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Econometrics of financial high-frequency data

Author: Nikolaus Hautsch
Publisher: Berlin ; Heidelberg : Springer, ©2012.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility.  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nikolaus Hautsch
ISBN: 9783642219252 364221925X 9783642219245 3642219241
OCLC Number: 758362610
Description: 1 online resource (xiii, 371 pages)
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. 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.
Responsibility: Nikolaus Hautsch.

Abstract:

This book covers major approaches in high-frequency econometrics. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and  Read more...

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