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.
Yazar hakkında
Frederi G. Viens, Ph D, 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, Ph D, 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, Ph D, 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.
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