural networks. We first consider the problem of optimally hedging a portfolio of derivatives in a scenario based discrete-time market with transaction costs. Risk-preferences are specified in terms of a convex risk-measure. Such a framework has suffered from numerical intractability up until recently, but this has changed thanks to technological advances: using hedging strategies built from neural networks and machine learning optimization techniques, optimal hedging strategies can be approximated efficiently. In order to optimize the choice of the network architecture we then study approximation properties of reservoir computing systems, putting particular emphasis on random recurrent neural networks. We prove that only a small proportion of the parameters need to be trained and obtain high-probability bounds on the generalization error in terms of the network parameters. The talk is based on joint work with Hans Bühler, Josef Teichmann and Ben Wood as well as joint work in progress with Lyudmila Grigoryeva and Juan-Pablo Ortega.
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13.05.2019 14:15 Rama Cont, Oxford University: Rough calculus: pathwise calculus for functionals of irregular paths

Rough calculus: pathwise calculus for functionals of irregular paths Abstract Hans Foellmer showed that the Ito formula holds pathwise, for functions paths with finite quadratic variation along a sequence of partitions. We build on Foellmer's insight to construct a pathwise calculus for smooth functionals of continuous paths with regularity defined in terms of the p-th variation along a sequence of time partitions for arbitrary large p >0. We construct a pathwise integral, defined as a pointwise limit of compensated Riemann sums and show that it satisfies a change of variable formula and an isometry formula. Results for functions are extended to path-dependent functionals using the concept of vertical derivative of a functional. Finally, we obtain a "signal plus noise" decomposition for regular functionals of paths with strictly increasing p-th variation. Our results apply to sample paths of semi-martingales as well as fractional Brownian motion with arbitrary Hurst parameter H>0. Based on joint work with: Anna Ananova (Oxford), Henry Chiu (Imperial College London) and Nicholas Perkowski (Humboldt). https://www.fm.mathematik.uni-muenchen.de/teaching/teaching_summer_term_2019/seminars/oberseminar_finanz_2019/cont/index.html
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13.05.2019 15:00 Russell Gerrard, CASS Business School: An optimal investment strategy for future pension plans

Since the work of Merton (1969) the construction of optimal investment portfolios in a time-homogeneous market has been well understood. We review the situation when market parameters are allowed to vary stochastically and apply this to the problem of constructing a pension investment scheme which provides a guaranteed minimum sum on retirement at the expense of imposing an upper limit. We limit ourselves to the accumulation phase, during which payments are made into the fund. The aim of the overall project is to devise pension investment strategies whose behaviour is close to optimal but which present investors with easily explained choices.
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13.05.2019 16:00 Alessandra Cretarola: Indifference Pricing of Pure Endowments via BSDEs under Partial Information

We investigate the pricing problem of a pure endowment contract when the insurer has a limited information on the mortality intensity of the policyholder. The payoff of this kind of policies depends on the residual life time of the insured as well as the trend of a portfolio traded in the financial market, where investments in a riskless asset, a risky asset and a longevity bond are allowed. We propose a modeling framework that takes into account mutual dependence between the financial and the insurance markets via an observable stochastic process, which affects the risky asset and the mortality index dynamics. Since the market is incomplete due to the presence of basis risk, in alternative to arbitrage pricing we use expected utility maximization under exponential preferences as evaluation approach, which leads to the so-called indifference price. Under partial information this methodology requires filtering techniques that can reduce the original control problem to an equivalent problem in complete information. Using stochastic dynamics techniques, we characterize the indifference price of the insurance derivative via the solutions of suitable backward stochastic differential equations.
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10.05.2019 10:00 Ingo Kraus, Damir Filipovic, Antoon Pelsser, Ralf Werner, Oleksandr Khomenko: Workshop on Replication in Life Insurance/ In cooperation with ERGO Group

Dear colleagues, We are pleased to announce the workshop on “Replication in Life Insurance” hosted by the ERGO Center of Excellence in Insurance at the Chair of Mathematical Finance, Technical University of Munich that will be held on May 10, 2019 in Munich. Our objective is to bring together a small number of practitioners and academics working in the field of life insurance liability replication to create a platform for discussions on improvements of current practice and theory. For organisational purposes, we kindly request interested attendants to confirm their participation by sending an email to the address sekm13@tum.de.
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28.01.2019 14:15 Pierre Devolder: OPTIMAL RISK SHARING IN PUBLIC PENSION SCHEMES

Traditionally, public pension schemes, organized in a social security framework, use a pay as you go technique (PAYG); from the benefit point of view, they are based on a Defined Benefit (DB) or a Defined Contribution (DC) approach. This dichotomy follows two extreme philosophies of risk spreading between the stakeholders: in DB, the organizer of the plan bears the risks; in DC (including the Notional accounts – NDC), the affiliates must bear the risks. Especially applied to social security, this traditional polar view can lead to unfair intergenerational equilibrium in both cases. The purpose of this presentation is to propose, in PAYG, alternative hybrid architectures based on a mix between DB and DC, in order to achieve simultaneously financial sustainability and social adequacy in a stochastic environment. Using different stochastic models for the risk factors, we propose different levels of optimality in terms of architecture of the pension scheme.
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28.01.2019 15:00 Axel Bücher: Extreme Value Analysis of Multivariate Time Series: Multiple Block sizes and Overlapping Blocks

