Nonlinear filtering in stochastic volatility models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Nonlinear filtering in stochastic volatility models PDF full book. Access full book title Nonlinear filtering in stochastic volatility models by . Download full books in PDF and EPUB format.
Author:
Publisher:
ISBN:
Category :
Languages : da
Pages :
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category :
Languages : da
Pages :
Get Book Here
Book Description
Author: Silvia Popa
Publisher:
ISBN: 9781109532661
Category : Filters (Mathematics)
Languages : en
Pages : 100
Get Book Here
Book Description
Filtering deals with recursive estimation of signals from their noisy measurements. A typical setup consists of a Markov process, which cannot be observed directly and is to be "filtered"from the trajectory of another process, related to it statistically. The general idea is to seek a "best estimate"for the true value of the signal, given only some (potentially noisy) observations of that signal. The optimal estimate is given by the conditional expectation and can be generated by a recursive equation, called the filtering equation, driven by the observation process. If the signal/observation model is linear and Gaussian, the filtering problem is called the Kalman-Bucy filter, otherwise is called a nonlinear filter. Being of considerable practical importance in engineering and economics, the filtering theory poses many interesting mathematical problems and it utilizes areas of mathematics such as stochastic calculus, martingales, etc. This thesis focuses on the mathematical aspects of nonlinear filtering for the case when the signal is a jump-diffusion process, i.e. a stochastic process that involves jumps and diffusion. One important objective of the thesis is to describe the evolution of the conditional distribution characterizing the optimal nonlinear filter using a stochastic differential equation known as the Zakai equation. The main contributions of the research are the moment estimates of the multi-dimensional jump-diffusion process which represent the signal in the nonlinear filtering problem, and a new approach for the uniqueness of the measure-valued solution of the stochastic differential equation that describes the evolution of the optimal filter. Applications of the nonlinear filtering theory to financial economics estimation problems including stochastic volatility models are discussed.
Author: Adam Clements
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 20
Get Book Here
Book Description
Author: Jitendra R. Raol
Publisher: CRC Press
ISBN: 1498745180
Category : Technology & Engineering
Languages : en
Pages : 581
Get Book Here
Book Description
Nonlinear Filtering covers linear and nonlinear filtering in a comprehensive manner, with appropriate theoretic and practical development. Aspects of modeling, estimation, recursive filtering, linear filtering, and nonlinear filtering are presented with appropriate and sufficient mathematics. A modeling-control-system approach is used when applicable, and detailed practical applications are presented to elucidate the analysis and filtering concepts. MATLAB routines are included, and examples from a wide range of engineering applications - including aerospace, automated manufacturing, robotics, and advanced control systems - are referenced throughout the text.
Author: Chris Kirby
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Linear filtering techniques are used to develop a quasi maximum likelihood estimator for asymmetric stochastic volatility models. The estimator is straightforward to implement and performs well in Monte Carlo experiments.
Author: Shelton Peiris
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This paper is concerned with filtering for various types of time series models including the class of generalized ARCH models and stochastic volatility models. We extend the results of Thavaneswaran and Abraham (1988) for some time series models using martingale estimating functions. Nonlinear filtering for biostatistical time series models with censored observations is also discussed as a special case.
Author: Ramaprasad Bhar
Publisher: World Scientific
ISBN: 9814304859
Category : Business & Economics
Languages : en
Pages : 354
Get Book Here
Book Description
This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in the context of different market segments. Although no prior knowledge in this area is required, the reader is expected to have knowledge of probability theory as well as a general mathematical aptitude. Its simple presentation of complex algorithms required to solve modeling problems in increasingly sophisticated financial markets makes this book particularly valuable as a reference for graduate students and researchers interested in the field. Furthermore, it analyses the model estimation results in the context of the market and contrasts these with contemporary research publications. It is also suitable for use as a text for graduate level courses on stochastic modeling.
Author: S.K. Mitter
Publisher: Springer
ISBN: 3540394311
Category : Mathematics
Languages : en
Pages : 310
Get Book Here
Book Description
Author: Sueo Sugimoto
Publisher: Ohmsha, Ltd.
ISBN: 4274805026
Category : Mathematics
Languages : en
Pages : 457
Get Book Here
Book Description
This book covers a broad range of filter theories, algorithms, and numerical examples. The representative linear and nonlinear filters such as the Kalman filter, the steady-state Kalman filter, the H infinity filter, the extended Kalman filter, the Gaussian sum filter, the statistically linearized Kalman filter, the unscented Kalman filter, the Gaussian filter, the cubature Kalman filter are first visited. Then, the non-Gaussian filters such as the ensemble Kalman filter and the particle filters based on the sequential Bayesian filter and the sequential importance resampling are described, together with their recent advances. Moreover, the information matrix in the nonlinear filtering, the nonlinear smoother based on the Markov Chain Monte Carlo, the continuous-discrete filters, factorized filters, and nonlinear filters based on stochastic approximation method are detailed. 1 Review of the Kalman Filter and Related Filters 2 Information Matrix in Nonlinear Filtering 3 Extended Kalman Filter and Gaussian Sum Filter 4 Statistically Linearized Kalman Filter 5 The Unscented Kalman Filter 6 General Gaussian Filters and Applications 7 The Ensemble Kalman Filter 8 Particle Filter 9 Nonlinear Smoother with Markov Chain Monte Carlo 10 Continuous-Discrete Filters 11 Factorized Filters 12 Nonlinear Filters Based on Stochastic Approximation Method
Author: Peyman Setoodeh
Publisher: John Wiley & Sons
ISBN: 1118835816
Category : Technology & Engineering
Languages : en
Pages : 308
Get Book Here
Book Description
NONLINEAR FILTERS Discover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource Nonlinear Filters: Theory and Applications delivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learning-based filtering algorithms. Readers of Nonlinear Filters will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy: Organization that allows the book to act as a stand-alone, self-contained reference A thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines A profound account of Bayesian filters including Kalman filter and its variants as well as particle filter A rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values A concise tutorial on deep learning and reinforcement learning A detailed presentation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation Guidelines for constructing nonparametric Bayesian models from parametric ones Perfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, Nonlinear Filters: Theory and Applications will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.
