Microscopic Simulation (MS) uses a computer to represent and keep track of individual ("microscopic") elements in order to investigate complex systems which are analytically intractable. A methodology that was developed to solve physics problems, MS has been used to study the relation between microscopic behavior and macroscopic phenomena in systems ranging from those of atomic particles, to cars, animals, and even humans. In finance, MS can help explain, among other things, the effects of various elements of investor behavior on market dynamics and asset pricing. It is these issues in particular, and the value of an MS approach to finance in general, that are the subjects of this book. The authors not only put their work in perspective by surveying traditional economic analyses of investor behavior, but they also briefly examine the use of MS in fields other than finance. Most models in economics and finance assume that investors are rational. However, experimental studies reveal systematic deviations from rational behavior. How can we determine the effect of investors' deviations from rational behavior on asset prices and market dynamics? By using Microscopic Simulation, a methodology originally developed by physicists for the investigation of complex systems, the authors are able to relax classical assumptions about investor behavior and to model it as empirically and experimentally observed. This rounded and judicious introduction to the application of MS in finance and economics reveals that many of the empirically-observed "puzzles" in finance can be explained by investors' quasi-rationality. Researchers use the book because it models heterogeneous investors, a group that has proven difficult to model. Being able to predict how people will invest and setting asset prices accordingly is inherently appealing, and the combination of computing power and statistical mechanics in this book makes such modeling possible. Because many finance researchers have backgrounds in physics, the material here is accessible. Emphasizes investor behavior in determining asset prices and market dynamics Introduces Microscopic Simulation within a simplified framework Offers ways to model deviations from rational decision-making

Microscopic Simulation (MS) uses a computer to represent and keep track of individual ("microscopic") elements in order to investigate complex systems which are analytically intractable. A methodology that was developed to solve physics problems, MS has been used to study the relation between microscopic behavior and macroscopic phenomena in systems ranging from those of atomic particles, to cars, animals, and even humans. In finance, MS can help explain, among other things, the effects of various elements of investor behavior on market dynamics and asset pricing. It is these issues in particular, and the value of an MS approach to finance in general, that are the subjects of this book. The authors not only put their work in perspective by surveying traditional economic analyses of investor behavior, but they also briefly examine the use of MS in fields other than finance. Most models in economics and finance assume that investors are rational. However, experimental studies reveal systematic deviations from rational behavior. How can we determine the effect of investors' deviations from rational behavior on asset prices and market dynamics? By using Microscopic Simulation, a methodology originally developed by physicists for the investigation of complex systems, the authors are able to relax classical assumptions about investor behavior and to model it as empirically and experimentally observed. This rounded and judicious introduction to the application of MS in finance and economics reveals that many of the empirically-observed "puzzles" in finance can be explained by investors' quasi-rationality. Researchers use the book because it models heterogeneous investors, a group that has proven difficult to model. Being able to predict how people will invest and setting asset prices accordingly is inherently appealing, and the combination of computing power and statistical mechanics in this book makes such modeling possible. Because many finance researchers have backgrounds in physics, the material here is accessible. Key Features * Emphasizes investor behavior in determining asset prices and market dynamics * Introduces Microscopic Simulation within a simplified framework * Offers ways to model deviations from rational decision-making

A careful examination of the interaction between physics and finance. It takes a look at the 100-year-long history of co-operation between the two fields and goes on to provide new research results on capital markets - taken from the field of statistical physics. The random walk model, well known in physics, is one good example of where the two disciplines meet. In the world of finance it is the basic model upon which the Black-Scholes theory of option pricing and hedging has been built. The underlying assumptions are discussed using empirical financial data and analogies to physical models such as fluid flows, turbulence, or superdiffusion. On this basis, new theories of derivative pricing and risk control can be formulated.

The models of portfolio selection and asset price dynamics in this volume seek to explain the market dynamics of asset prices. Presenting a range of analytical, empirical, and numerical techniques as well as several different modeling approaches, the authors depict the state of debate on the market selection hypothesis. By explicitly assuming the heterogeneity of investors, they present models that are descriptive and normative as well, making the volume useful for both finance theorists and financial practitioners. Explains the market dynamics of asset prices, offering insights about asset management approaches Assumes a heterogeneity of investors that yields descriptive and normative models of portfolio selections and asset pricing dynamics

The current financial crisis has revealed serious flaws in models, measures and, potentially, theories, that failed to provide forward-looking expectations for upcoming losses originated from market risks. The Proceedings of the Perm Winter School 2011 propose insights on many key issues and advances in financial markets modeling and risk measurement aiming to bridge the gap. The key addressed topics include: hierarchical and ultrametric models of financial crashes, dynamic hedging, arbitrage free modeling the term structure of interest rates, agent based modeling of order flow, asset pricing in a fractional market, hedge funds performance and many more.

For the financial industry, it is of paramount importance to understand the statistical features of price processes. While statistical models can reproduce empirical findings, they cannot give insight into the causal relationships and the market's response to a changing environment. Microscopic models of financial markets are an approach to understand the underlying mechanisms determining the market behaviour. In this book, we give an introduction to the basic idea of microscopic modelling with a particular focus on the "Opinion Game," a microscopic market model introduced by Bovier, erny and Hryniv (2006). Within the framework of the Opinion Game, we devote ourselves to two issues: First, the market stability in dependence on long-term investors and, second, the market illiquidity when orders of large volumes are executed. A survey of a JAVA implementation of the Opinion Game completes the book.