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Bayesian Inference Of Asymmetric Stochastic Conditional Duration Models
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Book Synopsis Bayesian Inference of Asymmetric Stochastic Conditional Duration Models by : Zhongxian Men
Download or read book Bayesian Inference of Asymmetric Stochastic Conditional Duration Models written by Zhongxian Men and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper extends a stochastic conditional duration (SCD) model for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process with the aim of improving the statistical fit of the model. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to t the resulting SCD model under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD model in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed model and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allows comparison of our model and method to those existing in the literature.
Book Synopsis Bayesian Inference of Multiscale Stochastic Conditional Duration Models by : Tony S. Wirjanto
Download or read book Bayesian Inference of Multiscale Stochastic Conditional Duration Models written by Tony S. Wirjanto and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we revisit the notion that a single factor of duration running on single time scale is adequate to capture the dynamics of the duration process of financial transaction data. The documented poor fit of the left tail of the marginal distribution of the observed durations in some existing one-factor stochastic duration models may be indicative of the possible existence of multiple stochastic duration factors running on different time scales. This paper proposes multiscale stochastic conditional duration (MSCD) models to describe the dynamics of duration of financial transaction data. Suitable algorithms of MCMC are developed to fit the resulting MSCD models under three distributional assumptions about the innovation of the measurement equation. Simulation studies suggest that our proposed models and methods result in improved in-sample fits as well as improved duration forecasts. Applications of our models and methods to two duration data sets of FIAT and IBM indicate the existence of at least two factors governing the dynamics of the duration of the stock transactions.
Book Synopsis Bayesian Analysis of the Stochastic Conditional Duration Model by : Chris M. Strickland
Download or read book Bayesian Analysis of the Stochastic Conditional Duration Model written by Chris M. Strickland and published by . This book was released on 2003 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymmetric Stochastic Conditional Duration Model :a Mixture of Normals Approach by : Dinghai Xu
Download or read book Asymmetric Stochastic Conditional Duration Model :a Mixture of Normals Approach written by Dinghai Xu and published by . This book was released on 2008 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Financial Econometrics by : Yiu-Kuen Tse
Download or read book Financial Econometrics written by Yiu-Kuen Tse and published by MDPI. This book was released on 2019-10-14 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial econometrics has developed into a very fruitful and vibrant research area in the last two decades. The availability of good data promotes research in this area, specially aided by online data and high-frequency data. These two characteristics of financial data also create challenges for researchers that are different from classical macro-econometric and micro-econometric problems. This Special Issue is dedicated to research topics that are relevant for analyzing financial data. We have gathered six articles under this theme.
Book Synopsis Bayesian Inference for Stochastic Processes by : Lyle D. Broemeling
Download or read book Bayesian Inference for Stochastic Processes written by Lyle D. Broemeling and published by CRC Press. This book was released on 2017-12-12 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.
Book Synopsis Bayesian Time Series Models by : David Barber
Download or read book Bayesian Time Series Models written by David Barber and published by Cambridge University Press. This book was released on 2011-08-11 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
Book Synopsis Bayesian Analysis of Stochastic Process Models by : David Insua
Download or read book Bayesian Analysis of Stochastic Process Models written by David Insua and published by John Wiley & Sons. This book was released on 2012-05-07 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.
Book Synopsis Stochastic Volatility and Realized Stochastic Volatility Models by : Makoto Takahashi
Download or read book Stochastic Volatility and Realized Stochastic Volatility Models written by Makoto Takahashi and published by Springer Nature. This book was released on 2023-04-18 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management.
Book Synopsis Bayesian Multivariate Time Series Methods for Empirical Macroeconomics by : Gary Koop
Download or read book Bayesian Multivariate Time Series Methods for Empirical Macroeconomics written by Gary Koop and published by Now Publishers Inc. This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.
Book Synopsis Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling by : Ivan Jeliazkov
Download or read book Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling written by Ivan Jeliazkov and published by Emerald Group Publishing. This book was released on 2019-10-18 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume 40B of Advances in Econometrics examines innovations in stochastic frontier analysis, nonparametric and semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression.
