Handbook of High-Frequency Trading and Modeling in Finance

Ionut Florescu, Maria Cristina Mariani, H. Eugene Stanley, Frederi G. Viens, Ionut Florescu, Maria C. Mariani, H. Eugene Stanley, Frederi G. Viens, Ionut Florescu, Maria C. Mariani, H. Eugene Stanley, Frederi G. Viens

• Publisher: Wiley
• Year: 2016
• Print ISBN: 9781118443989
• eBook ISBN: 9781118593325
• Edition: 1
• Topic: Business
• Subtopic: Finance
• Index: Business

Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data.

Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features:

• Contributions by well-known experts within the academic, industrial, and regulatory fields

• A well-structured outline on the various data analysis methodologies used to identify new trading opportunities

• Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets

• Practical applications using real-world data to help readers better understand the presented material

The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics.

Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley.

Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.

H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences.

Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.

Chapter One
Trends and Trades

Michael Carlisle¹, Olympia Hadjiliadis², and Ioannis Stamos³

¹Department of Mathematics, Baruch College, City University of New York, New York, NY, USA

²Department of Mathematics and Statistics, Hunter College, City University of New York, and Departments of Computer Science and Mathematics, Graduate Center, City University of New York, New York, NY, USA

³Department of Computer Science, Hunter College, City University of New York, and Department of Computer Science, Graduate Center, City University of New York, New York, NY, USA

1.1 Introduction

High-frequency data in finance is often characterized by fast fluctuations and noise (see, e.g., [7]), a trait that is known to make the volatility of the data very hard to estimate (see, e.g., [13]). Although this characteristic creates many challenges in modeling, ...

Table of contents

• Cover
• Title page
• Copyright
• Notes on Contributors
• Preface
• Chapter 1 Trends and Trades
• Chapter 2 Gaussian Inequalities and Tranche Sensitivities
• Chapter 3 A Nonlinear Lead Lag Dependence Analysis of Energy Futures: Oil, Coal, and Natural Gas1
• Chapter 4 Portfolio Optimization: Applications in Quantum Computing
• Chapter 5 Estimation Procedure for Regime Switching Stochastic Volatility Model and Its Applications
• Chapter 6 Detecting Jumps in High-Frequency Prices Under Stochastic Volatility: A Review and a Data-Driven Approach
• Chapter 7 Hawkes Processes and Their Applications to High-Frequency Data Modeling
• Chapter 8 Multifractal Random Walk Driven by a Hermite Process:
• Chapter 9 Interpolating Techniques and Nonparametric Regression Methods Applied to Geophysical and Financial Data Analysis
• Chapter 10 Study of Volatility Structures in Geophysics and Finance Using Garch Models
• Chapter 11 Scale Invariance and Lévy Models Applied to Earthquakes and Financial High-Frequency Data
• Chapter 12 Analysis of Generic Diversity in the Fossil Record, Earthquake Series, and High-Frequency Financial Data
• Index
• Series
• EULA