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Arbeitskreis Physik sozio-ökonomischer Systeme Tagesübersichten Hauptvorträge PHYSIK SOZIO-ÖKONOMISCHER SYSTEME (AKSOE) Vorsitzender: Priv.-Doz. Dr. Dr. Frank Schweitzer Fraunhofer Institut für Autonome Intelligente Systeme Schloß Birlinghoven 53754 Sankt Augustin E-Mail: schweitzer@ais.fraunhofer.de ÜBERSICHT DER HAUPTVORTRÄGE UND FACHSITZUNGEN (Hörsäle H8 und H10) AKSOE 1.1 Mo 09:30 (H8) Toward an Understanding of Market Behavior: A Physics Perspective, Neil Johnson AKSOE 4.1 Di 09:30 (H8) Stochastic Games: Theory and Simulation, Kristian Lindgren AKSOE 6.1 Di 16:00 (H10) The Dynamics of Information, Bernardo Huberman AKSOE 10.1 Do 14:00 (H8) Physicists’ Insights into Wealth Distributions, Frantiˇsek Slanina Fachsitzungen AKSOE 1 Finanzmärkte und Risikomanagement Mo 09:30–12:30 H8 AKSOE 1.1–1.5 AKSOE 2 Urbane Systeme und Verkehrsdynamik Mo 14:00–15:30 H8 AKSOE 2.1–2.3 AKSOE 3 Postersitzung Mo 16:00–18:00 Poster D AKSOE 3.1–3.13 AKSOE 4 Soziale Systeme und Entscheidungsmodelle I Di 09:30–12:30 H8 AKSOE 4.1–4.5 AKSOE 5 Soziale Systeme und Entscheidungsmodelle II Di 14:00–15:30 H8 AKSOE 5.1–5.3 AKSOE 6 Sondersitzung: Verleihung des Young-Scientist- Award for Socio- and Econophysics Di 16:00–18:00 H10 AKSOE 6.1–6.2 AKSOE 7 Makro-ökonomische Modelle und Wirtschaftswachtum I Mi 14:00–15:30 H8 AKSOE 7.1–7.3 AKSOE 8 Makro-ökonomische Modelle und Wirtschaftswachtum II Mi 16:00–18:00 H8 AKSOE 8.1–8.4 AKSOE 9 Symposium: Fat Tail Distributions – Applicati- Do 09:30–12:30 H1 AKSOE 9.1–9.1 on from Physics to Finance AKSOE 10 Mikro-ökonomische Modelle und Multi- Agenten-Systeme Sondersitzung: Verleihung des Young-Scientist Award for Socio- and Econophysics Di 16:00–18:00, H10 1. Hauptvortrag: Bernardo Huberman 2. Preisverleihung 3. Vortrag der Preisträgerin / des Preisträgers Do 14:00–17:30 H8 AKSOE 10.1–10.6 Mitgliederversammlung des Fachverbands Arbeitskreis Physik sozio-ökonomischer Systeme Mi 18:00–19:00 H8 1. Bericht des Vorsitzenden des AKSOE 2. Diskussion über geplante Aktivitäten 3. Verschiedenes gez. Frank Schweitzer
Arbeitskreis Physik sozio-ökonomischer Systeme Montag Fachsitzungen – Haupt-, Kurzvorträge und Posterbeiträge – AKSOE 1 Finanzmärkte und Risikomanagement Zeit: Montag 09:30–12:30 Raum: H8 Hauptvortrag AKSOE 1.1 Mo 09:30 H8 Toward an Understanding of Market Behavior: A Physics Perspective — •Neil Johnson — Physics Department, Oxford University U.K. There is growing interest in agent-based market models within the physics community. In light of this, we give an overview of such physicsbased approaches to self-consistent market modelling. We will focus on the physics community’s search for a minimal market model, and examine the trade-off between the desire for simplicity, yet consistency with the known ’stylized facts’ of markets. [For further details, see ’Financial Market Complexity’ (Oxford University Press, 2003).] We will then briefly explore related applications of these models in sociological and biological domains. 15 min. Pause AKSOE 1.2 Mo 10:30 H8 Modeling Correlations in Portfolio Credit Risk — •Bernd Rosenow 1 , Rafael Weißbach 2 , and Frank Altrock 2 — 1 Institut für Theoretische Physik, Universität zu Köln, 50923 Köln, Germany — 2 Risk Management Support & Control, WestLB AG, Düsseldorf, Germany The risk of a credit portfolio depends crucially on correlations between the probability of default (PD) in different economic sectors. Often, PD correlations have to be estimated from relatively short time series of default rates, and the resulting estimation error hinders the detection of a signal. We present statistical evidence that PD correlations are well described by a (one-)factorial model. However, when using the model to generate short time series and calculating their correlation matrix, one typically observes large statistical fluctuations in the correlation structure. Due to these fluctuations, the parameter estimation for a one–factor model is plagued by large uncertainties. When estimating the model parameters in such a way that the empirically observed ones appear as a worst case scenario, the reliability of the estimate is increased in a systematic way, leading to a moderately increased CreditVaR. AKSOE 1.3 Mo 11:00 H8 Tackling Mutual Funds Style Analysis with Medoid Clustering and Differential Evolution — •Thiemo Krink 1 , Sandra Paterlini 2 , Tommaso Minerva 2 , and Francesco Pattarin 2 — 1 EVALife Group, Dept. of Computer Science, Univ. of Aarhus, Denmark — 2 Dept. of Political