Book Synopsis Bayesian Analysis of Linear Models by : Lyle D. Broemeling
Download or read book Bayesian Analysis of Linear Models written by Lyle D. Broemeling and published by . This book was released on 1985 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Inference and Decision Techniques by : P. K. Goel
Download or read book Bayesian Inference and Decision Techniques written by P. K. Goel and published by North Holland. This book was released on 1986 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary objective of this volume is to describe the impact of Professor Bruno de Finetti's contributions on statistical theory and practice, and to provide a selection of recent and applied research in Bayesian statistics and econometrics. Included are papers (all previously unpublished) from leading econometricians and statisticians from several countries. Part I of this book relates most directly to de Finetti's interests whilst Part II deals specifically with the implications of the assumption of finitely additive probability. Parts III & IV discuss applications of Bayesian methodology in econometrics and economic forecasting, and Part V examines assessment of prior parameters in specific parametric setting and foundational issues in probability assessment. The following section deals with state of the art for comparing probability functions and gives an assessment of prior distributions and utility functions. In Parts VII & VIII are a collection of papers on Bayesian methodology for general linear models and time series analysis (the most often used tools in economic modelling), and papers relevant to modelling and forecasting. The remaining two Parts examine, respectively, optimality considerations and the effectiveness of the Conditionality-Likelihood Principle as a vehicle to convince the non-Bayesians about the usefulness of the Bayesian paradigm.
Book Synopsis Bayesian Econometrics by : Siddhartha Chib
Download or read book Bayesian Econometrics written by Siddhartha Chib and published by Emerald Group Publishing. This book was released on 2008-12-18 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.
Book Synopsis Bayesian Forecasting and Dynamic Models by : Mike West
Download or read book Bayesian Forecasting and Dynamic Models written by Mike West and published by Springer. This book was released on 1999-03-26 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory of classes of dynamic models and their uses in forecasting and time series analysis. The principles, models and methods of Bayesian forecasting and time - ries analysis have been developed extensively during the last thirty years. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland statistical aspects of forecasting models and related techniques. With this has come experience with applications in a variety of areas in commercial, industrial, scienti?c, and socio-economic ?elds. Much of the technical - velopment has been driven by the needs of forecasting practitioners and applied researchers. As a result, there now exists a relatively complete statistical and mathematical framework, presented and illustrated here. In writing and revising this book, our primary goals have been to present a reasonably comprehensive view of Bayesian ideas and methods in m- elling and forecasting, particularly to provide a solid reference source for advanced university students and research workers.
Book Synopsis Bayesian Inference of State Space Models by : Kostas Triantafyllopoulos
Download or read book Bayesian Inference of State Space Models written by Kostas Triantafyllopoulos and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering. Over the past years there has been a growing literature on Bayesian inference of state space models, focusing on multivariate models as well as on non-linear and non-Gaussian models. The availability of time series data in many fields of science and industry on the one hand, and the development of low-cost computational capabilities on the other, have resulted in a wealth of statistical methods aimed at parameter estimation and forecasting. This book brings together many of these methods, presenting an accessible and comprehensive introduction to state space models. A number of data sets from different disciplines are used to illustrate the methods and show how they are applied in practice. The R package BTSA, created for the book, includes many of the algorithms and examples presented. The book is essentially self-contained and includes a chapter summarising the prerequisites in undergraduate linear algebra, probability and statistics. An up-to-date and complete account of state space methods, illustrated by real-life data sets and R code, this textbook will appeal to a wide range of students and scientists, notably in the disciplines of statistics, systems engineering, signal processing, data science, finance and econometrics. With numerous exercises in each chapter, and prerequisite knowledge conveniently recalled, it is suitable for upper undergraduate and graduate courses.
Book Synopsis Bayesian Analysis of a Threshold Stochastic Volatility Model by : Tony S. Wirjanto
Download or read book Bayesian Analysis of a Threshold Stochastic Volatility Model written by Tony S. Wirjanto and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution in which the mean innovation switches between two regimes. In our model, the threshold is treated as an unknown parameter. We show that the proposed threshold SV model not only can capture the time-varying volatility of returns, but also can accommodate the asymmetric shape of conditional distribution of the returns. Parameter estimation is carried out by using Markov Chain Monte Carlo methods. For model selection and volatility forecast, an auxiliary particle filter technique is employed to approximate the filter and prediction distributions of the returns. Several experiments are conducted to assess the robustness of the proposed model and estimation methods. In the empirical study, we apply our threshold SV model to three return time series. The empirical analysis results show that the threshold parameter has a nonzero value and the mean innovations belong to two separately distinct regimes. We also find that the model with an unknown threshold parameter value consistently outperforms the model with a known threshold parameter value.
