The chapters of this book are the selected papers from those presented at the Third International Workshop on Agent-Based Approaches in Economic and Social Complex Systems held in Tokyo, Japan in 2005. Articles cover methodological issues, computational model/software, combination with gaming simulation, and real-world applications to economic, management/organizational and social issues.
Assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes; mathematical literature on the stochastic processes involved; and models from economic theory providing plausible micro foundations for the occurrence of long memory in economics.
The explosive growth in computational power over the past several decades offers new tools and opportunities for economists. This handbook volume surveys recent research on Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. Empirical referents for "agents" in ACE models can range from individuals or social groups with learning capabilities to physical world features with no cognitive function. Topics covered include: learning; empirical validation; network economics; social dynamics; financial markets; innovation and technological change; organizations; market design; automated markets and trading agents; political economy; social-ecological systems; computational laboratory development; and general methodological issues.*Every volume contains contributions from leading researchers*Each Handbook presents an accurate, self-contained survey of a particular topic *The series provides comprehensive and accessible surveys
This book provides an alternative view of the workings of foreign exchange markets. The authors' modeling approach is based on the idea that agents use simple forecasting rules and switch to those rules that have been shown to be the most profitable in the past. This selection mechanism is based on trial and error and is probably the best possible strategy in an uncertain world, the authors contend. It creates a rich dynamic in the foreign exchange markets and can generate bubbles and crashes. Sensitivity to initial conditions is a pervasive force in De Grauwe and Grimaldi's model. It explains why large exchange-rate changes and volatility clustering occur. It also has important implications for understanding how the news affects the exchange rate. De Grauwe and Grimaldi conclude that news in fundamentals has an unpredictable effect on the exchange rate. Sometimes, they maintain, it alters the exchange rate considerably; at other times it has no effectwhatsoever. The authors also use their model to analyze the effects of official interventions in the foreign exchange market. They show that simple intervention rules of the "leaning-against-the-wind" variety can be effective in eliminating bubbles and crashes in the exchange rate. They further demonstrate how, quite paradoxically, by intervening in the foreign exchange market the central bank makes the market look more efficient. Clear and comprehensive, The Exchange Rate in a Behavioral Finance Framework is a must-have for analysts in foreign exchange markets as well as students of international finance and economics.
The book is motivated by the disruptions introduced by the financial crisis and the many attempts that have followed to propose new ideas and remedies. Assembling contributions by authors from a variety of backgrounds, this collection illustrates the potentials resulting from the marriage of financial economics, complexity theory and an out-of-equilibrium view of the economic world. Challenging the traditional hypotheses that lie behind financial market functioning, new evidence is provided about the hidden factors fuelling bubbles, the impact of agents’ heterogeneity, the importance of endogeneity in the information transmission mechanism, the dynamics of herding, the sources of volatility, the portfolio optimization techniques, the financial innovation and the trend identification in a nonlinear time-series framework. Presenting the advances made in financial market analysis, and putting emphasis on nonlinear dynamics, this book suggests interdisciplinary methodologies for the study of well-known stylised facts and financial abnormalities. This book was originally published as a special issue of The European Journal of Finance.
The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.
Liquid markets generate hundreds or thousands of ticks (the minimum change in price a security can have, either up or down) every business day. Data vendors such as Reuters transmit more than 275,000 prices per day for foreign exchange spot rates alone. Thus, high-frequency data can be a fundamental object of study, as traders make decisions by observing high-frequency or tick-by-tick data. Yet most studies published in financial literature deal with low frequency, regularly spaced data. For a variety of reasons, high-frequency data are becoming a way for understanding market microstructure. This book discusses the best mathematical models and tools for dealing with such vast amounts of data.This book provides a framework for the analysis, modeling, and inference of high frequency financial time series. With particular emphasis on foreign exchange markets, as well as currency, interest rate, and bond futures markets, this unified view of high frequency time series methods investigates the price formation process and concludes by reviewing techniques for constructing systematic trading models for financial assets.
Understanding the mechanism of a socio-economic system requires more than an understanding of the individuals that comprise the system. It also requires understanding how individuals interact with each other, and how the agg- gated outcome can be more than the sum of individual behaviors. This book contains the papers fostering the formation of an active multi-disciplinary community on socio-economic systems with the exciting new ?elds of age- based modeling and econophysics. We especially intend to increase the awareness of researchers in many ?elds with sharing the common view many economic and social activities as collectives of a large-scale heterogeneous and interacting agents. Economists seek to understand not only how individuals behave but also how the interaction of many individuals leads to complex outcomes. Age- based modeling is a method for studying socio-economic systems exhibiting the following two properties: (1) the system is composed of interacting agents, and (2) the system exhibits emergent properties, that is, properties arising from the interactions of the agents that cannot be deduced simply by agg- gating the properties of the system’s components. When the interaction of the agents is contingent on past experience, and especially when the agents continually adapt to that experience, mathematical analysis is typically very limited in its ability to derive the outcome.
The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.
Economic Systems exhibit complex dynamics evidenced by large-amplitude and aperiodic fluctuations in economic variables, such as foreign exchange rates and stock market prices, indicating that these systems are driven far from the equilibrium. Characterization of the complex behavior of economic cycles, by identifying regular and irregular patterns and regime switching in economic time series, is the key for pattern recognition and forecasting of economic cycles. Statistical analysis of stock markets and foreign exchange markets has demonstrated the intermittent nature of economic time series. A nonlinear model of business cycles is able to simulate intermittency arising from order-chaos and chaos-chaos transitions. This monograph introduces new concepts of unstable periodic orbits and chaotic saddles which are unstable structures embedded in a chaotic attractor, responsible for economic intermittency.
