Center for Advanced Studies (CAS) Research Focus Quantitative Network Science (LMU) - HDWhile the scientific disciplines in which networks occur are diverse, the needs for analysis are similar and may be dealt with by using the same or similar quantitative methods and models. These include methods and theories ranging from mathematical graph theory and statistical network models to visualization techniques in computer science. The Research Focus Quantitative Network Science intends to bundle the different activities within network science at the LMU Munich and brings together mathematicians, statisticians, and computer scientists with empirical scientists from a wide range of disciplines in order to advance the field of quantitative network science.While the scientific disciplines in which networks occur are diverse, the needs for analysis are similar and may be dealt with by using the same or similar quantitative methods and models. These include methods and theories ranging from mathematical graph theory and statistical network models to visualization techniques in computer science. The Research Focus Quantitative Network Science intends to bundle the different activities within network science at the LMU Munich and brings together mathematicians, statisticians, and computer scientists with empirical scientists from a wide range of disciplines in order to advance the field of quantitative network science.methods, network, statistics, mathematics
https://cast.itunes.uni-muenchen.de/vod/playlists/5ai6fLQf5U.html
Fri, 24 Aug 2018 11:32:17 +0000Center for Advanced Studies (CAS)Ludwig-Maximilians-Universität Münchenitunes@lmu.denoenManaging Systemic Risk in Financial Multilayer Networks
https://cast.itunes.uni-muenchen.de/clips/v2iLnuvdoI/vod/high_quality.mp4
Systemic risk in financial markets arises largely because of the interconnectedness of agents through financial contracts. We show that the systemic risk level of every agent in the system can be quantified by simple network measures. With actual central bank data for Austria and Mexico we are able to compute the expected systemic losses of an economy, a number that allows to estimate the cost of a crises. We can further show with real data that is possible to compute the systemic risk contribution of every single financial transaction to the financial system. We suggest an intelligent financial transaction tax that taxes the systemic risk contribution of all transactions. This tax provides an incentive for market participants to trade financial assets in a way that effectively restructures financial networks so that contagion events become impossible. With an agent based model we can demonstrate that this Systemic Risk Tax practically eliminates the network-component of systemic risk in a system. | Stefan Thurner ist Professor für Science of Complex Systems an der Medizinischen Universität Wien.Systemic risk in financial markets arises largely because of the interconnectedness of agents through financial contracts. We show that the systemic risk level of every agent in the system can be quantified by simple network measures. With actual central bank data for Austria and Mexico we are able to compute the expected systemic losses of an economy, a number that allows to estimate the cost of a crises. We can further show with real data that is possible to compute the systemic risk contribution of every single financial transaction to the financial system. We suggest an intelligent financial transaction tax that taxes the systemic risk contribution of all transactions. This tax provides an incentive for market participants to trade financial assets in a way that effectively restructures financial networks so that contagion events become impossible. With an agent based model we can demonstrate that this Systemic Risk Tax practically eliminates the network-component of systemic risk in a system. | Stefan Thurner ist Professor für Science of Complex Systems an der Medizinischen Universität Wien.Wed, 05 Oct 2016 19:30:00 +0000Prof. Stefan Thurner (Wien) | Moderation: Prof. Paul Thurner (LMU)What’ s New in Networks? Special Lectureno01:19:46network, bridges, computational, mathematic, statistic, analysis0https://cast.itunes.uni-muenchen.de/clips/v2iLnuvdoI/vod/high_quality.mp4How Do Neuronal Circuits Operate?
https://cast.itunes.uni-muenchen.de/clips/zF8dT4qYf4/vod/high_quality.mp4
Brains are highly interconnected networks of millions to billions of neurons. How they work and how they process and store information − these questions are addressed differently by both speakers. Alexander Borst is interested in the processing of neuronal information at the level of individual neurons or small neuronal circuits. As an example he will present the structure of the neural circuit and its key elements responsible for performing the computations of photoreceptor signals in the visual system of the fly whereas Moritz Helmstädter develops and applies methods to map neuronal networks at a larger scale. In his talk, he gives insights into the new field of connectomics, the measurement of communication maps of neuronal circuits. | Center for Advanced Studies LMU: 14.01.2016 | Speakers: Prof. Dr. Alexander Borst, Dr. Moritz Helmstädter | Moderation: Prof. Dr. Martin WirsingBrains are highly interconnected networks of millions to billions of neurons. How they work and how they process and store information − these questions are addressed differently by both speakers. Alexander Borst is interested in the processing of neuronal information at the level of individual neurons or small neuronal circuits. As an example he will present the structure of the neural circuit and its key elements responsible for performing the computations of photoreceptor signals in the visual system of the fly whereas Moritz Helmstädter develops and applies methods to map neuronal networks at a larger scale. In his talk, he gives insights into the new field of connectomics, the measurement of communication maps of neuronal circuits. | Center for Advanced Studies LMU: 14.01.2016 | Speakers: Prof. Dr. Alexander Borst, Dr. Moritz Helmstädter | Moderation: Prof. Dr. Martin WirsingThu, 14 Jan 2016 19:00:00 +0000Prof. Dr. Alexander Borst, Dr. Moritz Helmstädter | Moderation: Prof. Dr. Martin WirsingCAS Research Focus Quantitative Network Scienceno01:29:531https://cast.itunes.uni-muenchen.de/clips/zF8dT4qYf4/vod/high_quality.mp4Model Risk, Solvency, and Risk Aggregation
https://cast.itunes.uni-muenchen.de/clips/VHqkBfDxX2/vod/high_quality.mp4
Under both Basel II/III for banking as well
as Solvency 2/SST for insurance, Model
Risk (MR), especially for Risk Aggregation
purposes, plays an important role. In this
talk I will concentrate on Dependence Uncertainty and quantify MR from that point
of view. Besides reviewing some of the main results obtained over the recent years,
I will discuss several examples coming from the realm of Operational Risk, as well as the calculation of economic Capital in a real banking example. A basic reference
is A.J. McNeil, R. Frey, P. Embrechts
(2015) Quantitative Risk Management:
Concepts, Techniques and Tools. Revised Edition, Princeton University Press. | Center for Advanced Studies: 09.11.2015 | Speaker: Prof. Dr. Paul Embrechts
Under both Basel II/III for banking as well
as Solvency 2/SST for insurance, Model
Risk (MR), especially for Risk Aggregation
purposes, plays an important role. In this
talk I will concentrate on Dependence Uncertainty and quantify MR from that point
of view. Besides reviewing some of the main results obtained over the recent years,
I will discuss several examples coming from the realm of Operational Risk, as well as the calculation of economic Capital in a real banking example. A basic reference
is A.J. McNeil, R. Frey, P. Embrechts
(2015) Quantitative Risk Management:
Concepts, Techniques and Tools. Revised Edition, Princeton University Press. | Center for Advanced Studies: 09.11.2015 | Speaker: Prof. Dr. Paul Embrechts
Mon, 09 Nov 2015 19:00:00 +0000Prof. Dr. Paul EmbrechtsComputational Methods for Networksno01:11:522https://cast.itunes.uni-muenchen.de/clips/VHqkBfDxX2/vod/high_quality.mp4