Talks in Financial and Insurance Mathematics

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This is the regular weekly research seminar on Insurance Mathematics and Stochastic Finance.

Spring Semester 2017

Note: The highlighted event marks the next occurring event.

Date / Time Speaker Title Location
23 February 2017
Mathias Beiglböck 
TU Wien
Brenier-type results in Martingale Optimal Transport  HG G 43 
Abstract: A seminal result in optimal transport is Brenier's theorem on the structure of the optimal plan for squared distance costs. We briefly review related results on the martingale version of the transport problem and connections with robust finance and the Skorokhod embedding problem. We then introduce a continuous time Brenier-type theorem for the martingale transport problem which exhibits a particularly simple functional form. Finally, we explain a link of this result with the local vol model.
2 March 2017
Anthony Réveillac 
INSA de Toulouse
A Black-Scholes type formula for the pricing of some reinsurance contract  HG G 43 
Abstract: In this talk we will derive, using the Malliavin calculus, a new formula which can be thought as a counterpart for some reinsurance contracts of the celebrated Black-Scholes formula in Finance. Our approach allows one for instance to consider claims that may depend on the intensity of the underlying counting process defining the risk process. This constitutes a joint work with Caroline Hillairet (ENSAE - Paris) and Ying Jiao (ISFA - Lyon).
9 March 2017
Juan-Pablo Ortega 
Universität Sankt Gallen
Title T.B.A. HG G 43 
23 March 2017
Walter Schachermayer 
University of Vienna
Title T.B.A. HG G 43 
30 March 2017
Fred Espen Benth 
University of Oslo
Title T.B.A. HG G 43 
6 April 2017
Lisa R. Goldberg 
Identifying Financial Risk Factor with Sparse and Low-Rank Decompositions  HG G 43 
Abstract: We show how to use sparse and low-rank (SLR) matrix decompositions based on convex optimization to extract financial risk factors from a sample return covariance matrix. We provide an example that highlights the difference between this approach and the academic standard for financial factor identification, principal component analysis (PCA), which makes systematic errors. Using finance-oriented metrics, we analyze the accuracy of SLR and PCA on equally weighted portfolios and minimum variance portfolios in a simulated global equity market. Finally, we discuss non-convex programming formulations that show promise in identifying numerous sparse factors (industries, counties, etc) at various scales. A preprint that gives some background on what we’re up to is linked here: and more information can be found ion this page:
20 April 2017
Tom Hurd 
McMaster University
Title T.B.A. HG G 43 
27 April 2017
Scott Robertson 
Boston University
Title T.B.A. HG G 43 
4 May 2017
Ari-Pekka Perkkiö 
Title T.B.A. HG G 43 
11 May 2017
Chris Rogers 
University of Cambridge
Title T.B.A. HG G 43 
18 May 2017
Felix Kübler 
University of Zürich
Title T.B.A. HG G 43 
25 May 2017
Ascension day
1 June 2017
Hanspeter Schmidli 
University of Cologne
Title T.B.A. HG G 43 

Archive: SS 17  AS 16  SS 16  AS 15  SS 15  AS 14  SS 14  AS 13  SS 13  AS 12  SS 12  AS 11  SS 11  AS 10  SS 10  AS 09 

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