Handbook of Modeling High-Frequency Data in Finance

1. Auflage 2011
ISBN: 978-0-470-87688-6
Verlag: Wiley

Buch, Englisch, 456 Seiten, Format (B × H): 157 mm x 236 mm, Gewicht: 717 g

Reihe: Wiley Handbooks in Financial Engineering and Econometrics

This exciting volume presents cutting-edge developments in high frequency financial econometrics, spanning a diverse range of topics: stochastic modeling, statistical analysis of high-frequency data, models in econophysics, applications to the analysis of high-frequency data, systems and complex adaptive systems in finance, among a host of others.

Written, in part, on the outgrowth of several recent conferences in the subject matter and in concert with over two-dozen experts in the field, the main purpose of the handbook is to mathematically illustrate the fundamental implementation of high-frequency models in the banking and financial industries, both at home and abroad, through use of real-world, time-sensitive applications.

By using examples derived from consulting projects, current research and course instruction, each chapter in the book offers a systematic understanding of the recent advances in high-frequency modeling related to real-world situations. Every effort is made to present a balanced treatment between theory and practice, as well as to showcase how accuracy and efficiency in implementing various methods can be used as indispensable tools.

To by-pass tedious computation, software illustrations are presented in an assortment of packages, ranging from R, C++, EXCEL-VBA, Minitab, to JMP/SAS. Shedding light on some of the most relevant open questions in the analysis of high-frequency data, this volume will be of interest to graduate students, researchers and industry professionals.

Autoren/Hrsg.

Viens, Frederi G

Mariani, Maria Cristina

Mariani, Maria C.

Florescu, Ionut

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensfinanzen Finanzierung, Investition, Leasing
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie

Inhaltsverzeichnis

Preface xi

Contributors xiii

Part One Analysis of Empirical Data 1

1 Estimation of NIG and VG Models for High Frequency Financial Data 3
José E. Figueroa-López, Steven R. Lancette, Kiseop Lee, and Yanhui Mi

1.1 Introduction, 3

1.2 The Statistical Models, 6

1.3 Parametric Estimation Methods, 9

1.4 Finite-Sample Performance via Simulations, 14

1.5 Empirical Results, 18

1.6 Conclusion, 22

References, 24

2 A Study of Persistence of Price Movement using High Frequency Financial Data 27
Dragos Bozdog, Ionut¸ Florescu, Khaldoun Khashanah, and Jim Wang

2.1 Introduction, 27

2.2 Methodology, 29

2.3 Results, 35

2.4 Rare Events Distribution, 41

2.5 Conclusions, 44

References, 45

3 Using Boosting for Financial Analysis and Trading 47
Germán Creamer

3.1 Introduction, 47

3.2 Methods, 48

3.3 Performance Evaluation, 53

3.4 Earnings Prediction and Algorithmic Trading, 60

3.5 Final Comments and Conclusions, 66

References, 69

4 Impact of Correlation Fluctuations on Securitized structures 75
Eric Hillebrand, Ambar N. Sengupta, and Junyue Xu

4.1 Introduction, 75

4.2 Description of the Products and Models, 77

4.3 Impact of Dynamics of Default Correlation on

Low-Frequency Tranches, 79

4.4 Impact of Dynamics of Default Correlation on High-Frequency Tranches, 87

4.5 Conclusion, 92

References, 94

5 Construction of Volatility Indices Using A Multinomial Tree Approximation Method 97
Dragos Bozdog, Ionut¸ Florescu, Khaldoun Khashanah, and Hongwei Qiu

5.1 Introduction, 97

5.2 New Methodology, 99

5.3 Results and Discussions, 101

5.4 Summary and Conclusion, 110

References, 115

Part Two Long Range Dependence Models 117

6 Long Correlations Applied to the Study of Memory Effects in High Frequency (TICK) Data, the Dow Jones Index, and International Indices 119
Ernest Barany and Maria Pia Beccar Varela

6.1 Introduction, 119

6.2 Methods Used for Data Analysis, 122

6.3 Data, 128

6.4 Results and Discussions, 132

6.5 Conclusion, 150

References, 160

7 Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales 163
Alec N. Kercheval and Yang Liu

