The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance. Each individual volume in the series should present an accurate self-contained survey of a sub-field of finance, suitable for use by finance and economics professors and lecturers, professional researchers, graduate students and as a teaching supplement. The goal is to have a broad group of outstanding volumes in various areas of finance. The Handbook of Heavy Tailed Distributions in Finance is the first handbook to be published in this series.

This volume presents current research focusing on heavy tailed distributions in finance. The contributions cover methodological issues, i.e., probabilistic, statistical and econometric modelling under non- Gaussian assumptions, as well as the applications of the stable and other non -Gaussian models in finance and risk management.

A Probability Metrics Approach to Financial Risk Measures relates the field of probability metrics and risk measures to one another and applies them to finance for the first time. * Helps to answer the question: which risk measure is best for a given problem? * Finds new relations between existing classes of risk measures * Describes applications in finance and extends them where possible * Presents the theory of probability metrics in a more accessible form which would be appropriate for non-specialists in the field * Applications include optimal portfolio choice, risk theory, and numerical methods in finance * Topics requiring more mathematical rigor and detail are included in technical appendices to chapters

"Bayesian Methods in Finance" provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management--since these are the areas in finance where Bayesian methods have had the greatest penetration to date.

A comprehensive look at how probability and statistics is applied to the investment process

Finance has become increasingly more quantitative, drawing on techniques in probability and statistics that many finance practitioners have not had exposure to before. In order to keep up, you need a firm understanding of this discipline."Probability and Statistics for Finance" addresses this issue by showing you how to apply quantitative methods to portfolios, and in all matter of your practices, in a clear, concise manner. Informative and accessible, this guide starts off with the basics and builds to an intermediate level of mastery. - Outlines an array of topics in probability and statistics and how to apply them in the world of finance- Includes detailed discussions of descriptive statistics, basic probability theory, inductive statistics, and multivariate analysis- Offers real-world illustrations of the issues addressed throughout the textThe authors cover a wide range of topics in this book, which can be used by all finance professionals as well as students aspiring to enter the field of finance.

Financial econometrics combines mathematical and statistical theory and techniques to understand and solve problems in financial economics. Modeling and forecasting financial time series, such as prices, returns, interest rates, financial ratios, and defaults, are important parts of this field.

In Financial Econometrics, you'll be introduced to this growing discipline and the concepts associated with it--from background material on probability theory and statistics to information regarding the properties of specific models and their estimation procedures.

With this book as your guide, you'll become familiar with: Autoregressive conditional heteroskedasticity (ARCH) and GARCH modeling Principal components analysis (PCA) and factor analysis Stable processes and ARMA and GARCH models with fat-tailed errors Robust estimation methods Vector autoregressive and cointegrated processes, including advanced estimation methods for cointegrated systems And much more

The experienced author team of Svetlozar Rachev, Stefan Mittnik, Frank Fabozzi, Sergio Focardi, and Teo Jasic not only presents you with an abundant amount of information on financial econometrics, but they also walk you through a wide array of examples to solidify your understanding of the issues discussed.

Filled with in-depth insights and expert advice, Financial Econometrics provides comprehensive coverage of this discipline and clear explanations of how the models associated with it fit into today's investment management process.

An in-depth guide to understanding probability distributions and financial modeling for the purposes of investment management

In "Financial Models with Levy Processes and Volatility Clustering," the expert author team provides a framework to model the behavior of stock returns in both a univariate and a multivariate setting, providing you with practical applications to option pricing and portfolio management. They also explain the reasons for working with non-normal distribution in financial modeling and the best methodologies for employing it.

The book's framework includes the basics of probability distributions and explains the alpha-stable distribution and the tempered stable distribution. The authors also explore discrete time option pricing models, beginning with the classical normal model with volatility clustering to more recent models that consider both volatility clustering and heavy tails.Reviews the basics of probability distributionsAnalyzes a continuous time option pricing model (the so-called exponential Levy model)Defines a discrete time model with volatility clustering and how to price options using Monte Carlo methodsStudies two multivariate settings that are suitable to explain joint extreme events

"Financial Models with Levy Processes and Volatility Clustering" is a thorough guide to classical probability distribution methods and brand new methodologies for financial modeling.

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization

The finance industry is seeing increased interest in new risk measures and techniques for portfolio optimization when parameters of the model are uncertain.

This groundbreaking book extends traditional approaches of risk measurement and portfolio optimization by combining distributional models with risk or performance measures into one framework. Throughout these pages, the expert authors explain the fundamentals of probability metrics, outline new approaches to portfolio optimization, and discuss a variety of essential risk measures. Using numerous examples, they illustrate a range of applications to optimal portfolio choice and risk theory, as well as applications to the area of computational finance that may be useful to financial engineers. They also clearly show how stochastic models, risk assessment, and optimization are essential to mastering risk, uncertainty, and performance measurement.

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization provides quantitative portfolio managers (including hedge fund managers), financial engineers, consultants, and?academic researchers with answers to the key question of which risk measure is best for any given problem.

The Athens Conference on Applied Probability and Time Series in 1995 brought together researchers from across the world. The published papers appear in two volumes. Volume I includes papers on applied probability in Honor of J.M. Gani. The topics include probability and probabilistic methods in recursive algorithms and stochastic models, Markov and other stochastic models such as Markov chains, branching processes and semi-Markov systems, biomathematical and genetic models, epidemilogical models including S-I-R (Susceptible-Infective-Removal), household and AIDS epidemics, financial models for option pricing and optimization problems, random walks, queues and their waiting times, and spatial models for earthquakes and inference on spatial models.