logo资料库

Econometrics of Financial High-Frequency Data..pdf

发布时间：2022-06-03 发布人：admin 分类：说明书 资料大小：7.12M 资料格式：pdf 举报 版权申诉
第1页 / 共378页
第2页 / 共378页
第3页 / 共378页
第4页 / 共378页
第5页 / 共378页
第6页 / 共378页
第7页 / 共378页
第8页 / 共378页
资料共378页，剩余部分请下载后查看
    000
    front-matter
    Econometrics of Financial High-Frequency Data
    Preface
    Contents
    001
    Chapter 1: Introduction
    1.1 Motivation
    1.2 Structure of the Book
    References
    002
    Chapter 2: Microstructure Foundations
    2.1 The Institutional Framework of Trading
    2.1.1 Types of Traders and Forms of Trading
    2.1.2 Types of Orders
    2.1.3 Market Structures
    2.1.3.1 Quote-Driven Dealer Markets
    2.1.3.2 Order-Driven Markets
    2.1.3.3 Brokered Markets
    2.1.4 Order Precedence and Pricing Rules
    2.1.5 Trading Forms at Selected International Exchanges
    2.1.5.1 The New York Stock Exchange (NYSE)
    2.1.5.2 NASDAQ
    2.1.5.3 XETRA
    2.1.5.4 Australian Stock Exchange
    2.2 A Review of Market Microstructure Theory
    2.2.1 Asymmetric Information Based Models
    2.2.1.1 Sequential Trade Models
    2.2.1.2 Strategic Trade Models
    2.2.2 Inventory Models
    2.2.3 Major Implications for Trading Variables
    2.2.4 Models for Limit Order Book Markets
    References
    003
    Chapter 3: Empirical Properties of High-Frequency Data
    3.1 Handling High-Frequency Data
    3.1.1 Databases and Trading Variables
    3.1.2 Matching Trades and Quotes
    3.1.3 Data Cleaning
    3.1.4 Split-Transactions
    3.1.5 Identification of Buyer- and Seller-Initiated Trades
    3.2 Aggregation by Trading Events: Financial Durations
    3.2.1 Trade and Order Arrival Durations
    3.2.2 Price and Volume Durations
    3.3 Properties of Financial Durations
    3.4 Properties of Trading Characteristics
    3.5 Properties of Time Aggregated Data
    3.6 Summary of Major Empirical Findings
    References
    004
    Chapter 4: Financial Point Processes
    4.1 Basic Concepts of Point Processes
    4.1.1 Fundamental Definitions
    4.1.1.1 Point Processes
    4.1.1.2 Counting Processes
    4.1.1.3 Durations and Backward Recurrence Times
    4.1.1.4 Filtrations and (Time-Varying) Covariates
    4.1.2 Compensators and Intensities
    4.1.3 The Homogeneous Poisson Process
    4.1.4 Generalizations of Poisson Processes
    4.1.5 A Random Time Change Argument
    4.1.6 Intensity-Based Inference
    4.1.7 Simulation and Diagnostics
    4.2 Four Ways to Model Point Processes
    4.2.1 Intensity Models
    4.2.2 Hazard Models
    4.2.2.1 Proportional Hazard (PH) Models
    4.2.2.2 Accelerated Failure Time Models
    4.2.3 Duration Models
    4.2.4 Count Data Models
    4.3 Censoring and Time-Varying Covariates
    4.3.1 Censoring
    4.3.2 Time-Varying Covariates
    4.4 An Outlook on Dynamic Extensions
    References
    005
    Chapter 5: Univariate Multiplicative Error Models
    5.1 ARMA Models for Log Variables
    5.2 A MEM for Durations: The ACD Model
    5.3 Estimation of the ACD Model
    5.3.1 QML Estimation
    5.3.2 ML Estimation
    5.4 Seasonalities and Explanatory Variables
    5.5 The Log-ACD Model
    5.6 Testing the ACD Model
    5.6.1 Portmanteau Tests
    5.6.2 Independence Tests
    5.6.3 Distribution Tests
    5.6.4 Lagrange Multiplier Tests
    5.6.5 Conditional Moment Tests
    5.6.5.1 Adapting Newey's Conditional Moment Test
    5.6.5.2 Integrated Conditional Moment Tests
    5.6.6 Monte Carlo Evidence
    References
    006
    Chapter 6: Generalized Multiplicative Error Models
    6.1 A Class of Augmented ACD Models
    6.1.1 Special Cases
    6.1.1.1 Additive and Multiplicative ACD (AMACD) Model
    6.1.1.2 Box–Cox ACD (BACD) Model
    6.1.1.3 EXponential ACD (EXACD) Model
    6.1.1.4 Augmented Box–Cox ACD (ABACD) Model
    6.1.1.5 Hentschel ACD (HACD) Model
    6.1.1.6 Augmented Hentschel ACD (AHACD) Model
    6.1.1.7 Spline News Impact ACD (SNIACD) Model
    6.1.2 Theoretical Properties
    6.1.3 Empirical Illustrations
    6.2 Regime-Switching ACD Models
    6.2.1 Threshold ACD Models
    6.2.2 Smooth Transition ACD Models
    6.2.3 Markov Switching ACD Models
    6.3 Long Memory ACD Models
    6.4 Mixture and Component Multiplicative Error Models
    6.4.1 The Stochastic Conditional Duration Model
    6.4.2 Stochastic Multiplicative Error Models
    6.4.3 Component Multiplicative Error Models
    6.5 Further Generalizations of Multiplicative Error Models
    6.5.1 Competing Risks ACD Models
    6.5.2 Semiparametric ACD Models
    6.5.3 Stochastic Volatility Duration Models
    References
    007
    Chapter 7: Vector Multiplicative Error Models
    7.1 VMEM Processes