7.1 Introduction, 163

7.2 The Skewed t Distributions, 165

7.3 Risk Forecasts on a Fixed Timescale, 176

7.4 Multiple Timescale Forecasts, 185

7.5 Backtesting, 188

7.6 Further Analysis: Long-Term GARCH and Comparisons using Simulated Data, 203

7.7 Conclusion, 216

References, 217

8 Parameter Estimation and Calibration for Long-Memory Stochastic Volatility Models 219
Alexandra Chronopoulou

8.1 Introduction, 219

8.2 Statistical Inference Under the LMSV Model, 222

8.3 Simulation Results, 227

8.4 Application to the S&P Index, 228

8.5 Conclusion, 229

References, 230

Part Three Analytical Results 233

9 A Market Microstructure Model of Ultra High Frequency Trading 235
Carlos A. Ulibarri and Peter C. Anselmo

9.1 Introduction, 235

9.2 Microstructural Model, 237

9.3 Static Comparisons, 239

9.4 Questions for Future Research, 241

References, 242

10 Multivariate Volatility Estimation with High Frequency Data Using Fourier Method 243
Maria Elvira Mancino and Simona Sanfelici

10.1 Introduction, 243

10.2 Fourier Estimator of Multivariate Spot Volatility, 246

10.3 Fourier Estimator of Integrated Volatility in the Presence of

Microstructure Noise, 252

10.4 Fourier Estimator of Integrated Covariance in the Presence of Microstructure Noise, 263

10.5 Forecasting Properties of Fourier Estimator, 272

10.6 Application: Asset Allocation, 286

References, 290

11 The "Retirement" Problem 295
Cristian Pasarica

11.1 Introduction, 295

11.2 The Market Model, 296

11.3 Portfolio and Wealth Processes, 297

11.4 Utility Function, 299

11.5 The Optimization Problem in the Case p(t ,T] = 0, 299

11.6 Duality Approach, 300

11.7 Infinite Horizon Case, 305

References, 324

12 Stochastic Di

Über Autor(innen)

Mariani, Maria C.
Maria C. Mariani, PhD, is Pro-fessor and Chair in the Department of Mathematical Sciences at The University of Texas at El Paso. She currently focuses her research on mathematical finance, applied mathematics, and numerical methods. Dr. Mariani is co-organizer of the annual Conference on Modeling High-Frequency Data in Finance.

Florescu, Ionut
Ionut Florescu, PhD, is Assistant Professor of Mathematics at Stevens Institute of Technology. He has published in research areas including stochastic volatility, stochastic partial differential equations, Monte Carlo methods, and numerical methods for stochastic processes. Dr. Florescu is lead organizer of the annual Conference on Modeling High-Frequency Data in Finance.

Viens, Frederi G
Frederi G. Viens, PhD, is Director and Coordinator of the Computational Finance Program at Purdue University, where he also serves as Professor of Statistics and Mathematics. He has published extensively in the areas of mathematical finance, probability theory, and stochastic processes. Dr. Viens is co-organizer of the annual Conference on Modeling High-Frequency Data in Finance.

Frederi G. Viens, PhD, is Director and Coordinator of the Computational Finance Program at Purdue University, where he also serves as Professor of Statistics and Mathematics. He has published extensively in the areas of mathematical finance, probability theory, and stochastic processes. Dr. Viens is co-organizer of the annual Conference on Modeling High-Frequency Data in Finance.

Maria C. Mariani, PhD, is Pro-fessor and Chair in the Department of Mathematical Sciences at The University of Texas at El Paso. She currently focuses her research on mathematical finance, applied mathematics, and numerical methods. Dr. Mariani is co-organizer of the annual Conference on Modeling High-Frequency Data in Finance.

Ionut Florescu, PhD, is Assistant Professor of Mathematics at Stevens Institute of Technology. He has published in research areas including stochastic volatility, stochastic partial differential equations, Monte Carlo methods, and numerical methods for stochastic processes. Dr. Florescu is lead organizer of the annual Conference on Modeling High-Frequency Data in Finance.