Modelling Irregularly Spaced Financial Data: Theory and Practice of Dynamic Duration Models

ISBN-13: 9783540211341 / Angielski / Miękka / 2004 / 292 str.

Nikolaus Hautsch

Opis

This book provides a methodological framework to model univariate and multivariate irregularly spaced financial data. It gives a thorough review of recent developments in the econometric literature, puts forward existing approaches and opens up new directions. The book presents alternative ways to model so-called financial point processes using dynamic duration as well as intensity models and discusses their ability to account for specific features of point process data, like the occurrence of time-varying covariates, censoring mechanisms and multivariate structures. Moreover, it illustrates the use of various types of financial point processes to model financial market activity from different viewpoints and to construct volatility and liquidity measures under explicit consideration of the passing trading time.

Recenzje

From the reviews of the first edition:

"This book regards financial point processes. ... Valuable risk and liquidity measures are constructed by defining financial events in terms of price and /or the volume process. Several applications are illustrated." (Klaus Ehemann, Zentralblatt MATH, Vol. 1081, 2006)

Spis treści

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 Estimating Multivariate Price Intensities.- 7.4.2 Estimating Simultaneous Buy/Sell Intensities.- 7.4.3 Estimating Trading Intensities Using LFI Models.- 8 Summary and Conclusions.- A Important Distributions for Duration Data.- B List of Symbols (in Alphabetical Order).- References.