This book brings together the latest research in the areas of market microstructure and high-frequency finance along with new econometric methods to address critical practical issues in these areas of research. Thirteen chapters, each of which makes a valuable and significant contribution to the existing literature have been brought together, spanning a wide range of topics including information asymmetry and the information content in limit order books, high-frequency return distribution models, multivariate volatility forecasting, analysis of individual trading behaviour, the analysis of liquidity, price discovery across markets, market microstructure models and the information content of order flow. These issues are central both to the rapidly expanding practice of high frequency trading in financial markets and to the further development of the academic literature in this area. The volume will therefore be of immediate interest to practitioners and academics. This book was originally published as a special issue of European Journal of Finance.

Portfolio theory and much of asset pricing, as well as many empirical applications, depend on the use of multivariate probability distributions to describe asset returns. Traditionally, this has meant the multivariate normal (or Gaussian) distribution. More recently, theoretical and empirical work in financial economics has employed the multivariate Student (and other) distributions which are members of the elliptically symmetric class. There is also a growing body of work which is based on skew-elliptical distributions. These probability models all exhibit the property that the marginal distributions differ only by location and scale parameters or are restrictive in other respects. Very often, such models are not supported by the empirical evidence that the marginal distributions of asset returns can differ markedly. Copula theory is a branch of statistics which provides powerful methods to overcome these shortcomings. This book provides a synthesis of the latest research in the area of copulae as applied to finance and related subjects such as insurance. Multivariate non-Gaussian dependence is a fact of life for many problems in financial econometrics. This book describes the state of the art in tools required to deal with these observed features of financial data. This book was originally published as a special issue of the European Journal of Finance.

This book brings together the latest research in the areas of market microstructure and high-frequency finance along with new econometric methods to address critical practical issues in these areas of research. Thirteen chapters, each of which makes a valuable and significant contribution to the existing literature have been brought together, spanning a wide range of topics including information asymmetry and the information content in limit order books, high-frequency return distribution models, multivariate volatility forecasting, analysis of individual trading behaviour, the analysis of liquidity, price discovery across markets, market microstructure models and the information content of order flow. These issues are central both to the rapidly expanding practice of high frequency trading in financial markets and to the further development of the academic literature in this area. The volume will therefore be of immediate interest to practitioners and academics. This book was originally published as a special issue of European Journal of Finance.

Portfolio theory and much of asset pricing, as well as many empirical applications, depend on the use of multivariate probability distributions to describe asset returns. Traditionally, this has meant the multivariate normal (or Gaussian) distribution. More recently, theoretical and empirical work in financial economics has employed the multivariate Student (and other) distributions which are members of the elliptically symmetric class. There is also a growing body of work which is based on skew-elliptical distributions. These probability models all exhibit the property that the marginal distributions differ only by location and scale parameters or are restrictive in other respects. Very often, such models are not supported by the empirical evidence that the marginal distributions of asset returns can differ markedly. Copula theory is a branch of statistics which provides powerful methods to overcome these shortcomings. This book provides a synthesis of the latest research in the area of copulae as applied to finance and related subjects such as insurance. Multivariate non-Gaussian dependence is a fact of life for many problems in financial econometrics. This book describes the state of the art in tools required to deal with these observed features of financial data. This book was originally published as a special issue of the European Journal of Finance.

Although geometry has always aided intuition in econometrics, more recently differential geometry has become a standard tool in the analysis of statistical models, offering a deeper appreciation of existing methodologies and highlighting the essential issues which can be hidden in an algebraic development of a problem. Originally published in 2000, this volume was an early example of the application of these techniques to econometrics. An introductory chapter provides a brief tutorial for those unfamiliar with the tools of Differential Geometry. The topics covered in the following chapters demonstrate the power of the geometric method to provide practical solutions and insight into problems of econometric inference.

Differential geometry has become a standard tool in the analysis of statistical models, offering a deeper appreciation of existing methodologies and highlighting the issues that can be hidden in an algebraic development of a problem. This volume is the first to apply these techniques to econometrics. An introductory chapter provides a brief tutorial for those unfamiliar with the tools of differential geometry. The following chapters offer applications of geometric methods to practical solutions and offer insight into problems of econometric inference.

Nonlinear Dynamics and Economics, first published in 1997, presents developments in nonlinear economic dynamics along with related research from associated fields, including mathematics, statistics, biology, and physics. Specific areas covered include instability in economic theory, nonlinearity in financial markets, tests for nonlinearity and chaos, frequency domain methods, nonlinear business cycles, and nonlinear prediction and forecasting. This volume comprises the tenth in the International Symposia in Economic Theory and Econometrics series under the general editorship of William Barnett. This proceedings volume includes revisions of the most important papers presented at a conference held at the European University Institute in Florence on July 6-17, 1992, along with revisions of the related, invited papers presented at the annual meetings of the American Statistical Association held in San Francisco on August 8-12, 1993.