Image from Google Jackets

Econometrics of financial high-frequency data / by Nikolaus Hautsch.

By: Material type: TextTextPublication details: Berlin : Springer, c2012.Description: xiii, 371 : ill. ; 24 cmISBN:
  • 3642219241
  • 9783642219245
Subject(s): DDC classification:
  • 330.15195 23 HAU/E
Online resources:
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 --
Contents 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 --
Contents 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 --
Contents 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 --
Contents 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 --
Contents 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 --
Contents 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Home library Call number Status Date due Barcode Item holds
Technical Reference Book Technical Reference Book Central Library, IIT Bhubaneswar Central Library, IIT Bhubaneswar 330.15195 HAU/E (Browse shelf(Opens below)) Available 8596
Total holds: 0

Includes bibliographical references and index.

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 --

Contents 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 --

Contents 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 --

Contents 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 --

Contents 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 --

Contents 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 --

Contents 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.

There are no comments on this title.

to post a comment.

Central Library, Indian Institute of Technology Bhubaneswar, 4th Floor, Administrative Building, Argul, Khordha, PIN-752050, Odisha, India
Phone: +91-674-7138750 | Email: circulation.library@iitbbs.ac.in (For circulation related queries),
Email: info.library@iitbbs.ac.in (For other queries)

Powered by Koha