Palgrave Handbook of Econometrics

Palgrave Handbook of Econometrics

Author: Terence C. Mills

Publisher: Palgrave Handbook of Econometr

Published: 2009-06-25

Total Pages: 1432

ISBN-13:

DOWNLOAD EBOOK

Palgrave Handbooks of Econometrics comprises 'landmark' essays by the world's leading scholars and provides authoritative guidance in key areas of econometrics. With definitive contributions on the subject, the Handbook is an essential source for reference for professional econometricians, economists, researchers and students. Following the successful Palgrave Handbook of Econometrics: Volume 1, this second volume brings together leading academics working in econometrics today and explores applied econometrics. Volume 2 contains contributions on subjects including growth/development econometrics, computing, microeconomics, macroeconomics, finance, spatial and urban economics and international economics.


Information Spillover Effect and Autoregressive Conditional Duration Models

Information Spillover Effect and Autoregressive Conditional Duration Models

Author: Xiangli Liu

Publisher: Routledge

Published: 2014-07-11

Total Pages: 229

ISBN-13: 1317667662

DOWNLOAD EBOOK

This book studies the information spillover among financial markets and explores the intraday effect and ACD models with high frequency data. This book also contributes theoretically by providing a new statistical methodology with comparative advantages for analyzing comovements between two time series. It explores this new method by testing the information spillover between the Chinese stock market and the international market, futures market and spot market. Using the high frequency data, this book investigates the intraday effect and examines which type of ACD model is particularly suited in capturing financial duration dynamics. The book will be of invaluable use to scholars and graduate students interested in comovements among different financial markets and financial market microstructure and to investors and regulation departments looking to improve their risk management.


Information Spillover Effect and Autoregressive Conditional Duration Models

Information Spillover Effect and Autoregressive Conditional Duration Models

Author: Xiangli Liu

Publisher: Routledge

Published: 2014-07-11

Total Pages: 216

ISBN-13: 1317667654

DOWNLOAD EBOOK

This book studies the information spillover among financial markets and explores the intraday effect and ACD models with high frequency data. This book also contributes theoretically by providing a new statistical methodology with comparative advantages for analyzing comovements between two time series. It explores this new method by testing the information spillover between the Chinese stock market and the international market, futures market and spot market. Using the high frequency data, this book investigates the intraday effect and examines which type of ACD model is particularly suited in capturing financial duration dynamics. The book will be of invaluable use to scholars and graduate students interested in comovements among different financial markets and financial market microstructure and to investors and regulation departments looking to improve their risk management.


Detecting Misspecifications in Autoregressive Conditional Duration Models and Non-Negative Time-Series Processes

Detecting Misspecifications in Autoregressive Conditional Duration Models and Non-Negative Time-Series Processes

Author: Yongmiao Hong

Publisher:

Published: 2011

Total Pages: 0

ISBN-13:

DOWNLOAD EBOOK

We develop a general theory to test correct specification of multiplicative error models of non-negative time-series processes, which include the popular autoregressive conditional duration (ACD) models. Both linear and nonlinear conditional expectation models are covered, and standardized innovations can have time-varying conditional dispersion and higher-order conditional moments of unknown form. No specific estimation method is required, and the tests have a convenient null asymptotic N(0,1) distribution. To reduce the impact of parameter estimation uncertainty in finite samples, we adopt Wooldridge's (1990a) device to our context and justify its validity. Simulation studies show that in the context of testing ACD models, finite sample correction gives better sizes in finite samples and are robust to parameter estimation uncertainty. And, it is important to take into account time-varying conditional dispersion and higher-order conditional moments in standardized innovations; failure to do so can cause strong overrejection of a correctly specified ACD model. The proposed tests have reasonable power against a variety of popular linear and nonlinear ACD alternatives.


Nonparametric Estimation and Testing in Semiparametric Autoregressive Conditional Duration Models

Nonparametric Estimation and Testing in Semiparametric Autoregressive Conditional Duration Models

Author: Pipat Wongsaart

Publisher:

Published: 2011

Total Pages: 346

ISBN-13:

DOWNLOAD EBOOK

The advent of the so-called transaction data in finance has given econometrician the tool to address a variety of issues surrounding the structure of the trading process and/or price discovery in nancial markets. However, transaction data pose a number of unique econometric challenges that do not easily fit into the traditional modeling framework that have been developed so far in the literature. The ultimate goal of this thesis is to establish a novel econometric method of estimating the conditional intensity of the arrival times of financial events. This goal can be broken down into a few research objectives. (1) Firstly, it is to establish a new generation (semiparametric) approach to efficiently model the dynamics of the waiting time between the arrivals of financial events or what is commonly known as duration. (2) Secondly, it is to derive a set of estimators, so that empirical estimates of the density, survival and the baseline intensity functions associated with duration processes can be calculated. (3) Thirdly, it is to develop a novel testing procedure to test the marginal density function of financial durations. While the first and second objectives are discussed in detail in Chapter 2, the third objective is considered in Chapter 3. These semiparametric estimation and nonparametric testing procedure are introduced in conjunction with the detailed theoretical and experimental examinations of their statistical validity. Furthermore, the usefulness and practicability of these methods are illustrated using various datasests from both foreign exchange and international stock markets.


Forecasting Transaction Rates

Forecasting Transaction Rates

Author: Robert F. Engle

Publisher:

Published: 1994

Total Pages: 64

ISBN-13:

DOWNLOAD EBOOK

This paper will propose a new statistical model for the analysis of data that does not arrive in equal time intervals such as financial transactions data, telephone calls, or sales data on commodities that are tracked electronically. In contrast to fixed interval analysis, the model treats the time between observation arrivals as a stochastic time varying process and therefore is in the spirit of the models of time deformation initially proposed by Tauchen and Pitts (1983), Clark (1973) and more recently discussed by Stock (1988), Lamoureux and Lastrapes (1992), Muller et al. (1990) and Ghysels and Jasiak (1994) but does not require auxiliary data or assumptions on the causes of time flow. Strong evidence is provided for duration clustering beyond a deterministic component for the financial transactions data analyzed. We will show that a very simple version of the model can successfully account for the significant autocorrelations in the observed durations between trades of IBM stock on the consolidated market. A simple transformation of the duration data allows us to include volume in the model.