An edition of Econometrics of financial high-frequency data
(2011)
Econometrics of financial high-frequency data
by Nikolaus Hautsch
Share
×Close
This edition doesn't have a description yet. Can you add one?
Check nearby libraries
Buy this book
Previews available in: English
Subjects
Foreign exchange rates, Econometric models, Finance, Econometrics| Edition | Availability |
|---|---|
1
|
zzzz
|
2
|
aaaa
|
|
3
|
zzzz
|
4
Econometrics of Financial High-Frequency Data
Oct 16, 2011, Springer
paperback
3642219268 9783642219269
|
zzzz
|
Book Details
Table of 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.
Edition Notes
Includes bibliographical references and index.
Classifications
The Physical Object
ID Numbers
Community Reviews (0)
Feedback?
History
- Created July 4, 2012
- 7 revisions
Wikipedia citation
×CloseCopy and paste this code into your Wikipedia page. Need help?
| October 17, 2020 | Edited by MARC Bot | import existing book |
| October 9, 2020 | Edited by ImportBot | import existing book |
| August 2, 2020 | Edited by ImportBot | import existing book |
| June 29, 2019 | Edited by MARC Bot | import existing book |
| July 4, 2012 | Created by LC Bot | Imported from Library of Congress MARC record |