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Calculation Of Volatility In A Jump Diffusion Model
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Book Synopsis Calculation of Volatility in a Jump-Diffusion Model by : Javier F. Navas
Download or read book Calculation of Volatility in a Jump-Diffusion Model written by Javier F. Navas and published by . This book was released on 2007 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: A common way to incorporate discontinuities in asset returns is to add a Poisson process to a Brownian motion. The jump-diffusion process provides probability distributions that typically fit market data better than those of the simple diffusion process. To compare the performance of these models in option pricing, the total volatility of the jump-diffusion process must be used in the Black-Scholes formula. A number of authors, including Merton (1976a amp; b), Ball and Torous (1985), Jorion (1988), and Amin (1993), miscalculate this volatility because they do not include the effect of uncertainty over the jump size. We calculate the volatility correctly and show how this affects option prices.
Book Synopsis Estimation of Volatilities Under a Merton's Jump-diffusion Model and an Uncertain Volatility Model by : Changhong He
Download or read book Estimation of Volatilities Under a Merton's Jump-diffusion Model and an Uncertain Volatility Model written by Changhong He and published by . This book was released on 2005 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Hull and White Formula for a General Stochastic Volatility Jump-diffusion Model with Applications to the Study of the Short-time Behavior of the Implied Volatility by : Elisa Alós
Download or read book A Hull and White Formula for a General Stochastic Volatility Jump-diffusion Model with Applications to the Study of the Short-time Behavior of the Implied Volatility written by Elisa Alós and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Financial Modelling with Jump Processes by : Peter Tankov
Download or read book Financial Modelling with Jump Processes written by Peter Tankov and published by CRC Press. This book was released on 2003-12-30 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Applied Stochastic Control of Jump Diffusions by : Bernt Øksendal
Download or read book Applied Stochastic Control of Jump Diffusions written by Bernt Øksendal and published by Springer Science & Business Media. This book was released on 2007-04-26 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here is a rigorous introduction to the most important and useful solution methods of various types of stochastic control problems for jump diffusions and its applications. Discussion includes the dynamic programming method and the maximum principle method, and their relationship. The text emphasises real-world applications, primarily in finance. Results are illustrated by examples, with end-of-chapter exercises including complete solutions. The 2nd edition adds a chapter on optimal control of stochastic partial differential equations driven by Lévy processes, and a new section on optimal stopping with delayed information. Basic knowledge of stochastic analysis, measure theory and partial differential equations is assumed.
Book Synopsis Estimation of Jump-diffusion Processes Via Empirical Characteristic Functions by : Maria Semenova
Download or read book Estimation of Jump-diffusion Processes Via Empirical Characteristic Functions written by Maria Semenova and published by . This book was released on 2006 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Approximation and Calibration of Short-term Implied Volatilities Under Jump-diffusion Stochastic Volatility by : Alexey Medvedev
Download or read book Approximation and Calibration of Short-term Implied Volatilities Under Jump-diffusion Stochastic Volatility written by Alexey Medvedev and published by . This book was released on 2006 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Encyclopedia of Finance by : Cheng-Few Lee
Download or read book Encyclopedia of Finance written by Cheng-Few Lee and published by Springer Science & Business Media. This book was released on 2006-07-27 with total page 861 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a major new reference work covering all aspects of finance. Coverage includes finance (financial management, security analysis, portfolio management, financial markets and instruments, insurance, real estate, options and futures, international finance) and statistical applications in finance (applications in portfolio analysis, option pricing models and financial research). The project is designed to attract both an academic and professional market. It also has an international approach to ensure its maximum appeal. The Editors' wish is that the readers will find the encyclopedia to be an invaluable resource.
