Hautsch | Modelling Irregularly Spaced Financial Data | Buch | sack.de

Hautsch Modelling Irregularly Spaced Financial Data



Theory and Practice of Dynamic Duration Models

Softcover Nachdruck of the original 1. Auflage 2004, Band: 539, 292 Seiten, Kartoniert, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 464 g Reihe: Lecture Notes in Economics and Mathematical Systems
ISBN: 978-3-540-21134-1
Verlag: Springer Berlin Heidelberg


Hautsch Modelling Irregularly Spaced Financial Data

This book has been written as a doctoral dissertation at the Department of Economics at the University of Konstanz. I am indebted to my supervisor Winfried Pohlmeier for providing a stimulating and pleasant research en- ronment and his continuous support during my doctoral studies. I strongly bene?tted from inspiring discussions with him, his valuable advices and he- ful comments regarding the contents and the exposition of this book. I am grateful to Luc Bauwens for refereeing my work as a second super- sor. Moreover, I wish to thank him for o?ering me the possibility of a research visit at the Center of Operations Research and Econometrics (CORE) at the Universit´ e Catholique de Louvain. Important parts of this book have been conceived during this period. Similarly, I am grateful to Tony Hall who invited me for a research visit at the University of Technology, Sydney, and provided me access to an excellent database from the Australian Stock Exchange. I would like to thank him for his valuable support and the permission to use this data for empirical studies in this book. I wish to thank my colleagues at the University of Konstanz Frank G- hard,DieterHess,JoachimInkmann,MarkusJochmann,StefanKlotz,Sandra Lechner and Ingmar Nolte who o?ered me advice, inspiration, friendship and successfulco-operations.Moreover,Iamgratefultothestudentresearchass- tantsat the Chair of Econometrics at the University of Konstanz, particularly Magdalena Ramada Sarasola, Danielle Tucker and Nadine Warmuth who did a lot of editing work.

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1 Introduction.- 2 Point Processes.- 2.1 Basic Concepts of Point Processes.- 2.1.1 Fundamental Definitions.- 2.1.2 The Homogeneous Poisson Process.- 2.1.3 The Intensity Function and its Properties.- 2.1.4 Intensity-Based Inference.- 2.2 Types of Point Processes.- 2.2.1 Poisson Processes.- 2.2.2 Renewal Processes.- 2.2.3 Dynamic Point Processes.- 2.3 Non-Dynamic Point Process Models.- 2.3.1 Intensity-Based Models.- 2.3.2 Duration Models.- 2.3.3 Count Data Models.- 2.4 Censoring and Time-Varying Covariates.- 2.4.1 Censoring.- 2.4.2 Time-Varying Covariates.- 2.5 Outlook on Dynamic Extensions.- 3 Economic Implications of Financial Durations.- 3.1 Types of Financial Durations.- 3.1.1 Selection by Single Marks.- 3.1.2 Selection by Sequences of Marks.- 3.2 The Role of Trade Durations in Market Microstructure Theory.- 3.2.1 Traditional Market Microstructure Approaches.- 3.2.2 Determinants of Trade Durations.- 3.3 Risk Estimation based on Price Durations.- 3.3.1 Duration-Based Volatility Measurement.- 3.3.2 Economic Implications of Directional Change Durations.- 3.4 Liquidity Measurement.- 3.4.1 The Liquidity Concept.- 3.4.2 Volume Durations and Liquidity.- 3.4.3 The VNET Measure.- 3.4.4 Measuring (Il)liquidity Risks using Excess Volume Durations.- 4 Statistical Properties of Financial Durations.- 4.1 Data Preparation Issues.- 4.1.1 Matching Trades and Quotes.- 4.1.2 Treatment of Split-Transactions.- 4.1.3 Identification of Buyer- and Seller-Initiated Trades.- 4.2 Transaction Databases and Data Preparation.- 4.2.1 NYSE Trading.- 4.2.2 XETRA Trading.- 4.2.3 Frankfurt Floor Trading.- 4.2.4 Bund Future Trading at EUREX and LIFFE.- 4.2.5 ASX Trading.- 4.3 Statistical Properties of Trade, Limit Order and Quote Durations.- 4.4 Statistical Properties of Price Durations.- 4.5 Statistical Properties of (Excess) Volume Durations.- 4.6 Summarizing the Statistical Findings.- 5 Autoregressive Conditional Duration Models.- 5.1 ARMA Models for (Log-)Durations.- 5.2 The ACD Model.- 5.2.1 The Basic ACD Framework.- 5.2.2 QML Estimation of the ACD Model.- 5.2.3 Distributional Issues and ML Estimation of the ACD Model.- 5.2.4 Seasonalities and Explanatory Variables.- 5.3 Extensions of the ACD Framework.- 5.3.1 Augmented ACD Models.- 5.3.2 Theoretical Properties of Augmented ACD Models.- 5.3.3 Regime-Switching ACD Models.- 5.3.4 Long Memory ACD Models.- 5.3.5 Further Extensions.- 5.4 Testing the ACD Model.- 5.4.1 Simple Residual Checks.- 5.4.2 Density Forecast Evaluations.- 5.4.3 Lagrange Multiplier Tests.- 5.4.4 Conditional Moment Tests.- 5.4.5 Integrated Conditional Moment Tests.- 5.4.6 Monte Carlo Evidence.- 5.5 Applications of ACD Models.- 5.5.1 Evaluating ACD Models based on Trade and Price Durations.- 5.5.2 Modelling Trade Durations.- 5.5.3 Quantifying (Il)liquidity Risks.- 6 Semiparametric Dynamic Proportional Intensity Models.- 6.1 Dynamic Integrated Intensity Processes.- 6.2 The Semiparametric ACPI Model.- 6.3 Properties of the Semiparametric ACPI Model.- 6.3.1 Autocorrelation Structure.- 6.3.2 Evaluating the Estimation Quality.- 6.4 Extensions of the ACPI Model.- 6.4.1 Regime-Switching Dynamics.- 6.4.2 Regime-Switching Baseline Intensities.- 6.4.3 Censoring.- 6.4.4 Unobserved Heterogeneity.- 6.5 Testing the ACPI Model.- 6.6 Estimating Volatility Using the ACPI Model.- 6.6.1 The Data and the Generation of Price Events.- 6.6.2 Empirical Findings.- 7 Univariate and Multivariate Dynamic Intensity Models.- 7.1 Univariate Dynamic Intensity Models.- 7.1.1 The ACI Model.- 7.1.2 The Hawkes Model.- 7.2 Multivariate Dynamic Intensity Models.- 7.2.1 Definitions.- 7.2.2 The Multivariate ACI Model.- 7.2.3 The Multivariate Hawkes Model.- 7.3 Dynamic Latent Factor Models for Intensity Processes.- 7.3.1 The LFI Model.- 7.3.2 The Univariate LFI Model.- 7.3.3 The Multivariate LFI Model.- 7.3.4 Dynamic Properties of the LFI Model.- 7.3.5 SML Estimation of the LFI Model.- 7.3.6 Testing the LFI Model.- 7.4 Applications of Dynamic Intensity Models.- 7.4.1 Estimatin


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