{"product_id":"financial-models-with-levy-processes-and-volatility-clustering-isbn-9780470482353","title":"Financial Models with Levy Processes and Volatility Clustering","description":"An in-depth guide to understanding probability distributions and financial modeling for the purposes of investment management  \u003cp\u003eIn \u003ci\u003eFinancial Models with Lévy Processes and Volatility Clustering\u003c\/i\u003e, 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.\u003c\/p\u003e \u003cp\u003eThe 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eReviews the basics of probability distributions\u003c\/li\u003e \u003cli\u003eAnalyzes a continuous time option pricing model (the so-called exponential Lévy model)\u003c\/li\u003e \u003cli\u003eDefines a discrete time model with volatility clustering and how to price options using Monte Carlo methods\u003c\/li\u003e \u003cli\u003eStudies two multivariate settings that are suitable to explain joint extreme events\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eFinancial Models with Lévy Processes and Volatility Clustering\u003c\/i\u003e is a thorough guide to classical probability distribution methods and brand new methodologies for financial modeling.\u003c\/p\u003e  \u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAbout the Authors.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The need for better financial modeling of asset prices.\u003c\/p\u003e \u003cp\u003e1.2 The family of stable distribution and its properties.\u003c\/p\u003e \u003cp\u003e1.3 Option pricing with volatility clustering.\u003c\/p\u003e \u003cp\u003e1.4 Model dependencies.\u003c\/p\u003e \u003cp\u003e1.5 Monte Carlo.\u003c\/p\u003e \u003cp\u003e1.6 Organization of the book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Probability distributions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Basic concepts.\u003c\/p\u003e \u003cp\u003e2.2 Discrete probability distributions.\u003c\/p\u003e \u003cp\u003e2.3 Continuous probability distributions.\u003c\/p\u003e \u003cp\u003e2.4 Statistic moments and quantiles.\u003c\/p\u003e \u003cp\u003e2.5 Characteristic function.\u003c\/p\u003e \u003cp\u003e2.6 Joint probability distributions.\u003c\/p\u003e \u003cp\u003e2.7 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Stable and tempered stable distributions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 α-Stable distribution.\u003c\/p\u003e \u003cp\u003e3.2 Tempered stable distributions.\u003c\/p\u003e \u003cp\u003e3.3 Infinitely divisible distributions.\u003c\/p\u003e \u003cp\u003e3.4 Summary.\u003c\/p\u003e \u003cp\u003e3.5 Appendix.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Stochastic Processes in Continuous Time.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Some preliminaries.\u003c\/p\u003e \u003cp\u003e4.2 Poisson Process.\u003c\/p\u003e \u003cp\u003e4.3 Pure jump process.\u003c\/p\u003e \u003cp\u003e4.4 Brownian motion.\u003c\/p\u003e \u003cp\u003e4.5 Time-Changed Brownian motion.\u003c\/p\u003e \u003cp\u003e4.6 Lévy process.\u003c\/p\u003e \u003cp\u003e4.7 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Conditional Expectation and Change of Measure.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Events, s-fields, and filtration.\u003c\/p\u003e \u003cp\u003e5.2 Conditional expectation.\u003c\/p\u003e \u003cp\u003e5.3 Change of measures.\u003c\/p\u003e \u003cp\u003e5.4 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Exponential Lévy Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Exponential Lévy Models.\u003c\/p\u003e \u003cp\u003e6.2 Fitting a-stable and tempered stable distributions.\u003c\/p\u003e \u003cp\u003e6.3 Illustration: Parameter estimation for tempered stable distributions.\u003c\/p\u003e \u003cp\u003e6.4 Summary.\u003c\/p\u003e \u003cp\u003e6.5 Appendix : Numerical approximation of probability density and cumulative distribution functions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Option Pricing in Exponential Lévy Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Option contract.\u003c\/p\u003e \u003cp\u003e7.2 Boundary conditions for the price of an option.\u003c\/p\u003e \u003cp\u003e7.3 No-arbitrage pricing and equivalent martingale measure.\u003c\/p\u003e \u003cp\u003e7.4 Option pricing under the Black-Scholes model.\u003c\/p\u003e \u003cp\u003e7.5 European option pricing under exponential tempered stable Models.\u003c\/p\u003e \u003cp\u003e7.6 The subordinated stock price model.