Social Network Analysis

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Art of electronics

(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

Market Models provides an authoritative and up–to–date treatment of the use of market data to develop models for financial analysis. Written by a leading figure in the field of financial data analysis, this book is the first of its kind to address the vital techniques required for model selection and development. Model developers are faced with many decisions, about the pricing, the data, the statistical methodology and the calibration and testing of the model prior to implementation. It is important to make the right choices and Carol Alexander s clear exposition provides valuable insights at every stage. In each of the 13 Chapters, Market Models presents real world illustrations to motivate theoretical developments. The accompanying CD contains spreadsheets with data and programs; this enables you to Market models: a guide to financial data analysis Market models: A guide to financial data analysis. Home > Research > Our research > STUDY WITH US ABOUT US NEWS & EVENTS INTERNATIONAL RESEARCH HOME implement and adapt many of the examples. The pricing of options using normal mixture density functions to model returns; the use of Monte Carlo simulation to calculate the VaR of an options portfolio; modifying the covariance VaR to allow for fat–tailed P&L distributions; the calculation of implied, EWMA and historic volatilities; GARCH volatility term structure forecasting; principal components analysis; and many more are all included. Carol Alexander brings many new insights to the pricing and hedging of options with her understanding of volatility and correlation, and the uncertainty which surrounds these key determinants of option portfolio risk. Modelling the market risk of portfolios is covered where the main focus is on a linear algebraic approach; the covariance matrix and principal component analysis are developed as key tools for the analysis of financial systems. The traditional time series econometric approach is also explained with coverage ranging from the application cointegration to long–short equity hedge funds, to high–frequency data prediction using neural networks and nearest neighbour algorithms. Throughout this text the emphasis is on understanding concepts and implementing solutions. It has been designed to be accessible to a very wide audience: the coverage is comprehensive and complete and the technical appendix makes the book largely self–contained. Market Models: A Guide to Financial Data Analysis is the ideal reference for all those involved in market risk measurement, quantitative trading and investment analysis. Item Type: Book Schools and Departments: School of Business, Management and Economics > Business and Management Subjects: H Social Sciences > HG Finance Depositing User: Carol Alexander Date Deposited: 27 Sep 2012 12:36 Last Modified: 27 Sep 2012 12:36 URI: http://sro.sussex.ac.uk/id/eprint/40646

Market Models: A Guide to Financial Data Analysis

Collective control of a multiagent system is concerned with designing strategies for a group of autonomous agents operating in a networked environment. The aim is to achieve a global control objective through distributed sensing, communication, computing, and control. It has attracted many researchers from a wide range of disciplines, including the literature of automatic control. This paper aims to give a general framework that is able to accommodate many of these outcomes. Within this framework, the development on this topic is systematically reviewed and the representative outcomes can be sorted out from four aspects: 1) agent dynamics; 2) network topologies; 3) feedback and communication mechanisms; and 4) collective behaviors. Thus, the state-of-the-art approach and technology is described. Moreover, within this framework, further interesting and promising directions on this research topic are envisioned.

Overview: Collective Control of Multiagent Systems

Determining interrelatedness structure of various entities from multiple time series data is of significant interest to many areas. Knowledge of such a structure can aid in identifying cause and effect relationships, clustering of similar entities, identification of representative elements and model reduction. The majority of existing results are based on correlation based indices which effectively assume a static relationship between the time series data and are not suitable for detecting interrelatedness when the time series are dynamically related or when the time series involve loops. In this paper, a methodology for identifying the interrelatedness structure of dynamically related time series data is presented that also allows for the presence of loops in the connectivity structure. A linear dynamic graph model is presented where it is assumed that each time series data is the sum of an independent stochastic noise source and a dynamically weighted sum of other time series data. A link is assumed to be present between two time series if the weight of a time series, which is a linear time-invariant filter, is nonzero in the formation of the other. Reconstruction of the link connectivity structure under various scenarios is considered. It is shown that when the linear dynamic graph is allowed to admit non-causal weights, then the links structure can be recovered with the possibility of identifying spurious connections. However, it is shown that the spurious links remain local, where, a spurious link is restricted to be within one hop of a true link. Furthermore, strategies for exact reconstruction of the link structure when the weights are restricted to be causal are developed. The main tools for determining the network topology are based on variations of Wiener filtering. A significant insight provided by the article is that, in the class of network models considered in the paper, the Wiener filter estimating a stochastic process based on other processes remains local in the sense that the Wiener filter utilizes only measurements local to the node being estimated.

On the Problem of Reconstructing an Unknown Topology via Locality Properties of the Wiener Filter

The paper deals with the problem of reconstructing the tree-like topological structure of a network of linear dynamical systems. A distance function is defined in order to evaluate the “closeness” of two processes and some useful mathematical properties are derived. Theoretical results to guarantee the correctness of the identification procedure for networked linear systems characterized by a tree topology are provided as well. The paper also suggests the approximation of a complex connected network with a tree in order to detect the most meaningful interconnections. The application of the techniques to the analysis of an actual complex network, i.e., to high frequency time series of the stock market, is extensively illustrated.

/pdf/topological-identification-in-networks-of-dynamical-systems-ufwgo1983u.pdf

Topological identification in networks of dynamical systems

The paper deals with the problem of reconstructing the topological structure of a network of dynamical systems. A distance function is defined in order to evaluate the "closeness" of two processes and a few useful mathematical properties are derived. Theoretical results to guarantee the correctness of the identification procedure for networked linear systems with tree topology are provided as well. Finally, the application of the techniques to the analysis of an actual complex network, i.e. to high frequency time series of the stock market, is illustrated.

The paper tackles the problem of identifying an individual transfer function in a network of linear dynamical systems in the presence of loops under the assumptions that (i) only a subset of the nodes is observable, and (ii) data are being passively recorded (i.e. it is not possible to intervene on any part of the system by actively injecting an input). Such a scenario is often encountered in the study of many naturally occurring systems and is also motivated by operating networked systems where the injection of an external signal might lead to undesired disruptions. Sufficient conditions on which signals should be observable to guarantee the identifiability of a desired link are provided. The results generalize well-established identification criteria developed in the context of graphical models.

Identification of network components in presence of unobserved nodes

The paper deals with the problem of unveiling the link structure of a network of linear dynamical systems. A technique is provided guaranteeing an exact detection of the links of a network of dynamical systems with no undirected cycles (Linear Dynamic Polytrees). In particular, the presence of unobserved (latent) nodes is taken into account. Knowledge on the specific number of hidden processes is not required. It is proven that the topology can be consistently reconstructed, as long the degree of each latent node is at least three with outdegree of at least two. The result extends previous work that was limited to a more restricted class of dynamical systems (Rooted Trees).

Donatello Materassi

Papers

On the Problem of Reconstructing an Unknown Topology via Locality Properties of the Wiener Filter

Topological identification in networks of dynamical systems

Topological identification in networks of dynamical systems

Identification of network components in presence of unobserved nodes

Network reconstruction of dynamical polytrees with unobserved nodes