Regression Models For Ordinal Valued Time Series With Application To High Frequency Financial Data
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Regression Models For Ordinal Valued Time Series With Application To High Frequency Financial Data
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Author : Gernot Müller
language : en
Publisher:
Release Date : 2002
Regression Models For Ordinal Valued Time Series With Application To High Frequency Financial Data written by Gernot Müller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.
Regression Models For Ordinal Valued Time Series
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Author :
language : en
Publisher:
Release Date : 2007
Regression Models For Ordinal Valued Time Series written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Price changes arising in high-frequency financial data usually take on only values which are integer multiples of a certain amount, for example multiples of one sixteenth of a dollar. Therefore, the price changes represent an ordinal valued time series. Many of the common models cannot take this feature into account while also covering other features of such time series such as the dependency on covariates. Here two new models for ordinal valued time series with covariates are introduced. The first can be considered as an autoregressive extension of the common ordered probit model, the second as a discretized version of a stochastic volatility model. We investigate whether the estimation of the model parameters can be done by Markov Chain Monte Carlo (MCMC) methods. It is shown that in both cases standard MCMC algorithms have bad convergence properties. Therefore two grouped move multigrid Monte Carlo (GM-MGMC) samplers are developed which estimate the parameters accurate and fast. By applying both models to intraday data from the IBM stock at the New York Stock Exchange interesting dependencies of the price changes on covariates are detected and quantified. Implementations of the GM-MGMC samplers in C++ are provided.
Regression Models For Ordinal Valued Time Series
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Author : Gernot Müller
language : en
Publisher:
Release Date : 2004
Regression Models For Ordinal Valued Time Series written by Gernot Müller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Regression Modeling With Actuarial And Financial Applications
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Author : Edward W. Frees
language : en
Publisher: Cambridge University Press
Release Date : 2010
Regression Modeling With Actuarial And Financial Applications written by Edward W. Frees and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Business & Economics categories.
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Regression Models For Discrete Valued Time Series Data
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Author : Bernhard Klingenberg
language : en
Publisher:
Release Date : 2004
Regression Models For Discrete Valued Time Series Data written by Bernhard Klingenberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Testing For The Existence Of A Latent Process And Autocorrelation In The Poisson Regression Model For Count Data With Application To Ultra High Frequency Financial Time Series
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Author :
language : en
Publisher:
Release Date : 2008
Testing For The Existence Of A Latent Process And Autocorrelation In The Poisson Regression Model For Count Data With Application To Ultra High Frequency Financial Time Series written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Time Series Models
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Author : Andrew C. Harvey
language : en
Publisher: Financial Times/Prentice Hall
Release Date : 1993
Time Series Models written by Andrew C. Harvey and has been published by Financial Times/Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Time-series analysis categories.
A companion volume to The Econometric Analysis of Time series, this book focuses on the estimation, testing and specification of dynamic models which are not based on any behavioural theory. It covers univariate and multivariate time series and emphasizes autoregressive moving-average processes.
Predictions In Time Series Using Regression Models
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Author : Frantisek Stulajter
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Predictions In Time Series Using Regression Models written by Frantisek Stulajter and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Mathematics categories.
This book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people who apply time series theory to practical problems in their work and also serve as a textbook for postgraduate students in statistics economics and related subjects.
Modeling Financial Time Series With S Plus
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Author : Eric Zivot
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Modeling Financial Time Series With S Plus written by Eric Zivot and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Business & Economics categories.
The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. This is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This Second Edition is updated to cover S+FinMetrics 2.0 and includes new chapters on copulas, nonlinear regime switching models, continuous-time financial models, generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is an employee of Ronin Capital LLC. He received a Ph.D. in Economics from the University of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centre.
Functional Data Based Inference For High Frequency Financial Data
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Author : Bahaeddine Taoufik
language : en
Publisher:
Release Date : 2016
Functional Data Based Inference For High Frequency Financial Data written by Bahaeddine Taoufik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
This thesis is concerned with developing new functional data techniques for high frequency financial applications. Chapter 1 of the thesis introduces Functional Data Analysis (FDA) with examples of application to real data. In this chapter, we provide some theoretical foundations for FDA. We also present a general theory and basic properties of reproducing kernel Hilbert spaces (RKHS). Chapter 2 of the thesis explores the relationship between market returns and a number of financial factors by fitting functional regression models. We establish two estimation procedures based on the least squares and generalized least squares methods. We also present four hypothesis testing procedures on the functional regression coefficients based on the squared integral $L^2$ approach and the PCA approach for both least squares and generalized least squares methods. New asymptotic results are established allowing for minor departures from stationarity, to ensure convergence and asymptotic normality of our estimates. Our functional regression model is applied to cross-section returns data. Our data application results indicate a positive correlation between the volatility factor ``FVIX" and the higher returns and a negative correlation between the volatility factor ``FVIX" and the low and middle returns.Chapter 3 of the thesis develops a nonlinear function-on-function model using RKHS for real-valued functions. We establish the minimax rate of convergence of the excess prediction risk. Our simulation studies faced computational challenges due to the complexity of the estimation procedure. We examine the prediction performance accuracy of our model through a simulation study. Our nonlinear function-function model is applied to Cumulative intraday return (CIDR) data in order to investigate the prediction performance of Standard \& Poor's 500 Index (S\&P 500) and the Dow Jones Industrial Average (DJIA) for General Electric Company (GE) and International Business Machines Corp.(IBM) for the three periods defining the crisis: ``Before," `` During," and `` After''.