\u003c\/p\u003e \u003cp\u003e7.7 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Simulation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Random number generators.\u003c\/p\u003e \u003cp\u003e8.2 Simulation techniques for Lévy processes.\u003c\/p\u003e \u003cp\u003e8.3 Tempered stable processes.\u003c\/p\u003e \u003cp\u003e8.4 Tempered infinitely divisible processes.\u003c\/p\u003e \u003cp\u003e8.5 Time-changed Brownian motion.\u003c\/p\u003e \u003cp\u003e8.6  Monte Carlo methods.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Multi-Tail\u003c\/b\u003e \u003cb\u003e\u003ci\u003et\u003c\/i\u003e-distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Principal component analysis.\u003c\/p\u003e \u003cp\u003e9.3 Estimating parameters.\u003c\/p\u003e \u003cp\u003e9.4 Empirical results.\u003c\/p\u003e \u003cp\u003e9.5 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Non-Gaussian portfolio allocation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Multifactor linear model.\u003c\/p\u003e \u003cp\u003e10.3 Modeling dependencies.\u003c\/p\u003e \u003cp\u003e10.4 Average value-at-risk.\u003c\/p\u003e \u003cp\u003e10.5 Optimal portfolios.\u003c\/p\u003e \u003cp\u003e10.6 The algorithm.\u003c\/p\u003e \u003cp\u003e10.7 An empirical test.\u003c\/p\u003e \u003cp\u003e10.8 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Normal GARCH models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 GARCH dynamics with normal innovation.\u003c\/p\u003e \u003cp\u003e11.3 Market estimation.\u003c\/p\u003e \u003cp\u003e11.4 Risk-neutral estimation.\u003c\/p\u003e \u003cp\u003e11.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Smoothly truncated stable GARCH models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 A Generalized NGARCH Option Pricing Model.\u003c\/p\u003e \u003cp\u003e12.3 Empirical Analysis.\u003c\/p\u003e \u003cp\u003e12.4 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Infinitely divisible GARCH models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Stock price dynamic.\u003c\/p\u003e \u003cp\u003e13.2 Risk-neutral dynamic.\u003c\/p\u003e \u003cp\u003e13.3 Non-normal infinitely divisible GARCH.\u003c\/p\u003e \u003cp\u003e13.4 Simulate infinitely divisible GARCH.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Option Pricing with Monte Carlo Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction.\u003c\/p\u003e \u003cp\u003e14.2 Data set.\u003c\/p\u003e \u003cp\u003e14.3 Performance of Option Pricing Models.\u003c\/p\u003e \u003cp\u003e14.4 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 American Option Pricing with Monte Carlo Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 American option pricing in discrete time.\u003c\/p\u003e \u003cp\u003e15.2 The Least Squares Monte Carlo method.\u003c\/p\u003e \u003cp\u003e15.3 LSM method in GARCH option pricing model.\u003c\/p\u003e \u003cp\u003e15.4 Empirical illustration.\u003c\/p\u003e \u003cp\u003e15.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e  \u003cb\u003eSVETLOZAR T. RACHEV\u003c\/b\u003e is Chair-Professor in Statistics, Econometrics, and Mathematical Finance at the Karlsruhe Institute of Technology (KIT) in the School of Economics and Business Engineering; Professor Emeritus at the University of California, Santa Barbara; and Chief Scientist at FinAnalytica Inc.  \u003cp\u003e\u003cb\u003eYOUNG SHIN KIM\u003c\/b\u003e is a scientific assistant in the Department of Statistics, Econometrics, and Mathematical Finance at the Karlsruhe Institute of Technology (KIT).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMICHELE Leonardo BIANCHI\u003c\/b\u003e is an analyst in the Division of Risk and Financial Innovation Analysis at the Specialized Intermediaries Supervision Department of the Bank of Italy.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eFRANK J. FABOZZI\u003c\/b\u003e is Professor in the Practice of Finance and Becton Fellow at the Yale School of Management and Editor of the Journal of PortfolioManagement. He is an Affiliated Professor at the University of Karlsruhe's Institute of Statistics, Econometrics, and Mathematical Finance and serves on the Advisory Council for the Department of Operations Research and Financial Engineering at Princeton University.