Ebook: Financial Modeling Under Non-Gaussian Distributions

Practitioners and researchers who have handled financial market data know that asset returns do not behave according to the bell-shaped curve, associated with the Gaussian or normal distribution. Indeed, the use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. Consequently, non-Gaussian models and models based on processes with jumps are gaining popularity among financial market practitioners.

Non-Gaussian distributions are the key theme of this book which addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. One of the main aims is to bridge the gap between the theoretical developments and the practical implementations of what many users and researchers perceive as "sophisticated" models or black boxes. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series, such as exchange and interest rates. The authors have taken care to make the material accessible to anyone with a basic knowledge of statistics, calculus and probability, while at the same time preserving the mathematical rigor and complexity of the original models.

This book will be an essential reference for practitioners in the finance industry, especially those responsible for managing portfolios and monitoring financial risk, but it will also be useful for mathematicians who want to know more about how their mathematical tools are applied in finance, and as a text for advanced courses in empirical finance; financial econometrics and financial derivatives

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Practitioners and researchers who have handled financial market data know that asset returns do not behave according to the bell-shaped curve, associated with the Gaussian or normal distribution. Indeed, the use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. Consequently, non-Gaussian models and models based on processes with jumps are gaining popularity among financial market practitioners.

Non-Gaussian distributions are the key theme of this book which addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. One of the main aims is to bridge the gap between the theoretical developments and the practical implementations of what many users and researchers perceive as "sophisticated" models or black boxes. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series, such as exchange and interest rates. The authors have taken care to make the material accessible to anyone with a basic knowledge of statistics, calculus and probability, while at the same time preserving the mathematical rigor and complexity of the original models.

This book will be an essential reference for practitioners in the finance industry, especially those responsible for managing portfolios and monitoring financial risk, but it will also be useful for mathematicians who want to know more about how their mathematical tools are applied in finance, and as a text for advanced courses in empirical finance; financial econometrics and financial derivatives

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Practitioners and researchers who have handled financial market data know that asset returns do not behave according to the bell-shaped curve, associated with the Gaussian or normal distribution. Indeed, the use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. Consequently, non-Gaussian models and models based on processes with jumps are gaining popularity among financial market practitioners.

Non-Gaussian distributions are the key theme of this book which addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. One of the main aims is to bridge the gap between the theoretical developments and the practical implementations of what many users and researchers perceive as "sophisticated" models or black boxes. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series, such as exchange and interest rates. The authors have taken care to make the material accessible to anyone with a basic knowledge of statistics, calculus and probability, while at the same time preserving the mathematical rigor and complexity of the original models.

This book will be an essential reference for practitioners in the finance industry, especially those responsible for managing portfolios and monitoring financial risk, but it will also be useful for mathematicians who want to know more about how their mathematical tools are applied in finance, and as a text for advanced courses in empirical finance; financial econometrics and financial derivatives

Content:
Front Matter....Pages I-XVIII
Front Matter....Pages 1-1
Introduction....Pages 3-6
Statistical Properties of Financial Market Data....Pages 7-32
Functioning of Financial Markets and Theoretical Models for Returns....Pages 33-76
Front Matter....Pages 77-77
Modeling Volatility....Pages 79-142
Modeling Higher Moments....Pages 143-193
Modeling Correlation....Pages 195-264
Extreme Value Theory....Pages 265-312
Front Matter....Pages 313-313
Risk Management and VaR....Pages 315-347
Portfolio Allocation....Pages 349-362
Front Matter....Pages 363-363
Fundamentals of Option Pricing....Pages 365-382
Non-structural Option Pricing....Pages 383-416
Structural Option Pricing....Pages 417-448
Front Matter....Pages 449-449
Brownian Motion and Stochastic Calculus....Pages 451-470
Martingale and Changing Measure....Pages 471-476
Characteristic Functions and Fourier Transforms....Pages 477-486
Jump Processes....Pages 487-499
L?vy Processes....Pages 501-506
Back Matter....Pages 507-541

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Practitioners and researchers who have handled financial market data know that asset returns do not behave according to the bell-shaped curve, associated with the Gaussian or normal distribution. Indeed, the use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. Consequently, non-Gaussian models and models based on processes with jumps are gaining popularity among financial market practitioners.

Non-Gaussian distributions are the key theme of this book which addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. One of the main aims is to bridge the gap between the theoretical developments and the practical implementations of what many users and researchers perceive as "sophisticated" models or black boxes. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series, such as exchange and interest rates. The authors have taken care to make the material accessible to anyone with a basic knowledge of statistics, calculus and probability, while at the same time preserving the mathematical rigor and complexity of the original models.

This book will be an essential reference for practitioners in the finance industry, especially those responsible for managing portfolios and monitoring financial risk, but it will also be useful for mathematicians who want to know more about how their mathematical tools are applied in finance, and as a text for advanced courses in empirical finance; financial econometrics and financial derivatives

Content:
Front Matter....Pages I-XVIII
Front Matter....Pages 1-1
Introduction....Pages 3-6
Statistical Properties of Financial Market Data....Pages 7-32
Functioning of Financial Markets and Theoretical Models for Returns....Pages 33-76
Front Matter....Pages 77-77
Modeling Volatility....Pages 79-142
Modeling Higher Moments....Pages 143-193
Modeling Correlation....Pages 195-264
Extreme Value Theory....Pages 265-312
Front Matter....Pages 313-313
Risk Management and VaR....Pages 315-347
Portfolio Allocation....Pages 349-362
Front Matter....Pages 363-363
Fundamentals of Option Pricing....Pages 365-382
Non-structural Option Pricing....Pages 383-416
Structural Option Pricing....Pages 417-448
Front Matter....Pages 449-449
Brownian Motion and Stochastic Calculus....Pages 451-470
Martingale and Changing Measure....Pages 471-476
Characteristic Functions and Fourier Transforms....Pages 477-486
Jump Processes....Pages 487-499
L?vy Processes....Pages 501-506
Back Matter....Pages 507-541
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