Book Synopsis Jump Diffusion and Stochastic Volatility Models in Securities Pricing by : Mthuli Ncube
Download or read book Jump Diffusion and Stochastic Volatility Models in Securities Pricing written by Mthuli Ncube and published by . This book was released on 2012 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Jump-diffusion Model with Stochastic Volatility and Durations by : Wei Wei
Download or read book A Jump-diffusion Model with Stochastic Volatility and Durations written by Wei Wei and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbooks in Operations Research and Management Science: Financial Engineering by : John R. Birge
Download or read book Handbooks in Operations Research and Management Science: Financial Engineering written by John R. Birge and published by Elsevier. This book was released on 2007-11-16 with total page 1026 pages. Available in PDF, EPUB and Kindle. Book excerpt: The remarkable growth of financial markets over the past decades has been accompanied by an equally remarkable explosion in financial engineering, the interdisciplinary field focusing on applications of mathematical and statistical modeling and computational technology to problems in the financial services industry. The goals of financial engineering research are to develop empirically realistic stochastic models describing dynamics of financial risk variables, such as asset prices, foreign exchange rates, and interest rates, and to develop analytical, computational and statistical methods and tools to implement the models and employ them to design and evaluate financial products and processes to manage risk and to meet financial goals. This handbook describes the latest developments in this rapidly evolving field in the areas of modeling and pricing financial derivatives, building models of interest rates and credit risk, pricing and hedging in incomplete markets, risk management, and portfolio optimization. Leading researchers in each of these areas provide their perspective on the state of the art in terms of analysis, computation, and practical relevance. The authors describe essential results to date, fundamental methods and tools, as well as new views of the existing literature, opportunities, and challenges for future research.
Book Synopsis Jump-Diffusion Processes by : Leif B. G. Andersen
Download or read book Jump-Diffusion Processes written by Leif B. G. Andersen and published by . This book was released on 1999 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: The standard approach (e.g. Dupire (1994) and Rubinstein (1994)) to fitting stock processes to observed option prices models the underlying stock price as a one-factor diffusion process with state- and time-dependent volatility. While this approach is attractive in the sense that market completeness is maintained, the resulting model is often highly non-stationary, difficult to fit to steep volatility smiles, and generally is not well supported by empirical evidence. In this paper, we attempt to overcome some of these problems by overlaying the diffusion dynamics with a jump-process, effectively assuming that a large part of the observed volatility smiles can be explained by fear of sudden large market movements (quot;crash-o-phobiaquot;). The first part of this paper derives a forward PIDE (Partial Integro-Differential Equation) satisfied by European call option prices and demonstrates how the resulting equation can be used to fit the model to the observed volatility smile/skew. In the second part of the paper, we discuss efficient methods of applying the calibrated model to the pricing of contingent claims. In particular, we develop an ADI (Alternating Directions Implicit) finite difference method that is shown to be unconditionally stable and, if combined with FFT (Fast Fourier Transform) methods, computationally efficient. The paper also discusses the usage of Monte Carlo methods, and contains several detailed examples from the Samp;P500 market. We compare pricing results obtained by the jump-diffusion approach with those of pure diffusion, and find significant differences for a range of popular contracts.
Book Synopsis Stochastic Modeling of Stock Prices Incorporating Jump Diffusion and Shot Noise Models by : Daniel Janocha
Download or read book Stochastic Modeling of Stock Prices Incorporating Jump Diffusion and Shot Noise Models written by Daniel Janocha and published by GRIN Verlag. This book was released on 2016-08-01 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2016 in the subject Mathematics - Stochastics, grade: 1,7, Technical University of Darmstadt (Forschungsgebiet Stochastik), course: Mathematik - Finanzmathematik, language: English, abstract: In this thesis, we present a stochastic model for stock prices incorporating jump diffusion and shot noise models based on the work of Altmann, Schmidt and Stute ("A Shot Noise Model For Financial Assets") and on its continuation by Schmidt and Stute ("Shot noise processes and the minimal martingale measure"). These papers differ in modeling the decay of the jump effect: Whereas it is deterministic in the first paper, it is stochastic in the last paper. In general, jump effects exist because of overreaction due to news in the press, due to illiquidity or due to incomplete information, i.e. because certain information are available only to few market participants. In financial markets, jump effects fade away as time passes: On the one hand, if the stock price falls, new investors are motivated to buy the stock. On the other hand, a rise of the stock price may lead to profit-taking, i.e. some investors sell the stock in order to lock in gains. Shot noise models are based on Merton's jump diffusion models where the decline of the jump effect after a price jump is neglected. In contrast to jump diffusion models, shot noise models respect the decay of jump effects. In complete markets, the so-called equivalent martingale measure is used to price European options and for hedging. Since stock price models incorporating jumps describe incomplete markets, the equivalent martingale measure cannot be determined uniquely. Hence, in this thesis, we deduce the so-called equivalent minimal martingale measure, both in discrete and continuous time. In contrast to Merton's jump diffusion models and to the well-known pricing model of Black and Scholes, the presented shot noise models are able to reproduce volatility smile effects which can be observed in financial markets.
