Stable Paretian Models in Finance

Svetlozar Rachev and Stefan Mittnik

Wiley Series in Financial Economics and Quantitative Analysis (June, 2000)

In spite of the fact that the theoretical and empirical finance have been developed upon the assumption that asset returns follow a normal distribution, many empirical results prove that financial variables are leptokurtic. This is not only of concern to financial theorists, but also to practitioners who are troubled by the frequency of the sharp market down turns.

This book develops a generalization of the main concepts in modern theoretical and empirical finance, relaxing the assumption of normality of assets returns, using heavy-tailed distributions. New non-Gaussian approaches to issues like security pricing, portfolio management, risk analysis, and empirical analysis are presented.

The book will be of interest to researchers and graduate students working in financial economics, as well as to the practitioners, who want to base their financial models and decisions on more realistic and reliable distributional assumptions.

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures

Bayesian Methods in Finance

Financial Econometrics: From Basics to Advanced Modeling Techniques

Operational Risk: A Guide to Basel II Capital Requirements, Models and Analysis

Anna S. Chernobai, Svetlozar T. Rachev, Frank J. Fabozzi

Wiley (Fall, 2006)

While operational risk has long been regarded as a mere part of "other" risks--outside the realm of credit and market risk--it has quickly made its way to the forefront of finance. In fact, with implementation of the Basel II Capital Accord already underway, many financial professionals--as well as those preparing to enter this field--must now become familiar with a variety of issues related to operational risk modeling and management.

Written by the experienced team of Anna Chernobai, Svetlozar Rachev, and Frank Fabozzi, Operational Risk will introduce you to the key concepts associated with this discipline. Filled with in-depth insights, expert advice, and innovative research, this comprehensive guide not only presents you with an abundant amount of information regarding operational risk, but it also walks you through a wide array of examples that will solidify your understanding of the issues discussed.

Topics covered include:

• The main challenges that exist in modeling operational risk.
• The variety of approaches used to model operational losses.
• Value-at-Risk and its role in quantifying and managing operational risk.
• The three pillars of the Basel II Capital Accord.
• And much more.

Robust Statistics : Theory and Methods

Ricardo A. Maronna, R. Douglas Martin, Victor J. Yohai

Wiley Series in Probability and Statistics (June, 2006)

Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of the robust statistical methods in regression, multivariate analysis, generalized linear models and time series.

Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modeling and data analysis. It is ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work.

Fat-Tailed and Skewed Asset Return Distributions : Implications for Risk Management, Portfolio Selection, and Option Pricing

Svetlozar T. Rachev, Christian Menn and Frank J. Fabozzi

Wiley (August, 2005)

While mainstream financial theories and applications assume that asset returns are normally distributed, the overwhelming empirical evidence shows otherwise. Yet many professionals fail to appreciate the highly statistical models that take this empirical evidence into consideration. Svetlozar Rachev, Christian Menn, and Frank understand this dilemma, and in Fat-Tailed and Skewed Asset Return Distributions, they offer you a less technical look at how portfolio selection, risk management, and option pricing modeling should and can be undertaken when the assumption of a non-normal distribution for asset returns is violated.Topics covered in this comprehensive book include:

• An extensive discussion of probability distributions used in finance
• Estimating probability distributions
• The basics of stochastic processes
• Portfolio selection and alternative risk measures
• Market, credit, and operational risk measurement
• Black-Scholes option pricing model and its extensions when the model's assumptions are modified to meet the empirical distributional evidence and tests
• And much more

Fat-Tailed and Skewed Asset Return Distributions provides a bridge between the highly technical theory of statistical distributional analysis, stochastic processes, and econometrics of financial returns and real-world risk management and investments.

Introduction to Modern Portfolio Optimization with NuOPT, S-PLUS and S+Bayes

Bernd Scherer, R. Douglas Martin

Springer (May 2005)

In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management, and this trend will only accelerate in the coming years. Unfortunately there is a large gap between the limited treatment of portfolio construction methods that are presented in most university courses with relatively little hands-on experience and limited computing tools, and the rich and varied aspects of portfolio construction that are used in practice in the finance industry. Current practice demands the use of modern methods of portfolio construction that go well beyond the classical Markowitz mean-variance optimality theory and require the use of powerful scalable numerical optimization methods. This book fills the gap between current university instruction and current industry practice by providing a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods.

 The computational aspect of the book is based on extensive use of S-Plus®, the S+NuOPT™ optimization module, the S-Plus Robust Library and the S+Bayes™ Library, along with about 100 S-Plus scripts and some CRSP® sample data sets of stock returns. A special time-limited version of the S-Plus software is available to purchasers of this book.

 "The authors take a huge step in the long struggle to establish applied post-modern portfolio theory. The optimization and statistical techniques generalize the normal linear model to include robustness, non-normality, and semi-conjugate Bayesian analysis via MCMC. The techniques are very clearly demonstrated by the extensive use and tight integration of S-Plus software. Their book should be an enormous help to students and practitioners trying to move beyond traditional modern portfolio theory." Peter Knez CIO, Global Head of Fixed Income Barclays Global Investors.

Handbook of Computational and Numerical Methods in Finance

Svetlozar T. Rachev (Editor)

Birkhauser (June, 2004)

The subject of numerical methods in finance has recently emerged as a new discipline at the intersection of probability theory, finance, and numerical analysis. The methods employed bridge the gap between financial theory and computational practice, and provide solutions for complex problems that are difficult to solve by traditional analytical methods. Although numerical methods in finance have been studied intensively in recent years, many theoretical and practical financial aspects have yet to be explored. This volume presents current research and survey articles focusing on various numerical methods in finance. Key topics: methodological issues, i.e., genetic algorithms, neural networks, Monte–Carlo methods, finite difference methods, stochastic portfolio optimization, as well as the application of other computational and numerical methods in finance and risk management. The book is designed for the academic community and will also serve professional investors.

Credit Risk - Measurement, Evaluation, and Management

Georg Bol, Cholamreza Nakhaeizadeh, Svetlozar T. Rachev, Tomas Ridder, Karl-Heinz Vollmer

Physica Verlag (July, 2003)

New developments in measuring, evaluating and managing credit risk are discussed in this volume. Addressing both practitioners in the banking sector and research institutions, the book provides a manifold view on one of the most-discussed topics in finance. Among the subjects treated are important issues, such as: the consequences of the new Basel Capital Accord (Basel II), different applications of credit risk models, and new methodologies in rating and measuring credit portfolio risk. The volume provides an overview of recent developments as well as future trends: a state-of-the-art compendium in the area of credit risk.

Handbook of Heavy Tailed Distributions in Finance

Svetlozar T. Rachev

North-Holland (February, 2003)

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 modeling under non- Gaussian assumptions, as well as the applications of the stable and other non -Gaussian models in finance and risk management.

This handbook contains a survey of heavy tailed distributions in finance. Suitable for use by finance and economics professors and lecturers, professional researchers, and graduate students. Includes author and subject index.