Economics, Univ. of Modena and Reggio Emilia, Italy Mutual funds style analysis requires objective, representative, consistent and empirically testable classification schemes in order to give reliable information to investors and fund managers who are interested in evaluating and comparing different financial products. Institutional classification schemes, when available, do not always provide consistent and representative peer groups of funds. In this study, we introduce a classification algorithm, which identifies mutual funds styles by analysing the time series of past returns. The proposed classification procedure consists of three steps: (a) dimensionality reduction based on principal component analysis, (b) clustering by a novel medoid evolution approach utilizing differential evolution, and (c) style identification by a constrained regression model. We tested the algorithm regarding Italian mutual funds data and achieved satisfactory results with respect to the agreement with the existing institutional classification and the explanatory power of out of sample variability in the cross-section of returns. AKSOE 1.4 Mo 11:30 H8 Significance of log-periodic signatures in cumulative noise — •Hans-Christian Graf v. Bothmer — Universität Hannover, Institut für Mathematik (C), Welfengarten 1, 30167 Hannover Using methods introduced by Scargle we derive a cumulative version of the Lomb periodogram that exhibits frequency independent statistics when applied to cumulative noise. We show how this cumulative Lomb periodogram allows us to estimate the significance of log-periodic signatures in the S&P 500 anti-bubble that started in August 2000. AKSOE 1.5 Mo 12:00 H8 Application of Heston and Hull-White model to German DAX data — •Ralf Remer and Reinhard Mahnke — University of Rostock, Department of Physics, 18051 Rostock We concentrate on the stock price dynamics described by the Heston and the Hull -White model [1]. We apply the model to the German tickby-tick DAX data [2]. The data are from May 1996 to December 2001. We compare the short and the long time solution of the Heston model [3] with the DAX data and with numerical simulations of the Heston model. Therefore we use the probability density distributions of the logarithmic returns, calculated out of the data, and fit these distributions with the theoretical distribution. Finally we compare the short-time solution of the Heston model and of the Hull-White model as another example of the models with stochastic volatility. [1] Fouque, J P, Papanicolaou G and Sircar K R 2000 Derivatives in Financial Markets with Stochastic Volatility (Cambridge: Cambridge University Press) [2] Karlsruher Kapitalmarktdatenbank 2002 DAX and its stock prices (Universität Karlsruhe) [3] Drăgulescu A A and Yakovenko V M 2002 Quant. Finance 2 443 AKSOE 2 Urbane Systeme und Verkehrsdynamik Zeit: Montag 14:00–15:30 Raum: H8 AKSOE 2.1 Mo 14:00 H8 New experimental results concerning the car-following dynamics — •Peter Wagner 1 , Georg Hertkorn 1 , and Ihor Lubashevsky 2 — 1 Institut für Verkehrsforschung, Deutsches Zentrum für Luft- und Raumfahrt, Rutherfordstrasse 2, 12489 Berlin — 2 General Physics Institute, Russian Academy of Sciences, Vavilov Str. 38, Moscow, 119991, RUSSIA Car-following experiments conducted recently with cars equipped with DGPS (differential GPS) deliver fairly accurate and highly interesting data. The distances ∆x, the speeds v(t) and the accelerations can be measured (or computed) making it possible to draw solid conclusion related to the car-following dynamic. In contrast to almost all of the car-following models currently in use the data demonstrate that the behaviour of the drivers is described by fairly old (but rarely used) ”action-point” models. These models claim that the acceleration is constant most of the time but is changing very fast at the action-points. The time between subsequent action-points is distributed exponentially, compatible with the assumption that the decision to change acceleration is drawn randomly. It follows, that there is no fixed-point of the car-following dynamic. It can be hypothesized that this is due to driver’s inability to regulate speed difference ∆v and acceleration exactly to zero. Nevertheless, there is a strong attraction toward these values, as can be seen by the cusp in the frequency distribution of ∆v = 0. From these results, important consequences might be drawn for the modeling of the car-following process.
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