Handbook of 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.

CUTTING-EDGE DEVELOPMENTS IN HIGH-FREQUENCY FINANCIAL ECONOMETRICS
 
In recent years, the availability of high-frequency data and advances in computing have allowed financial practitioners to design systems that can handle and analyze this information. Handbook of Modeling High-Frequency Data in Finance addresses the many theoretical and practical questions raised by the nature and intrinsic properties of this data.
 
A one-stop compilation of empirical and analytical research, this handbook explores data sampled with high-frequency finance in financial engineering, statistics, and the modern financial business arena. Every chapter uses real-world examples to present new, original, and relevant topics that relate to newly evolving discoveries in high-frequency finance, such as:
* Designing new methodology to discover elasticity and plasticity of price evolution
* Constructing microstructure simulation models
* Calculation of option prices in the presence of jumps and transaction costs
* Using boosting for financial analysis and trading
 
The handbook motivates practitioners to apply high-frequency finance to real-world situations by including exclusive topics such as risk measurement and management, UHF data, microstructure, dynamic multi-period optimization, mortgage data models, hybrid Monte Carlo, retirement, trading systems and forecasting, pricing, and boosting. The diverse topics and viewpoints presented in each chapter ensure that readers are supplied with a wide treatment of practical methods.
 
Handbook of Modeling High-Frequency Data in Finance is an essential reference for academics and practitioners in finance, business, and econometrics who work with high-frequency data in their everyday work. It also serves as a supplement for risk management and high-frequency finance courses at the upper-undergraduate and graduate levels.

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,