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Informationen zum Autor Svetlozar T. Rachev, PhD , Doctor of Science, is Chair-Professor at the University of Karlsruhe in the School of Economics and Business Engineering; Professor Emeritus at the University of California, Santa Barbara; and Chief-Scientist of FinAnalytica Inc. Stoyan V. Stoyanov, PhD , is the Chief Financial Researcher at FinAnalytica Inc. Frank J. Fabozzi, PhD, CFA , is Professor in the Practice of Finance and Becton Fellow at Yale University's School of Management and the Editor of the Journal of Portfolio Management. Klappentext Advanced Stochastic Models, Risk Assessment, and Portfolio OptimizationThe 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. Zusammenfassung Advanced Stochastic Models, Risk Assessment, and Portfolio OptimizationThe 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. Inhaltsverzeichnis Preface xiii Acknowledgments xv About the Authors xvii Chapter 1 Concepts of Probability 1 1.1 Introduction 1 1.2 Basic Concepts 2 1.3 Discrete Probability Distributions 2 1.3.1 Bernoulli Distribution 3 1.3.2 Binomial Distribution 3 1.3.3 Poisson Distribution 4 1.4 Continuous Probability Distributions 5 1.4.1 Probability Distribution Function, Probability Density Function, and Cumulative Distribution Function 5 1.4.2 The Normal Distribution 8 1.4.3 Exponential Distribution 10 1.4.4 Student's t-distribution 11 1.4.5 Extreme Value Distribution 12 1.4.6 Generalized Extreme Value Distribution 12 1.5 Statistical Moments and Quantiles 1...