The core of the classical block maxima method in (multivariate) extreme value statistics consists of fitting an extreme value distribution to a sample of maxima over blocks extracted from an underlying time series. Traditionally, the maxima are taken over disjoint blocks of observations of a fixed size. Alternatively, the blocks can be chosen to be of varying size and to slide through the observation period, yielding a larger number of overlapping blocks. Nonparametric estimation of extreme value copulas based on sliding blocks is found to be more efficient than estimation based on disjoint blocks.
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28.01.2019 16:00 Jean-David Fermanian: On Kendall's regression

Conditional Kendall's tau is a measure of dependence between two random variables, conditionally on some covariates. We assume a regression-type relationship between conditional Kendall's tau and some covariates, in a parametric setting with a large number of transformations of a small number of regressors. This model may be sparse, and the underlying parameter is estimated through a penalized criterion. We prove non-asymptotic bounds with explicit constants that hold with high probabilities. We derive the consistency of a two-step estimator, its asymptotic law and some oracle properties. We show how the problem of estimating conditional Kendall's tau can be rewritten as a classification task. We detail specific algorithms adapting usual machine learning techniques, including nearest neighbors, decision trees, random forests and neural networks, to the setting of the estimation of conditional Kendall's tau. Finite sample properties of these estimators and their sensitivities to each component of the data-generating process are assessed in a simulation study. Finally, we apply all these estimators to a dataset of European stock indices.
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03.12.2018 14:15 Miguel de Carvalho: Nonstationary Joint Extremes

In this talk, I will discuss key ideas on time-changing extremal dependence structures. Extremal dependence between international stock markets is of particular interest in today’s global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, or other suitable covariate. Working within a framework of asymptotic dependence, we introduce a regression model for the angular density of a bivariate extreme value distribution that allows us to assess how extremal dependence evolves over a covariate. We apply the proposed model to assess the dynamics governing extremal dependence of some leading European stock markets over the last three decades, and find evidence of an increase in extremal dependence over recent years.
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03.12.2018 15:00 Matti Kiiski : Pathwise Pricing-Hedging Duality

We discuss weak topologies on the Skorokhod space of cadlag functions. In particular, we study the weak* topology they induce on the family of probability measures on the canonical space and give applications to the pathwise pricing-hedging duality. We also discuss related open problems.
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03.12.2018 16:00 Miriam Isabel Seifert: Financial risk measures for a network of individual agents holding portfolios of light-tailed objects

In this talk we consider a financial network of agents holding portfolios of independent light-tailed risky objects with losses assumed to be asymptotically exponentially distributed with distinct tail parameters. The derived asymptotic distributions of portfolio losses refer to the class of functional exponential mixtures. We also provide statements for Value-at-Risk and Expected Shortfall measures as well as for their conditional counterparts. We establish important qualitative differences in the asymptotic behavior of portfolio risks under light tail assumption compared to heavy tail settings which should be accounted for in practical risk management. (joint work with Claudia Klüppelberg)
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05.11.2018 14:15 Thorsten Rheinländer : On the stochastic heat equation with mutiplicative noise

We study a parsimonious but non-trivial model of the latent limit order book where orders get placed with a fixed displacement from a center price process, i.e. some process in-between best bid and best ask, and get executed whenever this center price reaches their level. This mechanism corresponds to the fundamental solution of the stochastic heat equation with multiplicative noise for the relative order volume distribution, for which we provide a solution via a local time functional. Moreover, we classify various types of trades, and introduce the trading excursion process which is a Poisson point process. This allows to derive the Laplace transforms of the times to various trading events under the corresponding intensity measure.
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05.11.2018 15:00 Gonçalo dos Reis: Large Deviations for McKean Vlasov Equations and Importance Sampling

We discuss two Freidlin-Wentzell large deviation principles for McKean-Vlasov equations (MV-SDEs) in certain path space topologies. The equations have a drift of polynomial growth and an existence/uniqueness result is provided. We apply the Monte-Carlo methods for evaluating expectations of functionals of solutions to MV-SDE with drifts of super-linear growth. We assume that the MV-SDE is approximated in the standard manner by means of an interacting particle system and propose two importance sampling (IS) techniques to reduce the variance of the resulting Monte Carlo estimator. In the "complete measure change" approach, the IS measure change is applied simultaneously in the coefficients and in the expectation to be evaluated. In the "decoupling" approach we first estimate the law of the solution in a first set of simulations without measure change and then perform a second set of simulations under the importance sampling measure using the approximate solution law computed in the first step.
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