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Business Analytics Seminars

The seminars are on Fridays at 11am in Room 498, Merewether Building (cnr of City Road and Butlin Avenue), unless otherwise specified.

The seminar organiser is Laurent Pauwels.

Upcoming Seminars

6th Mar 2015 - 11:00 am

Speaker:

Professor Mikhail Prokopenko,

Affiliation:

Faculty of Engineering & Information Technologies, University of Sydney

Venue:

Seminar Room 498, Merewether Building H04

Title:

Information dynamics of complex computation

Abstract:

Complex networks and systems, such as cortical networks in human modern, power grids, communication and transport systems, genetic regulatory networks, social and eco-systems, and so on, generate rich interactions among components with non-trivial information flows, and exhibit critical phenomena, characterised by phase transitions.   The talk will focus on information dynamics of computation within spatio-temporal systems in terms of three fundamental operations: information storage, transfer, and modification, quantifying these operations on a local scale in space and time. The methods will be exemplified in two contexts: computational neuroscience and random Boolean networks. In addition, we shall discuss a relation between Fisher information and phase transitions, drawing from both thermodynamics and statistical estimation theory. 
13th Mar 2015 - 11:00 am

Speaker:

Prof. Claudia Czado,

Affiliation:

Technische Universität München

Venue:

Seminar Room 498, Merewether Building H04

Title:

Conditional copula simulation for systemic risk stress testing

Abstract:

Since the financial crisis of 2007–2009 there is an active debate by regulators and academic researchers on systemic risk, with the aim of preventing similar crises in the future or at least reducing their impact. A major determinant of systemic risk is the interconnectedness of the international financial market. We propose to analyze interdependencies in the financial market using copulas, in particular using flexible vine copulas, which overcome limitations of the popular elliptical and Archimedean copulas. To investigate contagion effects among financial institutions, we develop methods for stress testing by exploiting the underlying dependence structure. New approaches for Archimedean and, especially, for vine copulas are derived. In a case study of 38 major international institutions, 20 insurers and 18 banks, we then analyze interdependencies of CDS spreads and perform a systemic risk stress test. The specified dependence model and the results from the stress test provide new insights into the interconnectedness of banks and insurers. In particular, the failure of a bank seems to constitute a larger systemic risk than the failure of an insurer.

*joint work with Eike C. Brechmann and Katharina Hendrich

20th Mar 2015 - 11:00 am

Speaker:

Professor Herman K. Van Dijk,

Affiliation:

Department VU, University Amsterdam

Venue:

Seminar Room 498, Merewether Building H04

Title:

Combined Density Nowcasting in an Uncertain Economic Environment

Abstract:

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian Sequential Monte Carlo method which re-balances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on US real-time data of 120 leading indicators, indicate that CDN gives more accurate density nowc asts of US GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.

* joint work with Knut Are Aastveit and Francesco Ravazzolo