Book Synopsis Application of Stochastic Volatility Models in Option Pricing by : Pascal Debus
Download or read book Application of Stochastic Volatility Models in Option Pricing written by Pascal Debus and published by GRIN Verlag. This book was released on 2013-09-09 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bachelorarbeit aus dem Jahr 2010 im Fachbereich BWL - Investition und Finanzierung, Note: 1,2, EBS Universität für Wirtschaft und Recht, Sprache: Deutsch, Abstract: The Black-Scholes (or Black-Scholes-Merton) Model has become the standard model for the pricing of options and can surely be seen as one of the main reasons for the growth of the derivative market after the model ́s introduction in 1973. As a consequence, the inventors of the model, Robert Merton, Myron Scholes, and without doubt also Fischer Black, if he had not died in 1995, were awarded the Nobel prize for economics in 1997. The model, however, makes some strict assumptions that must hold true for accurate pricing of an option. The most important one is constant volatility, whereas empirical evidence shows that volatility is heteroscedastic. This leads to increased mispricing of options especially in the case of out of the money options as well as to a phenomenon known as volatility smile. As a consequence, researchers introduced various approaches to expand the model by allowing the volatility to be non-constant and to follow a sto-chastic process. It is the objective of this thesis to investigate if the pricing accuracy of the Black-Scholes model can be significantly improved by applying a stochastic volatility model.
Book Synopsis Parameter Estimation in Stochastic Volatility Models by : Jaya P. N. Bishwal
Download or read book Parameter Estimation in Stochastic Volatility Models written by Jaya P. N. Bishwal and published by Springer Nature. This book was released on 2022-08-06 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
Book Synopsis A New Class of Stochastic Volatility Models with Jumps by : Mikhail Chernov
Download or read book A New Class of Stochastic Volatility Models with Jumps written by Mikhail Chernov and published by . This book was released on 2012 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is to propose a new class of jump diffusions which feature both stochastic volatility and random intensity jumps. Previous studies have focused primarily on pure jump processes with constant intensity and log-normal jumps or constant jump intensity combined with a one factor stochastic volatility model. We introduce several generalizations which can better accommodate several empirical features of returns data. In their most general form we introduce a class of processes which nests jump-diffusions previously considered in empirical work and includes the affine class of random intensity models studied by Bates (1998) and Duffie, Pan and Singleton (1998) but also allows for non-affine random intensity jump components. We attain the generality of our specification through a generic Levy process characterization of the jump component. The processes we introduce share the desirable feature with the affine class that they yield analytically tractable and explicit option pricing formula. The non-affine class of processes we study include specifications where the random intensity jump component depends on the size of the previous jump which represent an alternative to affine random intensity jump processes which feature correlation between the stochastic volatility and jump component. We also allow for and experiment with different empirical specifications of the jump size distributions. We use two types of data sets. One involves the Samp;P500 and the other comprises of 100 years of daily Dow Jones index. The former is a return series often used in the literature and allows us to compare our results with previous studies. The latter has the advantage to provide a long time series and enhances the possibility of estimating the jump component more precisely. The non-affine random intensity jump processes are more parsimonious than the affine class and appear to fit the data much better.
Book Synopsis Simulation-based Econometric Methods by : Christian Gouriéroux
Download or read book Simulation-based Econometric Methods written by Christian Gouriéroux and published by OUP Oxford. This book was released on 1997-01-09 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new generation of statistical econometrics. After linear models leading to analytical expressions for estimators, and non-linear models using numerical optimization algorithms, the availability of high- speed computing has enabled econometricians to consider econometric models without simple analytical expressions. The previous difficulties presented by the presence of integrals of large dimensions in the probability density functions or in the moments can be circumvented by a simulation-based approach. After a brief survey of classical parametric and semi-parametric non-linear estimation methods and a description of problems in which criterion functions contain integrals, the authors present a general form of the model where it is possible to simulate the observations. They then move to calibration problems and the simulated analogue of the method of moments, before considering simulated versions of maximum likelihood, pseudo-maximum likelihood, or non-linear least squares. The general principle of indirect inference is presented and is then applied to limited dependent variable models and to financial series.
