Numerical Analysis Of Stochastic Volatility Jump Diffusion Models

Numerical Analysis Of Stochastic Volatility Jump Diffusion Models PDF Author: Abdelilah Jraifi
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659564895
Category :
Languages : en
Pages : 104

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Book Description
In the modern economic world, the options contracts are used because they allow to hedge against the vagaries and risks refers to fluctuations in the prices of the underlying assets. The determination of the price of these contracts is of great importance for investors.We are interested in problems of options pricing, actually the European and Quanto options on a financial asset. The price of that asset is modeled by a multi-dimentional jump diffusion with stochastic volatility. Otherwise, the first model considers the volatility as a continuous process and the second model considers it as a jump process. Finally in the 3rd model, the underlying asset is without jump and volatility follows a model CEV without jump. This model allow better to take into account some phenomena observed in the markets. We develop numerical methods that determine the values of prices for these options. We first write the model as an integro-differential stochastic equations system "EIDS," of which we study existence and unicity of solutions. Then we relate the resolution of PIDE to the computation of the option value.

Numerical Analysis Of Stochastic Volatility Jump Diffusion Models

Numerical Analysis Of Stochastic Volatility Jump Diffusion Models PDF Author: Abdelilah Jraifi
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659564895
Category :
Languages : en
Pages : 104

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Book Description
In the modern economic world, the options contracts are used because they allow to hedge against the vagaries and risks refers to fluctuations in the prices of the underlying assets. The determination of the price of these contracts is of great importance for investors.We are interested in problems of options pricing, actually the European and Quanto options on a financial asset. The price of that asset is modeled by a multi-dimentional jump diffusion with stochastic volatility. Otherwise, the first model considers the volatility as a continuous process and the second model considers it as a jump process. Finally in the 3rd model, the underlying asset is without jump and volatility follows a model CEV without jump. This model allow better to take into account some phenomena observed in the markets. We develop numerical methods that determine the values of prices for these options. We first write the model as an integro-differential stochastic equations system "EIDS," of which we study existence and unicity of solutions. Then we relate the resolution of PIDE to the computation of the option value.

Applied Stochastic Processes and Control for Jump-Diffusions

Applied Stochastic Processes and Control for Jump-Diffusions PDF Author: Floyd B. Hanson
Publisher: SIAM
ISBN: 9780898718638
Category : Mathematics
Languages : en
Pages : 472

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Book Description
This self-contained, practical, entry-level text integrates the basic principles of applied mathematics, applied probability, and computational science for a clear presentation of stochastic processes and control for jump diffusions in continuous time. The author covers the important problem of controlling these systems and, through the use of a jump calculus construction, discusses the strong role of discontinuous and nonsmooth properties versus random properties in stochastic systems.

Numerical Solution of Stochastic Differential Equations with Jumps in Finance

Numerical Solution of Stochastic Differential Equations with Jumps in Finance PDF Author: Eckhard Platen
Publisher: Springer Science & Business Media
ISBN: 364213694X
Category : Mathematics
Languages : en
Pages : 868

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Book Description
In financial and actuarial modeling and other areas of application, stochastic differential equations with jumps have been employed to describe the dynamics of various state variables. The numerical solution of such equations is more complex than that of those only driven by Wiener processes, described in Kloeden & Platen: Numerical Solution of Stochastic Differential Equations (1992). The present monograph builds on the above-mentioned work and provides an introduction to stochastic differential equations with jumps, in both theory and application, emphasizing the numerical methods needed to solve such equations. It presents many new results on higher-order methods for scenario and Monte Carlo simulation, including implicit, predictor corrector, extrapolation, Markov chain and variance reduction methods, stressing the importance of their numerical stability. Furthermore, it includes chapters on exact simulation, estimation and filtering. Besides serving as a basic text on quantitative methods, it offers ready access to a large number of potential research problems in an area that is widely applicable and rapidly expanding. Finance is chosen as the area of application because much of the recent research on stochastic numerical methods has been driven by challenges in quantitative finance. Moreover, the volume introduces readers to the modern benchmark approach that provides a general framework for modeling in finance and insurance beyond the standard risk-neutral approach. It requires undergraduate background in mathematical or quantitative methods, is accessible to a broad readership, including those who are only seeking numerical recipes, and includes exercises that help the reader develop a deeper understanding of the underlying mathematics.

