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Econometrics of financial high-frequency data / Nikolaus Hautsch.

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Author/Creator:
Hautsch, Nikolaus.
Language:
English.
Publication date:
2012
Imprint:
Berlin : Springer, c2012.
Format:
  • Book
  • xiii, 371 : ill ; 24 cm.
Bibliography:
Includes bibliographical references and index.
Contents:
  • Machine generated contents note: 1.Introduction
  • 1.1.Motivation
  • 1.2.Structure of the Book
  • References
  • 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
  • 2.1.4.Order Precedence and Pricing Rules
  • 2.1.5.Trading Forms at Selected International Exchanges
  • 2.2.A Review of Market Microstructure Theory
  • 2.2.1.Asymmetric Information Based Models
  • 2.2.2.Inventory Models
  • 2.2.3.Major Implications for Trading Variables
  • 2.2.4.Models for Limit Order Book Markets
  • References
  • 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
  • Note continued: 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
  • 4.Financial Point Processes
  • 4.1.Basic Concepts of Point Processes
  • 4.1.1.Fundamental Definitions
  • 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
  • 4.2.3.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
  • 5.Univariate Multiplicative Error Models
  • Note continued: 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
  • 5.6.6.Monte Carlo Evidence
  • References
  • 6.Generalized Multiplicative Error Models
  • 6.1.A Class of Augmented ACD Models
  • 6.1.1.Special Cases
  • 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
  • Note continued: 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
  • 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
  • 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
  • Note continued: 8.4.Volatility Measurement Using Price Durations
  • 8.5.Modelling Quote Volatility
  • References
  • 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
  • 9.3.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
  • 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
  • Note continued: 10.6.Estimating Volatility Using the SACPH Model
  • 10.6.1.Data and the Generation of Price Events
  • 10.6.2.Empirical Findings
  • References
  • 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
  • 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
  • Note continued: 12.5.SCI Modelling of Multivariate Price Intensities
  • References
  • 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
  • A.Important Distributions for Positive-Valued Data.
Subjects:
ISBN:
3642219241
9783642219245

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