    7.1.1 The Basic VMEM Specification
    7.1.2 Statistical Inference
    7.1.3 Applications
    7.2 Stochastic Vector Multiplicative Error Models
    7.2.1 Stochastic VMEM Processes
    7.2.2 Simulation-Based Inference
    7.2.3 Modelling Trading Processes
    References
    008
    Chapter 8: Modelling High-Frequency Volatility
    8.1 Intraday Quadratic Variation Measures
    8.1.1 Maximum Likelihood Estimation
    8.1.2 The Realized Kernel Estimator
    8.1.3 The Pre-averaging Estimator
    8.1.4 Empirical Evidence
    8.1.5 Modelling and Forecasting Intraday Variances
    8.2 Spot Variances and Jumps
    8.3 Trade-Based Volatility Measures
    8.4 Volatility Measurement Using Price Durations
    8.5 Modelling Quote Volatility
    References
    009
    Chapter 9: Estimating Market Liquidity
    9.1 Simple Spread and Price Impact Measures
    9.1.1 Spread Measures
    9.1.2 Price Impact Measures
    9.2 Volume Based Measures
    9.2.1 The VNET Measure
    9.2.2 Excess Volume Measures
    9.2.2.1 Determinants of Excess Volume Durations
    9.2.2.2 Measuring Realized Market Depth
    9.3 Modelling Order Book Depth
    9.3.1 A Cointegrated VAR Model for Quotes and Depth
    9.3.2 A Dynamic Nelson–Siegel Type Order Book Model
    9.3.3 A Semiparametric Dynamic Factor Model
    References
    010
    Chapter 10: Semiparametric Dynamic Proportional Hazard Models
    10.1 Dynamic Integrated Hazard Processes
    10.2 The Semiparametric ACPH Model
    10.3 Properties of the Semiparametric ACPH Model
    10.3.1 Autocorrelation Structure
    10.3.2 Estimation Quality
    10.4 Extended SACPH Models
    10.4.1 Regime-Switching Baseline Hazard Functions
    10.4.2 Censoring
    10.4.3 Unobserved Heterogeneity
    10.5 Testing the SACPH Model
    10.6 Estimating Volatility Using the SACPH Model
    10.6.1 Data and the Generation of Price Events
    10.6.2 Empirical Findings
    References
    011
    Chapter 11: Univariate Dynamic Intensity Models
    11.1 The Autoregressive Conditional Intensity Model
    11.2 Generalized ACI Models
    11.2.1 Long-Memory ACI Models
    11.2.2 An AFT-Type ACI Model
    11.2.3 A Component ACI Model
    11.2.4 Empirical Application
    11.3 Hawkes Processes
    References
    012
    Chapter 12: Multivariate Dynamic Intensity Models
    12.1 Multivariate ACI Models
    12.2 Applications of Multivariate ACI Models
    12.2.1 Estimating Simultaneous Buy/Sell Intensities
    12.2.2 Modelling Order Aggressiveness
    12.3 Multivariate Hawkes Processes
    12.3.1 Statistical Properties
    12.3.2 Estimating Multivariate Price Intensities
    12.4 Stochastic Conditional Intensity Processes
    12.4.1 Model Structure
    12.4.2 Probabilistic Properties of the SCI Model
    12.4.3 Statistical Inference
    12.5 SCI Modelling of Multivariate Price Intensities
    References
    013
    Chapter 13: Autoregressive Discrete Processes and Quote Dynamics
    13.1 Univariate Dynamic Count Data Models
    13.1.1 Autoregressive Conditional Poisson Models
    13.1.2 Extended ACP Models
    13.1.3 Empirical Illustrations
    13.2 Multivariate ACP Models
    13.3 A Simple Model for Transaction Price Dynamics
    13.4 Autoregressive Conditional Multinomial Models
    13.5 Autoregressive Models for Integer-Valued Variables
    13.6 Modelling Ask and Bid Quote Dynamics
    13.6.1 Cointegration Models for Ask and Bid Quotes
    13.6.2 Decomposing Quote Dynamics
    References
    014
    Appendix A: Important Distributions for Positive-Valued Data
    Poisson Distribution
    Negative Binomial Distribution
    Log-Normal Distribution
    Exponential Distribution
    Gamma Distribution
    Weibull Distribution
    Generalized Gamma Distribution
    Generalized F Distribution
    Burr Distribution
    Extreme Value Type I Distribution
    Burr Type II Distribution
    Pareto Distribution
    Index
    Nikolaus Hautsch Econometrics of Financial High-Frequency Data 123
    Professor Dr. Nikolaus Hautsch Institute for Statistics and Econometrics School of Business and Economics Humboldt-Universit¨at zu Berlin Spandauer Str. 1 10178 Berlin Germany nikolaus.hautsch@wiwi.hu-berlin.de ISBN 978-3-642-21924-5 DOI 10.1007/978-3-642-21925-2 Springer Heidelberg Dordrecht London New York e-ISBN 978-3-642-21925-2 Library of Congress Control Number: 2011938812 c Springer-Verlag Berlin Heidelberg 2012 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: eStudio Calamar S.L. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
    To Christiane, Elias and Emilia
