Complex Systems In Finance And Econometrics

Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.

This title brings together frontier research on complex economic systems, heterogeneous interacting agents, bounded rationality, and nonlinear dynamics in economics. The book contains the proceedings of the CEF2015 (21st Computing in Economics in Finance), held 20-22 June 2015 in Taipei, Taiwan, and addresses some of the important driving forces for various emergent properties in economies, when viewed as complex systems. The breakthroughs reported in this book are a result of an interdisciplinary approach and simulation remains the unifying theme for these papers as they deal with a wide range of topics in economics. The text is a valuable addition to the efforts in promoting the complex systems view in economic science. The computational experiments reported in the book are both transparent and replicable. Complex System Modeling and Simulation in Economics and Finance is useful for graduate courses of complex systems, with particular focus on economics and finance. At the same time it serves as a good overview for researchers who are interested in the topic.

In the era of Big Data our society is given the unique opportunity to understand the inner dynamics and behavior of complex socio-economic systems. Advances in the availability of very large databases, in capabilities for massive data mining, as well as progress in complex systems theory, multi-agent simulation and computational social science open the possibility of modeling phenomena never before successfully achieved. This contributed volume from the Perm Winter School address the problems of the mechanisms and statistics of the socio-economics system evolution with a focus on financial markets powered by the high-frequency data analysis. ​

This interdisciplinary book argues that the economy has an underlying non-linear structure and that business cycles are endogenous, which allows a greater explanatory power with respect to the traditional assumption that dynamics are stochastic and shocks are exogenous. The first part of this work is formal-methodological and provides the mathematical background needed for the remainder, while the second part presents the view that signal processing involves construction and deconstruction of information and that the efficacy of this process can be measured. The third part focuses on economics and provides the related background and literature on economic dynamics and the fourth part is devoted to new perspectives in understanding nonlinearities in economic dynamics: growth and cycles. By pursuing this approach, the book seeks to (1) determine whether, and if so where, common features exist, (2) discover some hidden features of economic dynamics, and (3) highlight specific indicators of structural changes in time series. Accordingly, it is a must read for everyone interested in a better understanding of economic dynamics, business cycles, econometrics and complex systems, as well as non-linear dynamics and chaos theory.

Over the last two decades there has been a great deal of research into nonlinear dynamic models in economics, finance and the social sciences. This book contains twenty papers that range over very recent applications in these areas. Topics covered include structural change and economic growth, disequilibrium dynamics and economic policy as well as models with boundedly rational agents. The book illustrates some of the most recent research tools in this area and will be of interest to economists working in economic dynamics and to mathematicians interested in seeing ideas from nonlinear dynamics and complexity theory applied to the economic sciences.

Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters. Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.

Agent-based modeling/simulation is an emergent approach to the analysis of social and economic systems. It provides a bottom-up experimental method to be applied to social sciences such as economics, management, sociology, and politics as well as some engineering fields dealing with social activities. This book includes selected papers presented at the Sixth International Workshop on Agent-Based Approaches in Economic and Social Complex Systems held in Taipei in 2009. We have 39 presentations in the conference, and 14 papers are selected to be included in this volume. These 14 papers are then grouped into six parts: Agent-based financial markets; Financial forecasting and investment; Cognitive modeling of agents; Complexity and policy analysis; Agent-based modeling of good societies; and Miscellany. The research presented here shows the state of the art in this rapidly growing field.

This second book on financial and economic simulations in Swarm marks the continued progress by a group of researchers to incorporate agent-based computer models as an important tool within their disci pline. It is encouraging to see such a clear example of Swarm helping to foster a community of users who rely on the Swarm framework for their own analyses. Swarm aims at legitimizing agent-based computer models as a tool for the study of complex systems. A further goal is that a common base framework will lead to the growth of user communities in specific areas of application. By providing an organizing framework to guide the development of more problem-specific structures, and by dealing with a whole range of issues that affect their fundamental correctness and their ability to be developed and reused, Swarm has sought to make the use of agent-based models a legitimate tool of scientific investigation that also meets the practical needs of investigators within a community.