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Tap into the power of the most popular stochastic volatilitymodel for pricing equity derivatives Since its introduction in 1993, the Heston model has become apopular model for pricing equity derivatives, and the most popularstochastic volatility model in financial engineering. This vitalresource provides a thorough derivation of the original model, andincludes the most important extensions and refinements that haveallowed the model to produce option prices that are more accurateand volatility surfaces that better reflect market conditions. Thebook's material is drawn from research papers and many of themodels covered and the computer codes are unavailable from othersources. The book is light on theory and instead highlights theimplementation of the models. All of the models found here havebeen coded in Matlab and C#. This reliable resource offers anunderstanding of how the original model was derived from Ricattiequations, and shows how to implement implied and local volatility,Fourier methods applied to the model, numerical integrationschemes, parameter estimation, simulation schemes, Americanoptions, the Heston model with time-dependent parameters, finitedifference methods for the Heston PDE, the Greeks, and the doubleHeston model. A groundbreaking book dedicated to the exploration of theHeston model—a popular model for pricing equityderivatives Includes a companion website, which explores the Heston modeland its extensions all coded in Matlab and C# Written by Fabrice Douglas Rouah a quantitative analyst whospecializes in financial modeling for derivatives for pricing andrisk management Engaging and informative, this is the first book to dealexclusively with the Heston Model and includes code in Matlab andC# for pricing under the model, as well as code for parameterestimation, simulation, finite difference methods, Americanoptions, and more.
Practical options pricing for better-informed investmentdecisions. The Heston Model and Its Extensions in VBA is thedefinitive guide to options pricing using two of the derivativesindustry's most powerful modeling tools—the Heston model, andVBA. Light on theory, this extremely useful reference focuses onimplementation, and can help investors more efficiently—andaccurately—exploit market information to better informinvestment decisions. Coverage includes a description of the Hestonmodel, with specific emphasis on equity options pricing andvariance modeling, The book focuses not only on the original Hestonmodel, but also on the many enhancements and refinements that havebeen applied to the model, including methods that use the Fouriertransform, numerical integration schemes, simulation, methods forpricing American options, and much more. The companion websiteoffers pricing code in VBA that resides in an extensive set ofExcel spreadsheets. The Heston model is the derivatives industry's most popularstochastic volatility model for pricing equity derivatives. Thisbook provides complete guidance toward the successfulimplementation of this valuable model using the industry'subiquitous financial modeling software, giving users theunderstanding—and VBA code—they need to produce optionprices that are more accurate, and volatility surfaces that moreclosely reflect market conditions. Derivatives pricing is often the hinge on which profit is madeor lost in financial institutions, making accuracy of utmostimportance. This book will help risk managers, traders, portfoliomanagers, quants, academics and other professionals betterunderstand the Heston model and its extensions, in a writing stylethat is clear, concise, transparent and easy to understand. Forbetter pricing accuracy, The Heston Model and Its Extensions inVBA is a crucial resource for producing more accurate modeloutputs such as prices, hedge ratios, volatilities, and graphs.
Financial modelling Theory, Implementation and Practice with Matlab Source Jörg Kienitz and Daniel Wetterau Financial Modelling - Theory, Implementation and Practice with MATLAB Source is a unique combination of quantitative techniques, the application to financial problems and programming using Matlab. The book enables the reader to model, design and implement a wide range of financial models for derivatives pricing and asset allocation, providing practitioners with complete financial modelling workflow, from model choice, deriving prices and Greeks using (semi-) analytic and simulation techniques, and calibration even for exotic options. The book is split into three parts. The first part considers financial markets in general and looks at the complex models needed to handle observed structures, reviewing models based on diffusions including stochastic-local volatility models and (pure) jump processes. It shows the possible risk-neutral densities, implied volatility surfaces, option pricing and typical paths for a variety of models including SABR, Heston, Bates, Bates-Hull-White, Displaced-Heston, or stochastic volatility versions of Variance Gamma, respectively Normal Inverse Gaussian models and finally, multi-dimensional models. The stochastic-local-volatility Libor market model with time-dependent parameters is considered and as an application how to price and risk-manage CMS spread products is demonstrated. The second part of the book deals with numerical methods which enables the reader to use the models of the first part for pricing and risk management, covering methods based on direct integration and Fourier transforms, and detailing the implementation of the COS, CONV, Carr-Madan method or Fourier-Space-Time Stepping. This is applied to pricing of European, Bermudan and exotic options as well as the calculation of the Greeks. The Monte Carlo simulation technique is outlined and bridge sampling is discussed in a Gaussian setting and for Lévy processes. Computation of Greeks is covered using likelihood ratio methods and adjoint techniques. A chapter on state-of-the-art optimization algorithms rounds up the toolkit for applying advanced mathematical models to financial problems and the last chapter in this section of the book also serves as an introduction to model risk. The third part is devoted to the usage of Matlab, introducing the software package by describing the basic functions applied for financial engineering. The programming is approached from an object-oriented perspective with examples to propose a framework for calibration, hedging and the adjoint method for calculating Greeks in a Libor market model. Source code used for producing the results and analysing the models is provided on the author's dedicated website, http://www.mathworks.de/matlabcentral/fileexchange/authors/246981.
Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging provides the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. Topics are, amongst others, stylized facts of equity and options markets, risk-neutral valuation, Fourier transform methods, Monte Carlo simulation, model calibration, valuation and dynamic hedging. The financial models introduced in this book exhibit features like stochastic volatility, jump components and stochastic short rates. The approach is a practical one in that all important aspects are illustrated by a set of self-contained Python scripts. Benefits of Reading the Book: Data Analysis: Learn how to use Python for data and financial analysis. Reproduce major stylized facts of equity and options markets by yourself. Models: Learn risk-neutral pricing techniques from ground up, apply Fourier transform techniques to European options and advanced Monte Carlo pricing to American options. Simulation: Monte Carlo simulation is the most powerful and flexible numerical method for derivatives analytics. Simulate models with jumps, stochastic volatility and stochastic short rates. Calibration: Use global and local optimization techniques (incl. penalties) to calibrate advanced option pricing models to market quotes for options with different strikes and maturities. Hedging: Learn how to use advanced option pricing models in combination with advanced numerical methods to dynamically hedge American options. Python: All results, graphics, etc. presented are in general reproducible with the Python scripts accompanying the book. Benefit from more than 5,500 lines of code.