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:

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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.

Advances in Time Series Methods and Applications
Advances in Time Series Methods and Applications

Author: Wai Keung Li

Publisher: Springer

Published: 2016-12-02

Total Pages: 298

ISBN-13: 1493965689

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This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.

Copulae in Mathematical and Quantitative Finance
Copulae in Mathematical and Quantitative Finance

Author: Piotr Jaworski

Publisher: Springer Science & Business Media

Published: 2013-06-18

Total Pages: 299

ISBN-13: 3642354076

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Copulas are mathematical objects that fully capture the dependence structure among random variables and hence offer great flexibility in building multivariate stochastic models. Since their introduction in the early 1950s, copulas have gained considerable popularity in several fields of applied mathematics, especially finance and insurance. Today, copulas represent a well-recognized tool for market and credit models, aggregation of risks, and portfolio selection. Historically, the Gaussian copula model has been one of the most common models in credit risk. However, the recent financial crisis has underlined its limitations and drawbacks. In fact, despite their simplicity, Gaussian copula models severely underestimate the risk of the occurrence of joint extreme events. Recent theoretical investigations have put new tools for detecting and estimating dependence and risk (like tail dependence, time-varying models, etc) in the spotlight. All such investigations need to be further developed and promoted, a goal this book pursues. The book includes surveys that provide an up-to-date account of essential aspects of copula models in quantitative finance, as well as the extended versions of talks selected from papers presented at the workshop in Cracow.

STATISTICAL ANALYSIS OF HIGH F
STATISTICAL ANALYSIS OF HIGH F

Author: 彭國永

Publisher: Open Dissertation Press

Published: 2017-01-27

Total Pages: 92

ISBN-13: 9781374749641

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This dissertation, "Statistical Analysis of High Frequency Data Using Autoregressive Conditional Duration Models" by 彭國永, Kwok-wing, Pang, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b3122504 Subjects: Autoregression (Statistics) Time-series analysis - Econometric models

Research Papers in Statistical Inference for Time Series and Related Models
Research Papers in Statistical Inference for Time Series and Related Models

Author: Yan Liu

Publisher: Springer Nature

Published: 2023-05-31

Total Pages: 591

ISBN-13: 9819908035

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This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.

Detecting Autoregressive Conditional Heteroskedasticity in Non-Gaussian Time Series
Detecting Autoregressive Conditional Heteroskedasticity in Non-Gaussian Time Series

Author: Burkhard Raunig

Publisher:

Published: 2005

Total Pages: 26

ISBN-13:

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In economic time series conditional heteroskedasticity and conditional non-normality may occur simultaneously. Well known examples include time series of financial returns. The present paper examines a new test for (generalized) autoregressive conditional heteroskedasticity in Monte Carlo experiments with normal, fat-tailed and/or skewed conditional distributions. In the experiments the size of the new test is accurate and the test has good power against the considered ARCH and GARCH alternatives. Under conditional normality the test is as powerful as the standard Lagrange multiplier test for ARCH effects and a robust ARCH test. Under conditional non-normality the new test has often substantially more power then the other two tests.

Time-Series Forecasting
Time-Series Forecasting

Author: Chris Chatfield

Publisher: CRC Press

Published: 2000-10-25

Total Pages: 281

ISBN-13: 1420036203

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From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space

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:

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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.