Financial Modelling with Jump Processes

Financial Modelling with Jump Processes PDF Author: Peter Tankov
Publisher: CRC Press
ISBN: 1135437947
Category : Business & Economics
Languages : en
Pages : 552

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Book Description
WINNER of a Riskbook.com Best of 2004 Book Award! During the last decade, financial models based on jump processes have acquired increasing popularity in risk management and option pricing. Much has been published on the subject, but the technical nature of most papers makes them difficult for nonspecialists to understand, and the mathematic

Numerical Solution of Jump-diffusion Stochastic Differential Equations

Numerical Solution of Jump-diffusion Stochastic Differential Equations PDF Author: Gerald Teng
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Jump-diffusion processes are widely used in finance, economics, and other areas. They serve as models for asset, commodity and energy prices, interest and exchange rates, and the timing of corporate and sovereign defaults. The distributions of jump-diffusions are rarely analytically tractable, so Monte Carlo simulation methods are often used to treat the pricing, risk management, and statistical estimation problems arising in applications of jump-diffusion models. The first chapter is based on a paper that is joint work with Yexiang Wei. The chapter develops, analyzes and tests a discretization scheme for jump-diffusion processes with general state-dependent drift, volatility, jump intensity, and jump size. The scheme allows for an unbounded jump intensity, a feature of many standard jump-diffusion models in finance, economics, and other disciplines. It constructs the jump times as time-changed Poisson arrival times, and generates the process between the jump epochs using Euler discretization. Under technical conditions on the coefficient functions of the jump-diffusion, the convergence of the discretization error is proved to be of weak order arbitrarily close to one. The second chapter develops, analyzes and tests several methods for improving the computational efficiency of simulating jump-diffusions. The methods are applicable to simulation algorithms that discretize the Brownian component while using a standard Poisson process to generate the jump times, and whose weak order of convergence for the discretization error is known. We propose variance reduction methods based on nested simulation and antithetic variates, as well as methods for improving the efficiency of Richardson extrapolation techniques. We also investigate simulation efficiency improvements based on multilevel Monte Carlo methods. Numerical experiments demonstrate the methods give significant improvements to simulation efficiency.

Numerical Methods in Finance

Numerical Methods in Finance PDF Author: L. C. G. Rogers
Publisher: Cambridge University Press
ISBN: 9780521573542
Category : Business & Economics
Languages : en
Pages : 348

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Book Description
Numerical Methods in Finance describes a wide variety of numerical methods used in financial analysis.

Option Prices in Stochastic Volatility Models

Option Prices in Stochastic Volatility Models PDF Author: Giulia Terenzi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
We study option pricing problems in stochastic volatility models. In the first part of this thesis we focus on American options in the Heston model. We first give an analytical characterization of the value function of an American option as the unique solution of the associated (degenerate) parabolic obstacle problem. Our approach is based on variational inequalities in suitable weighted Sobolev spaces and extends recent results of Daskalopoulos and Feehan (2011, 2016) and Feehan and Pop (2015). We also investigate the properties of the American value function. In particular, we prove that, under suitable assumptions on the payoff, the value function is nondecreasing with respect to the volatility variable. Then, we focus on an American put option and we extend some results which are well known in the Black and Scholes world. In particular, we prove the strict convexity of the value function in the continuation region, some properties of the free boundary function, the Early Exercise Price formula and a weak form of the smooth fit principle. This is done mostly by using probabilistic techniques.In the second part we deal with the numerical computation of European and American option prices in jump-diffusion stochastic volatility models. We first focus on the Bates-Hull-White model, i.e. the Bates model with a stochastic interest rate. We consider a backward hybrid algorithm which uses a Markov chain approximation (in particular, a “multiple jumps” tree) in the direction of the volatility and the interest rate and a (deterministic) finite-difference approach in order to handle the underlying asset price process. Moreover, we provide a simulation scheme to be used for Monte Carlo evaluations. Numerical results show the reliability and the efficiency of the proposed methods.Finally, we analyze the rate of convergence of the hybrid algorithm applied to general jump-diffusion models. We study first order weak convergence of Markov chains to diffusions under quite general assumptions. Then, we prove the convergence of the algorithm, by studying the stability and the consistency of the hybrid scheme, in a sense that allows us to exploit the probabilistic features of the Markov chain approximation.

