Title Econometrics of financial high-frequency data / Nikolaus Hautsch

Published Berlin ; Heidelberg : Springer, ©2012

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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 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. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis Analysis Economics Finance Econometrics Economics/Management Science Financial Economics Quantitative Finance Bibliography Includes bibliographical references and index Subject Finance -- Econometric models BUSINESS & ECONOMICS -- Economics -- General. BUSINESS & ECONOMICS -- Reference. Science économique. Affaires. Finance -- Econometric models Form Electronic book ISBN 9783642219252 364221925X

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