    Preface This book is an extended revision of Modelling Irregularly Spaced Financial Data – Theory and Practice of Dynamic Duration Models (Hautsch 2004) which has been written as a doctoral dissertation at the Department of Economics at the University of Konstanz. Six years later, when I started thinking about a second edition of this book accounting for recent developments in the area of high-frequency finance, I realized that an extension of the scope of the book and the inclusion of more topics and material are inevitable. Given the developments in high-frequency finance, the number of new approaches and the current challenges induced by technological progress in market structures as well as in the trading industry, I decided to change the title of the book, to revise and restructure existing material and to include additional topics resulting in a new monography. Compared to Hautsch (2004), the list of topics has been extended, among others, by various types of univariate and multivariate multiplicative error models, autoregressive count data approaches, dynamic specifications for integer-valued variables as well as models for quote dynamics. Moreover, different approaches to quantify intraday volatility are discussed involving realized volatility measures, trade-based volatility concepts, and intensity-based measures. A further focus lies on the modeling of liquidity and order book dynamics. Finally, institutional settings, market structures, issues of data preparation, preprocessing, and implementation pitfalls as well as illustrations of the empirical properties of high-frequency data are discussed more extensively and thoroughly using updated data from trading at the New York Stock Exchange, NASDAQ and the Deutsche B¨orse. The book is intended for researchers interested in methods, approaches and applications in the area of high-frequency econometrics. Moreover, it is written for students and scholars covering this subject, for instance, in a course on financial econometrics, financial statistics, or empirical finance. Students using the book should have a basic knowledge in mathematical statistics, time series analysis, and econometric estimation theory. Finally, the book addresses the needs of financial practitioners who require statistical methods to model and predict high-frequency market processes as well as intraday volatility and liquidity dynamics. vii
    viii Preface Needless to say that a book focusing on a rapidly developing and growing field, such as high-frequency financial econometrics, is never complete and entirely up- to-date. Moreover, it is impossible to cover all specific topics, approaches, and applications. Furthermore, it is obvious that each topic could be addressed in more depth both from a methodological (and mathematical) viewpoint and from an applied side. In fact, some of the topics, such, for instance, the concept of realized volatility, are only touched without going into deep mathematical details. Therefore, I tried to find a compromise between elaborateness, compactness, and topical broadness. I wish to thank Axel Groß-Klußmann, Gustav Haitz, Ruihong Huang, Peter Malec, and Stefanie Schulz for helpful comments, proof reading, and editing work. Moreover, I am grateful to many colleagues, coworkers and coauthors for inspiring discussions and joint work building the basis for many aspects and topics covered in this book. In this context, I wish to express my special gratitude to Luc Bauwens, Wolfgang H¨ardle, Anthony Hall, Lada Kyj, Roel Oomen, Mark Podolskij, and Melanie Schienle. Last but not least, I would like to thank my family. I am exceptionally indebted to my wonderful wife Christiane. Without her love, support, and encouragement, this book could not have been written. I also wish to thank my children Elias and Emilia for providing refreshing and valuable distraction from research and writing a book. Berlin May 2011 Nikolaus Hautsch
    Contents 1 Introduction ................................................................. 1.1 Motivation ............................................................ 1.2 Structure of the Book ................................................ References .................................................................... 2.1 2 Microstructure Foundations............................................... The Institutional Framework of Trading ............................ Types of Traders and Forms of Trading ................... 2.1.1 2.1.2 Types of Orders ............................................. 2.1.3 Market Structures ........................................... 2.1.4 Order Precedence and Pricing Rules. ...................... 2.1.5 Trading Forms at Selected International Exchanges ...... A Review of Market Microstructure Theory ........................ Asymmetric Information Based Models................... 2.2.1 2.2.2 Inventory Models ........................................... 2.2.3 Major Implications for Trading Variables ................. 2.2.4 Models for Limit Order Book Markets .................... References .................................................................... 2.2 3 Empirical Properties of High-Frequency Data .......................... Handling High-Frequency Data ..................................... 3.1 3.1.1 Databases and Trading Variables .......................... 3.1.2 Matching Trades and Quotes ............................... Data Cleaning ............................................... 3.1.3 3.1.4 Split-Transactions .......................................... 3.1.5 Identification of Buyer- and Seller-Initiated Trades ...... Aggregation by Trading Events: Financial Durations .............. 3.2.1 Trade and Order Arrival Durations ........................ 3.2.2 Price and Volume Durations ............................... Properties of Financial Durations.................................... Properties of Trading Characteristics................................ Properties of Time Aggregated Data ................................ 3.3 3.4 3.5 3.2 1 1 4 8 9 9 9 10 12 14 16 19 19 21 22 23 24 27 27 27 30 32 34 34 35 35 36 37 44 52 ix
    x 4 5 Contents 3.6 Summary of Major Empirical Findings ............................. References .................................................................... Financial Point Processes .................................................. 4.1 Basic Concepts of Point Processes .................................. Fundamental Definitions ................................... 4.1.1 Compensators and Intensities .............................. 4.1.2 The Homogeneous Poisson Process ....................... 4.1.3 4.1.4 Generalizations of Poisson Processes...................... A Random Time Change Argument ....................... 4.1.5 Intensity-Based Inference .................................. 4.1.6 4.1.7 Simulation and Diagnostics ................................ Four Ways to Model Point Processes................................ Intensity Models ............................................ 4.2.1 Hazard Models .............................................. 4.2.2 Duration Models ............................................ 4.2.3 4.2.4 Count Data Models ......................................... Censoring and Time-Varying Covariates ............................ Censoring ................................................... 4.3.1 4.3.2 Time-Varying Covariates ................................... An Outlook on Dynamic Extensions ................................ 4.4 References .................................................................... 4.2 4.3 60 67 69 69 69 71 74 76 77 79 81 83 83 85 90 90 91 91 92 94 97 5.4 5.5 5.6 Univariate Multiplicative Error Models ................................. 5.1 5.2 5.3 99 ARMA Models for Log Variables ................................... 100 A MEM for Durations: The ACD Model ........................... 102 Estimation of the ACD Model ....................................... 104 5.3.1 QML Estimation ............................................ 104 5.3.2 ML Estimation .............................................. 109 Seasonalities and Explanatory Variables ............................ 113 The Log-ACD Model ................................................ 115 Testing the ACD Model .............................................. 117 Portmanteau Tests .......................................... 118 5.6.1 5.6.2 Independence Tests ......................................... 120 Distribution Tests ........................................... 123 5.6.3 Lagrange Multiplier Tests . ................................. 127 5.6.4 5.6.5 Conditional Moment Tests ................................. 130 5.6.6 Monte Carlo Evidence...................................... 136 References .................................................................... 139 6.1 6 Generalized Multiplicative Error Models. ............................... 143 A Class of Augmented ACD Models................................ 143 6.1.1 Special Cases ............................................... 144 Theoretical Properties ...................................... 148 6.1.2 6.1.3 Empirical Illustrations ...................................... 149
    分享到：
    赞 收藏

    相关推荐

    资料库

    大数据

    共收录841份资料，累计11个分类，关注成员有19位，主要包括：Matlab,统计分析,数据挖掘,Hbase,Hive,flink,kafka,spark,Storm,算法与数据结构,Hadoop

    热门标签

    Matlab 统计分析 数据挖掘 Hbase Hive flink kafka spark Storm 算法与数据结构 Hadoop

    最新资料