ECONOPHYSICSAssetreturndistribution：资产收益分布的研究.pptVIP

Networks of Companies from StockPrice Correlations J. Kertész1,2, L. Kullmann1, J.-P. Onnela2, A. Chakraborti2, K. Kaski2, A. Kanto3 1Department of Theoretical Physics Budapest University of Technology and Economics, Hungary 2Laboratory of Computational Engineering Helsinki University of Technology, Finland 3Dept of Quantitative Methods in Economics and Management Science Helsinki School of Economics, Finland Motivation Financial market is a self-adaptive complex system; many interacting units, obvious networking. Networks: Cooperation Most important and most difficult Activity, ownership Similarity Temporal aspects Networks generated by time dependencies Time dependent networks Revealing NW structure is crucial for understanding and also for pragmatic reasons (e.g., portfolio opt.) Outline Classification by Minimum Spanning Trees (MST) (Mantegna) Temporal evolution Relation to portfolio optimization Correlations vs. noise: Parametric aggregational classification Temporal correlations: Directed NW of influence Data: price and return Time series of asset returns Return matrix: Data is divided into M time-windows of width T displaced by ?T, thus getting M matrices Correlations and distances Central vertex Central vertex: comparison Asset tree and clusters Potts superparamagnetic clustering Asset tree clustering Mean occupation layer In order to characterise the spread of vertices on the asset tree, concept of mean occupation layer is introduced: where vc is the central vertex, lev(vi) denotes the level of vertex vi , such that lev(vc) = 0. Both static and dynamic central vertex may be used: exhibit similar behaviour ? Robustness Asset tree: topology change Normal market topology crash topology Robustness: single-step survival Robustness of dynamic asset tree topology measured as the ratio of surviving connections when moving by one step: Single-step survival ratio: Tree evolution: multi-step survival Evolution of graphs and tre