\u003c\/p\u003e  \u003cp\u003eThe financial crisis that began in the summer of 2007 has led to criticisms that the financial models used by risk managers, portfolio managers, and even regulators simply do not reflect the realities of today's markets. While one tool cannot be blamed for the entire global financial crisis, improving the flexibility and statistical reliability of existing models, in addition to developing better models, is essential for both financial practitioners and academics seeking to explain and prevent extreme events.\u003c\/p\u003e \u003cp\u003eNobody understands this better than the expert author team of Svetlozar Rachev, Young Shin Kim, Michele Leonardo Bianchi, and Frank Fabozzi, and in Financial Models with Lévy Processes and Volatility Clustering, they present a framework for modeling the behavior of stock returns in a univariate and multivariate setting—providing you with practical applications to option pricing and portfolio management. They also explain the reasons for working with non-normal distributions in financial modeling and the best methodologies for employing them.\u003c\/p\u003e \u003cp\u003eThis reliable resource includes detailed discussions of 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. This practical guide:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eReviews the basics of probability distributions\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eAnalyzes a continuous-time option pricing model (the so-called exponential Lévy model)\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eDefines a discrete-time model with volatility clustering and how to price options using Monte Carlo methods\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eStudies two multivariate settings that are suitable for explaining joint extreme events\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eAnd much more\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eFilled with in-depth insights and expert advice, Financial Models with Lévy Processes and Volatility Clustering is a thorough guide to both current probability distribution methods and brand new methodologies for financial modeling.\u003c\/p\u003e  FINANCIAL MODELS WITH LéVY PROCESSES AND VOLATILITY CLUSTERING  \u003cp\u003eThe failure of financial models has been identified by some market observers as a major contributor to the global financial crisis. More specifically, it's been argued that the underlying assumption made in most of these models—that distributions of prices and returns are normally distributed—have been responsible for their undoing.\u003c\/p\u003e \u003cp\u003eFinancial crises and black swan events may not be precisely predictable by models, but improving the reliability and flexibility of those models is essential for both financial practitioners and academics intent on limiting the impact of major market crashes.\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eFinancial Models with Lévy Processes and Volatility Clustering\u003c\/i\u003e, authors Svetlozar Rachev, Young Shin Kim, Michele Leonardo Bianchi, and Frank Fabozzi focus on the application of non-normal distributions for modeling the behavior of stock price returns. Opening with a brief introduction to the basics of probability distributions, this practical resource quickly moves on to:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAddress a wide array of methods for the simulation of infinitely divisible distributions and Lévy processes with a view toward option pricing.\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eDiscuss two approaches to deal with non-normal multivariate distributions, providing insight into portfolio allocation assuming a multi-tail t distribution and a non-Gaussian multivariate model.\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eExamine discrete time option pricing models with volatility clustering—namely non-Gaussian GARCH models.\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eProvide guidance on pricing American options with Monte Carlo methods.\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIf you want to gain a better understanding of how financial models can be used to capture the dynamics of economic and financial variables, \u003ci\u003eFinancial Models with Lévy Processes and Volatility Clustering\u003c\/i\u003e is the best place to start.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989211332837,"sku":"NP9780470482353","price":110.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470482353.jpg?v=1761783224","url":"https:\/\/k12savings.com\/es\/products\/financial-models-with-levy-processes-and-volatility-clustering-isbn-9780470482353","provider":"K12savings","version":"1.0","type":"link"}