Parameter Estimation In Stochastic Volatility Models Via Approximate Bayesian Computing
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Parameter Estimation In Stochastic Volatility Models Via Approximate Bayesian Computing
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Author : Achal Awasthi
language : en
Publisher:
Release Date : 2018
Parameter Estimation In Stochastic Volatility Models Via Approximate Bayesian Computing written by Achal Awasthi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Bayesian statistical decision theory categories.
In this thesis, we propose a generalized Heston model as a tool to estimate volatility. We have used Approximate Bayesian Computing to estimate the parameters of the generalized Heston model. This model was used to examine the daily closing prices of the Shanghai Stock Exchange and the NIKKEI 225 indices. We found that this model was a good fit for shorter time periods around financial crisis. For longer time periods, this model failed to capture the volatility in detail.
Handbook Of Approximate Bayesian Computation
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Author : Scott A. Sisson
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Handbook Of Approximate Bayesian Computation written by Scott A. Sisson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Mathematics categories.
As the world becomes increasingly complex, so do the statistical models required to analyse the challenging problems ahead. For the very first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and application of ABC methods. These simple, but powerful statistical techniques, take Bayesian statistics beyond the need to specify overly simplified models, to the setting where the model is defined only as a process that generates data. This process can be arbitrarily complex, to the point where standard Bayesian techniques based on working with tractable likelihood functions would not be viable. ABC methods finesse the problem of model complexity within the Bayesian framework by exploiting modern computational power, thereby permitting approximate Bayesian analyses of models that would otherwise be impossible to implement. The Handbook of ABC provides illuminating insight into the world of Bayesian modelling for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and implementing ABC techniques to analyse complex models in the modern world.
Parameter Estimation In Stochastic Volatility Models
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Author : Jaya P. N. Bishwal
language : en
Publisher: Springer Nature
Release Date : 2022-08-06
Parameter Estimation In Stochastic Volatility Models written by Jaya P. N. Bishwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-06 with Mathematics categories.
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.
Stochastic Volatility
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Author : Neil Shephard
language : en
Publisher: OUP Oxford
Release Date : 2005-03-10
Stochastic Volatility written by Neil Shephard and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-03-10 with Business & Economics categories.
Stochastic volatility is the main concept used in the fields of financial economics and mathematical finance to deal with time-varying volatility in financial markets. This book brings together some of the main papers that have influenced the field of the econometrics of stochastic volatility, and shows that the development of this subject has been highly multidisciplinary, with results drawn from financial economics, probability theory, and econometrics, blending to produce methods and models that have aided our understanding of the realistic pricing of options, efficient asset allocation, and accurate risk assessment. A lengthy introduction by the editor connects the papers with the literature.
Parameter Estimation In Stochastic Differential Equations
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Author : Jaya P. N. Bishwal
language : en
Publisher: Springer
Release Date : 2007-09-26
Parameter Estimation In Stochastic Differential Equations written by Jaya P. N. Bishwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-26 with Mathematics categories.
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.
Quantifying Uncertainty In Subsurface Systems
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Author : Céline Scheidt
language : en
Publisher: John Wiley & Sons
Release Date : 2018-04-27
Quantifying Uncertainty In Subsurface Systems written by Céline Scheidt and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-27 with Science categories.
Under the Earth’s surface is a rich array of geological resources, many with potential use to humankind. However, extracting and harnessing them comes with enormous uncertainties, high costs, and considerable risks. The valuation of subsurface resources involves assessing discordant factors to produce a decision model that is functional and sustainable. This volume provides real-world examples relating to oilfields, geothermal systems, contaminated sites, and aquifer recharge. Volume highlights include: • A multi-disciplinary treatment of uncertainty quantification • Case studies with actual data that will appeal to methodology developers • A Bayesian evidential learning framework that reduces computation and modeling time Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians. Read the Editors’ Vox: https://eos.org/editors-vox/quantifying-uncertainty-about-earths-resources
Bayesian Inference In The Social Sciences
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Author : Ivan Jeliazkov
language : en
Publisher: John Wiley & Sons
Release Date : 2014-11-04
Bayesian Inference In The Social Sciences written by Ivan Jeliazkov and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-04 with Mathematics categories.
Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
Handbook Of Parallel Computing And Statistics
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Author : Erricos John Kontoghiorghes
language : en
Publisher: CRC Press
Release Date : 2005-12-21
Handbook Of Parallel Computing And Statistics written by Erricos John Kontoghiorghes and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-21 with Computers categories.
Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts
The Oxford Handbook Of Bayesian Econometrics
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Author : John Geweke
language : en
Publisher: Oxford University Press
Release Date : 2011-09-29
The Oxford Handbook Of Bayesian Econometrics written by John Geweke and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-29 with Business & Economics categories.
Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology.
Computational Economics Heterogeneous Agent Modeling
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Author : Cars Hommes
language : en
Publisher: Elsevier
Release Date : 2018-06-27
Computational Economics Heterogeneous Agent Modeling written by Cars Hommes and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-27 with Business & Economics categories.
Handbook of Computational Economics: Heterogeneous Agent Modeling, Volume Four, focuses on heterogeneous agent models, emphasizing recent advances in macroeconomics (including DSGE), finance, empirical validation and experiments, networks and related applications. Capturing the advances made since the publication of Volume Two (Tesfatsion & Judd, 2006), it provides high-level literature with sections devoted to Macroeconomics, Finance, Empirical Validation and Experiments, Networks, and other applications, including Innovation Diffusion in Heterogeneous Populations, Market Design and Electricity Markets, and a final section on Perspectives on Heterogeneity. - Helps readers fully understand the dynamic properties of realistically rendered economic systems - Emphasizes detailed specifications of structural conditions, institutional arrangements and behavioral dispositions - Provides broad assessments that can lead researchers to recognize new synergies and opportunities