Asymptotic Parameter Estimation Theory For Stochastic Differential Equations PDF Download
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Author | : Jaya P. N. Bishwal |
Publisher | : Springer |
Total Pages | : 271 |
Release | : 2007-09-26 |
Genre | : Mathematics |
ISBN | : 3540744487 |
Download Parameter Estimation in Stochastic Differential Equations Book in PDF, ePub and Kindle
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
Author | : Raphael Abel Kasonga |
Publisher | : National Library of Canada |
Total Pages | : 190 |
Release | : 1986 |
Genre | : |
ISBN | : |
Download Asymptotic Parameter Estimation Theory for Stochastic Differential Equations [microform] Book in PDF, ePub and Kindle
Author | : Raphael Abel Kasonga |
Publisher | : |
Total Pages | : 0 |
Release | : 1986 |
Genre | : Estimation theory |
ISBN | : |
Download Asymptotic Parameter Estimation Theory for Stochastic Differential Equations Book in PDF, ePub and Kindle
Author | : Jaya P. N. Bishwal |
Publisher | : Springer Nature |
Total Pages | : 634 |
Release | : 2022-08-06 |
Genre | : Mathematics |
ISBN | : 3031038614 |
Download Parameter Estimation in Stochastic Volatility Models Book in PDF, ePub and Kindle
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.
Author | : A. V. Skorokhod |
Publisher | : American Mathematical Soc. |
Total Pages | : 339 |
Release | : 2009-01-07 |
Genre | : Mathematics |
ISBN | : 9780821846865 |
Download Asymptotic Methods in the Theory of Stochastic Differential Equations Book in PDF, ePub and Kindle
Written by one of the foremost Soviet experts in the field, this book is intended for specialists in the theory of random processes and its applications. The author's 1982 monograph on stochastic differential equations, written with Iosif Ilich Gikhman, did not include a number of topics important to applications. The present work begins to fill this gap by investigating the asymptotic behavior of stochastic differential equations. The main topics are ergodic theory for Markov processes and for solutions of stochastic differential equations, stochastic differential equations containing a small parameter, and stability theory for solutions of systems of stochastic differential equations.
Author | : Kęstutis Kubilius |
Publisher | : Springer |
Total Pages | : 403 |
Release | : 2018-01-04 |
Genre | : Mathematics |
ISBN | : 3319710303 |
Download Parameter Estimation in Fractional Diffusion Models Book in PDF, ePub and Kindle
This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is “white,” i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides simple and suitable parameter estimation methods in these models, making it a valuable resource for all researchers in this field. The book is addressed to specialists and researchers in the theory and statistics of stochastic processes, practitioners who apply statistical methods of parameter estimation, graduate and post-graduate students who study mathematical modeling and statistics.
Author | : A. V. Skorokhod |
Publisher | : American Mathematical Soc. |
Total Pages | : 362 |
Release | : 2009-01-07 |
Genre | : Mathematics |
ISBN | : 9780821898253 |
Download Asymptotic Methods in the Theory of Stochastic Differential Equations Book in PDF, ePub and Kindle
Ergodic theorems: General ergodic theorems Densities for transition probabilities and resolvents for Markov solutions of stochastic differential equations Ergodic theorems for one-dimensional stochastic equations Ergodic theorems for solutions of stochastic equations in $R^d$ Asymptotic behavior of systems of stochastic equations containing a small parameter: Equations with a small right-hand side Processes with rapid switching Averaging over variables for systems of stochastic differential equations Stability. Linear systems: Stability of sample paths of homogeneous Markov processes Linear equations in $R^d$ and the stochastic semigroups connected with them. Stability Stability of solutions of stochastic differential equations Linear stochastic equations in Hilbert space. Stochastic semigroups. Stability: Linear equations with bounded coefficients Strong stochastic semigroups with second moments Stability Bibliography
Author | : Yu. A. Kutoyants |
Publisher | : |
Total Pages | : 224 |
Release | : 1984 |
Genre | : Parameter estimation |
ISBN | : |
Download Parameter Estimation for Stochastic Processes Book in PDF, ePub and Kindle
Author | : Riccardo Cesari |
Publisher | : |
Total Pages | : 48 |
Release | : 1989 |
Genre | : Diffusion processes |
ISBN | : |
Download On the Estimation of Stochastic Differential Equations Book in PDF, ePub and Kindle
Author | : Yuri Kabanov |
Publisher | : Springer Science & Business Media |
Total Pages | : 288 |
Release | : 2003 |
Genre | : Language Arts & Disciplines |
ISBN | : 9783540653325 |
Download Two-Scale Stochastic Systems Book in PDF, ePub and Kindle
Two-scale systems described by singularly perturbed SDEs have been the subject of ample literature. However, this new monograph develops subjects that were rarely addressed and could be given the collective description "Stochastic Tikhonov-Levinson theory and its applications." The book provides a mathematical apparatus designed to analyze the dynamic behaviour of a randomly perturbed system with fast and slow variables. In contrast to the deterministic Tikhonov-Levinson theory, the basic model is described in a more realistic way by stochastic differential equations. This leads to a number of new theoretical questions but simultaneously allows us to treat in a unified way a surprisingly wide spectrum of applications like fast modulations, approximate filtering, and stochastic approximation.Two-scale systems described by singularly perturbed SDEs have been the subject of ample literature. However, this new monograph develops subjects that were rarely addressed and could be given the collective description "Stochastic Tikhonov-Levinson theory and its applications." The book provides a mathematical apparatus designed to analyze the dynamic behaviour of a randomly perturbed system with fast and slow variables. In contrast to the deterministic Tikhonov-Levinson theory, the basic model is described in a more realistic way by stochastic differential equations. This leads to a number of new theoretical questions but simultaneously allows us to treat in a unified way a surprisingly wide spectrum of applications like fast modulations, approximate filtering, and stochastic approximation.