Topics in Numerical Methods for Finance

Topics in Numerical Methods for Finance PDF Author: Mark Cummins
Publisher: Springer
ISBN: 9781461434344
Category : Mathematics
Languages : en
Pages : 204

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Book Description
Presenting state-of-the-art methods in the area, the book begins with a presentation of weak discrete time approximations of jump-diffusion stochastic differential equations for derivatives pricing and risk measurement. Using a moving least squares reconstruction, a numerical approach is then developed that allows for the construction of arbitrage-free surfaces. Free boundary problems are considered next, with particular focus on stochastic impulse control problems that arise when the cost of control includes a fixed cost, common in financial applications. The text proceeds with the development of a fear index based on equity option surfaces, allowing for the measurement of overall fear levels in the market. The problem of American option pricing is considered next, applying simulation methods combined with regression techniques and discussing convergence properties. Changing focus to integral transform methods, a variety of option pricing problems are considered. The COS method is practically applied for the pricing of options under uncertain volatility, a method developed by the authors that relies on the dynamic programming principle and Fourier cosine series expansions. Efficient approximation methods are next developed for the application of the fast Fourier transform for option pricing under multifactor affine models with stochastic volatility and jumps. Following this, fast and accurate pricing techniques are showcased for the pricing of credit derivative contracts with discrete monitoring based on the Wiener-Hopf factorisation. With an energy theme, a recombining pentanomial lattice is developed for the pricing of gas swing contracts under regime switching dynamics. The book concludes with a linear and nonlinear review of the arbitrage-free parity theory for the CDS and bond markets.

Handbook of Computational and Numerical Methods in Finance

Handbook of Computational and Numerical Methods in Finance PDF Author: Svetlozar T. Rachev
Publisher: Springer Science & Business Media
ISBN: 0817681809
Category : Mathematics
Languages : en
Pages : 438

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Book Description
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. The book is designed for the academic community and will also serve professional investors.

Modelling and Simulation of Stochastic Volatility in Finance

Modelling and Simulation of Stochastic Volatility in Finance PDF Author: Christian Kahl
Publisher: Universal-Publishers
ISBN: 1581123833
Category : Business & Economics
Languages : en
Pages : 219

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Book Description
The famous Black-Scholes model was the starting point of a new financial industry and has been a very important pillar of all options trading since. One of its core assumptions is that the volatility of the underlying asset is constant. It was realised early that one has to specify a dynamic on the volatility itself to get closer to market behaviour. There are mainly two aspects making this fact apparent. Considering historical evolution of volatility by analysing time series data one observes erratic behaviour over time. Secondly, backing out implied volatility from daily traded plain vanilla options, the volatility changes with strike. The most common realisations of this phenomenon are the implied volatility smile or skew. The natural question arises how to extend the Black-Scholes model appropriately. Within this book the concept of stochastic volatility is analysed and discussed with special regard to the numerical problems occurring either in calibrating the model to the market implied volatility surface or in the numerical simulation of the two-dimensional system of stochastic differential equations required to price non-vanilla financial derivatives. We introduce a new stochastic volatility model, the so-called Hyp-Hyp model, and use Watanabe's calculus to find an analytical approximation to the model implied volatility. Further, the class of affine diffusion models, such as Heston, is analysed in view of using the characteristic function and Fourier inversion techniques to value European derivatives.