Faculdade de Ciências da Universidade do Porto 1st Oporto Meeting on Mathematics for Industry  
  1st Oporto Meeting on Mathematics for Industry
4th Porto Meeting on
7th to 9th June 2012

Main Speakers and courses

  • Ethem Alpaydim, Department of Computer Engineering Bogaziçi University, Turkey  >>> +info
Title: Combining Features, Kernels and Algorithms
Abstract: Recently various methods have been proposed to combine multiple learners to improve accuracy. Combining learners is useful only when the learners are complementary and to achieve diversity, it has been
proposed to combine (1) features (from different representations/modalities/sources), (2) kernels (using different
measures of similarity), and/or (3) learning algorithms (with different inductive bias). We will discuss these different methods together with experimental results.
  • Irina Penner, Institut für Mathematik Humboldt-Universität zu Berlin, Germany  >>> +info
Title:   Convex measures of risk
Abstract:  Monetary measures of risk  quantify  minimal capital reserves, that should be maintained by financial institutions  in order to balance undertaken risks  and ensure financial stability. In this course I will focus on convex monetary risk measures. First, I will give an overview of the theory in the static case, explaining the axiomatic approach and providing the robust representation of convex risk measures. Then I will discuss risk measures in the dynamic environment, where risk assessment is updated over the time in accordance with the new information. In particular, I will characterize various time consistency aspects of dynamic risk assessment, and I will show how uncertainty about the time value of money can be taken into account. The results will be illustrated by examples. The course will be based on the book "Stochastic Finance" by H. Foellmer and A. Schied, and on joint works with Hans Foellmer and Beatrice Acciaio. 
  • José Carlos Príncipe, Computational NeuroEngineering Laboratory, University of Florida, USA >>> +info
Title: Online Learning in Data Analysis
Abstract: The old days of processing data off line are over. Most of the recent interesting applications of controls and machine learning in the manufacturing, service and entertainment industries require instant processing of streaming data, i.e. processing one sample at a time. It turns out that the field of adaptive signal processing has develop a large class of algorithms solving optimally the approximation problem for the linear and the nonlinear model using stochastic gradient approximations. This will be the focus of the course.
We will cover the following topics
1-      Filtering versus regression, and the importance of time
2-      Solving least squares with search algorithms: adaptive filtering
3-      Affine projection Algorithms
4-      Neural Networks for nonlinear regression and classification
5-      Reproducing kernel Hilbert space algorithms for regression and classification

  • Johannes Voit, German Savings Banks Association, Berlin  >>> +info
Title: Harnessing the Fat Tails - The Case of Operational Risk Management and Measurement
Abstract: One stylized fact of financial markets contents that the probability distribution functions describing market data are fat-tailed. Nevertheless, most of the risk-management instruments rely directly or indirectly on normal distributions. The loss distributions of operational incidents in banks are both heavily skewed and extremely fat-tailed. Operational risk is defined as the risk of loss due to inappropriate or failed infrastructure, processes, human resources, or due to external impact.
In my talks, I will discuss the challenges of measuring operational risk in banks, and how they were mastered in a network of 430 independent savings banks in Germany. In addition to the collection of loss information in each bank, this involves the use of scenario analysis and the operation of central data pools. I will describe in detail the development and calibration of a risk measurement tool which accurately models and aggregates the fat-tailed distributions found from almost 50,000 loss data from the pool.
Tentative outline:
Part 1:
Definition of and examples for operational risk, classification
Regulatory approaches to operational risk
The scarcity of loss data
Scenario analysis
Building a loss data pool
Scaling relations in the loss data pool
Part 2:
The loss distribution approach - a framework for quantification of operational risk
The frequency distribution
The severity distributions
The treatment of scenarios
Part 3:
The aggregate loss distributions: expectation values, value at risk, etc.
Aggregating the loss distribution of the bank
Results based on pool calculations
Open issues
Summary and outlookto be announced

Centro de Matemática da Universidade do Porto Centro de Matemática da Universidade de Coimbra Grupo de Física Matemática - Universidade de Lisboa Centro de Matemática Aplicada à Previsão e Devisão Económica Faculdade de Ciências da Universidade do Porto