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.

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.

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.

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 Ionu Florescu Khaldoun Khashanah and Jim Wang

2.1 Introduction 27

2.2 Methodology 29

2.3 Results 35

2.4…