Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Control theory --- Jump processes --- Linear systems --- Control theory --- Jump processes

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Stochastic processes with jumps and random measures are importance as drivers in applications like financial mathematics and signal processing. This 2002 text develops stochastic integration theory for both integrators (semimartingales) and random measures from a common point of view. Using some novel predictable controlling devices, the author furnishes the theory of stochastic differential equations driven by them, as well as their stability and numerical approximation theories. Highlights feature DCT and Egoroff's Theorem, as well as comprehensive analogs results from ordinary integration theory, for instance previsible envelopes and an algorithm computing stochastic integrals of càglàd integrands pathwise. Full proofs are given for all results, and motivation is stressed throughout. A large appendix contains most of the analysis that readers will need as a prerequisite. This will be an invaluable reference for graduate students and researchers in mathematics, physics, electrical engineering and finance who need to use stochastic differential equations.

Stochastic integrals. --- Jump processes. --- Integrals, Stochastic --- Stochastic analysis --- Processes, Jump --- Markov processes --- Jump processes

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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 mathematical tools required for applications can be intimidating. Potential users often get the impression that jump and Levy processes are beyond their reach. Financial Modelling with Jump Processes shows that this is not so. It provides a self-contained overview of the theoretical, numerical, and empirical aspects involved in using jump processes in financial modelling, and it does so in terms within the grasp of nonspecialists. The introduction of new mathematical tools is motivated by their use in the modelling process, and precise mathematical statements of results are accompanied by intuitive explanations.Topics covered in this book include: jump-diffusion models, Levy processes, stochastic calculus for jump processes, pricing and hedging in incomplete markets, implied volatility smiles, time-inhomogeneous jump processes and stochastic volatility models with jumps. The authors illustrate the mathematical concepts with many numerical and empirical examples and provide the details of numerical implementation of pricing and calibration algorithms. This book demonstrates that the concepts and tools necessary for understanding and implementing models with jumps can be more intuitive that those involved in the Black Scholes and diffusion models. If you have even a basic familiarity with quantitative methods in finance, Financial Modelling with Jump Processes will give you a valuable new set of tools for modelling market fluctuations.

Stochastic processes --- Finance --- Jump processes. --- Finances --- Processus de sauts --- Mathematical models. --- Modèles mathématiques --- Jump processes --- Mathematical models --- mathematische modellen, toegepast op economie --- stochastische modellen --- opties --- risk management --- -Jump processes --- 332.01519233 --- Processes, Jump --- Markov processes --- Funding --- Funds --- Economics --- Currency question --- Modèles mathématiques --- Finance - Mathematical models

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This monograph is a concise introduction to the stochastic calculus of variations (also known as Malliavin calculus) for processes with jumps. It is written for researchers and graduate students who are interested in Malliavin calculus for jump processes. In this book "processes with jumps" includes both pure jump processes and jump-diffusions. The author provides many results on this topic in a self-contained way; this also applies to stochastic differential equations (SDEs) "with jumps". The book also contains some applications of the stochastic calculus for processes with jumps to the control theory and mathematical finance. Namely, asymptotic expansions functionals related with financial assets of jump-diffusion are provided based on the theory of asymptotic expansion on the Wiener-Poisson space. Solving the Hamilton-Jacobi-Bellman (HJB) equation of integro-differential type is related with solving the classical Merton problem and the Ramsey theory. The field of jump processes is nowadays quite wide-ranging, from the Lévy processes to SDEs with jumps. Recent developments in stochastic analysis have enabled us to express various results in a compact form. Up to now, these topics were rarely discussed in a monograph. Contents: Preface Preface to the second edition Introduction Lévy processes and Itô calculus Perturbations and properties of the probability law Analysis of Wiener-Poisson functionals Applications Appendix Bibliography List of symbols Index

Malliavin calculus. --- Calculus of variations. --- Jump processes. --- Stochastic processes. --- Random processes --- Probabilities --- Processes, Jump --- Markov processes --- Isoperimetrical problems --- Variations, Calculus of --- Maxima and minima --- Calculus, Malliavin --- Stochastic analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Electronic books. -- local. --- Jump processes. --- Stochastic differential equations. --- Stochastic differential equations --- Jump processes --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Processes, Jump --- Mathematics. --- Applied mathematics. --- Engineering mathematics. --- Economics, Mathematical. --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Applications of Mathematics. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Quantitative Finance. --- Markov processes --- Differential equations --- Fokker-Planck equation --- Distribution (Probability theory. --- Finance. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Funding --- Funds --- Economics --- Currency question --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Statistics . --- Economics, Mathematical . --- Mathematical economics --- Engineering --- Engineering analysis --- Mathematical analysis --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Methodology