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Handbook of High-Frequency Trading and Modeling in Finance
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Artikel-Nr:
9781118443989
Veröffentl:
2016
Erscheinungsdatum:
25.04.2016
Seiten:
456
Autor:
Ionut Florescu
Gewicht:
844 g
Format:
240x161x29 mm
Sprache:
Englisch
Beschreibung:
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.
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.
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.
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.
Notes on Contributors xiii
Preface xv
1 Trends and Trades 1
Michael Carlisle, Olympia Hadjiliadis, and Ioannis Stamos
1.1 Introduction 1
1.2 A trend-based trading strategy 3
1.2.1 Signaling and trends 3
1.2.2 Gain over a subperiod 5
1.3 CUSUM timing 7
1.3.1 Cusum process and stopping time 7
1.3.2 A CUSUM timing scheme 10
1.3.3 US treasury notes, CUSUM timing 11
1.4 Example: Random walk on ticks 12
1.4.1 Random walk expected gain over a subperiod 15
1.4.2 Simple random walk, CUSUM timing 18
1.4.3 Lazy simple random walk, cusum timing 21
1.5 CUSUM strategy Monte Carlo 24
1.6 The effect of the threshold parameter 27
1.7 Conclusions and future work 39
Appendix: Tables 40
References 47
2 Gaussian Inequalities and Tranche Sensitivities 51
Claas Becker and Ambar N. Sengupta
2.1 Introduction 51
2.2 The tranche loss function 52
2.3 A sensitivity identity 54
2.4 Correlation sensitivities 55
Acknowledgment 58
References 58
3 A Nonlinear Lead Lag Dependence Analysis of Energy Futures: Oil, Coal, and Natural Gas 61
Germán G. Creamer and Bernardo Creamer
3.1 Introduction 61
3.1.1 Causality analysis 62
3.2 Data 64
3.3 Estimation techniques 64
3.4 Results 65
3.5 Discussion 67
3.6 Conclusions 69
Acknowledgments 69
References 70
4 Portfolio Optimization: Applications in Quantum Computing 73
Michael Marzec
4.1 Introduction 73
4.2 Background 75
4.2.1 Portfolios and optimization 76
4.2.2 Algorithmic complexity 77
4.2.3 Performance 78
4.2.4 Ising model 79
4.2.5 Adiabatic quantum computing 79
4.3 The models 80
4.3.1 Financial model 81
4.3.2 Graph-theoretic combinatorial optimization models 82
4.3.3 Ising and Qubo models 83
4.3.4 Mixed models 84
4.4 Methods 84
4.4.1 Model implementation 85
4.4.2 Input data 85
4.4.3 Mean-variance calculations 85
4.4.4 Implementing the risk measure 86
4.4.5 Implementation mapping 86
4.5 Results 88
4.5.1 The simple correlation model 88
4.5.2 The restricted minimum-risk model 91
4.5.3 The WMIS minimum-risk, max return model 94
4.6 Discussion 95
4.6.1 Hardware limitations 97
4.6.2 Model limitations 97
4.6.3 Implementation limitations 98
4.6.4 Future research 98
4.7 Conclusion 100
Acknowledgments 100
Appendix 4.A: WMIS Matlab Code 100
References 103
5 Estimation Procedure for Regime Switching Stochastic Volatility Model and Its Applications 107
Ionut Florescu and Forrest Levin
5.1 Introduction 107
5.1.1 The original motivation 108
5.1.2 The model and the problem 108
5.1.3 A brief historical note 109
5.2 The methodology 110
5.2.1 Obtaining filtered empirical distributions at t1,..., tT 110
5.2.2 Obtaining the parameters of the Markov chain 112
5.3 Results obtained applying the model to real data 113
5.3.1 Part i: financial applications 113
5.3.2 Part ii: physical data application. temperature data 119
5.3.3 Part iii: analysis of seismometer readings during an earthquake 121
5.3.4 Analysis of the earthquake signal: beginning 123
5.3.5 Analysis: during the earthquak
Preface xv
1 Trends and Trades 1
Michael Carlisle, Olympia Hadjiliadis, and Ioannis Stamos
1.1 Introduction 1
1.2 A trend-based trading strategy 3
1.2.1 Signaling and trends 3
1.2.2 Gain over a subperiod 5
1.3 CUSUM timing 7
1.3.1 Cusum process and stopping time 7
1.3.2 A CUSUM timing scheme 10
1.3.3 US treasury notes, CUSUM timing 11
1.4 Example: Random walk on ticks 12
1.4.1 Random walk expected gain over a subperiod 15
1.4.2 Simple random walk, CUSUM timing 18
1.4.3 Lazy simple random walk, cusum timing 21
1.5 CUSUM strategy Monte Carlo 24
1.6 The effect of the threshold parameter 27
1.7 Conclusions and future work 39
Appendix: Tables 40
References 47
2 Gaussian Inequalities and Tranche Sensitivities 51
Claas Becker and Ambar N. Sengupta
2.1 Introduction 51
2.2 The tranche loss function 52
2.3 A sensitivity identity 54
2.4 Correlation sensitivities 55
Acknowledgment 58
References 58
3 A Nonlinear Lead Lag Dependence Analysis of Energy Futures: Oil, Coal, and Natural Gas 61
Germán G. Creamer and Bernardo Creamer
3.1 Introduction 61
3.1.1 Causality analysis 62
3.2 Data 64
3.3 Estimation techniques 64
3.4 Results 65
3.5 Discussion 67
3.6 Conclusions 69
Acknowledgments 69
References 70
4 Portfolio Optimization: Applications in Quantum Computing 73
Michael Marzec
4.1 Introduction 73
4.2 Background 75
4.2.1 Portfolios and optimization 76
4.2.2 Algorithmic complexity 77
4.2.3 Performance 78
4.2.4 Ising model 79
4.2.5 Adiabatic quantum computing 79
4.3 The models 80
4.3.1 Financial model 81
4.3.2 Graph-theoretic combinatorial optimization models 82
4.3.3 Ising and Qubo models 83
4.3.4 Mixed models 84
4.4 Methods 84
4.4.1 Model implementation 85
4.4.2 Input data 85
4.4.3 Mean-variance calculations 85
4.4.4 Implementing the risk measure 86
4.4.5 Implementation mapping 86
4.5 Results 88
4.5.1 The simple correlation model 88
4.5.2 The restricted minimum-risk model 91
4.5.3 The WMIS minimum-risk, max return model 94
4.6 Discussion 95
4.6.1 Hardware limitations 97
4.6.2 Model limitations 97
4.6.3 Implementation limitations 98
4.6.4 Future research 98
4.7 Conclusion 100
Acknowledgments 100
Appendix 4.A: WMIS Matlab Code 100
References 103
5 Estimation Procedure for Regime Switching Stochastic Volatility Model and Its Applications 107
Ionut Florescu and Forrest Levin
5.1 Introduction 107
5.1.1 The original motivation 108
5.1.2 The model and the problem 108
5.1.3 A brief historical note 109
5.2 The methodology 110
5.2.1 Obtaining filtered empirical distributions at t1,..., tT 110
5.2.2 Obtaining the parameters of the Markov chain 112
5.3 Results obtained applying the model to real data 113
5.3.1 Part i: financial applications 113
5.3.2 Part ii: physical data application. temperature data 119
5.3.3 Part iii: analysis of seismometer readings during an earthquake 121
5.3.4 Analysis of the earthquake signal: beginning 123
5.3.5 Analysis: during the earthquak