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Summary:
Publisher Summary 1
"The book offers a systematic understanding of the recent advances in high-frequency modeling related to real-world situations"--
Publisher Summary 2
"This exciting volume presents cutting-edge developments in high frequency financial econometrics, spanning a diverse range of topics: stochastic modeling, statistical analysis of high-frequency data, models in econophysics, applications to the analysis of high-frequency data, systems and complex adaptive systems in finance, among a host of others. Written, in part, on the outgrowth of several recent conferences in the subject matter and in concert with over two-dozen experts in the field, the main purpose of the handbook is to mathematically illustrate the fundamental implementation of high-frequency models in the banking and financial industries, both at home and abroad, through use of real-world, time-sensitive applications. By using examples derived from consulting projects, current research and course instruction, each chapter in the book offers a systematic understanding of the recent advances in high-frequency modeling related to real-world situations. Every effort is made to present a balanced treatment between theory and practice, as well as to showcase how accuracy and efficiency in implementing various methods can be used as indispensable tools. To by-pass tedious computation, software illustrations are presented in an assortment of packages, ranging from R, C++, EXCEL-VBA, Minitab, to JMP/SAS. Shedding light on some of the most relevant open questions in the analysis of high-frequency data, this volume will be of interest to graduate students, researchers and industry professionals"--
Publisher Summary 3
Provided by publisher.
Publisher Summary 4
For academics and practitioners in finance, business, and econometrics, and for students in upper-undergraduate and graduate level courses in risk management and high-frequency finance, this handbook comprises contributed chapters on empirical work, properties of long memory (known as long range dependence), and new analytical and simulation results relevant to modeling questions. The three editors are mathematicians affiliated as follows: Frederi G. Viens (Purdue U.), Maria C. Mariani (U. of Texas at El Paso), and Ionut Florescu (Stevens Institute of Technology). Annotation 漏2012 Book News, Inc., Portland, OR (booknews.com)
Publisher Summary 5
CUTTING-EDGE DEVELOPMENTS IN HIGH-FREQUENCY FINANCIAL ECONOMETRICSIn 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 Financeaddresses 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 evolutionConstructing microstructure simulation modelsCalculation of option prices in the presence of jumps and transaction costsUsing boosting for financial analysis and tradingThe 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 Financeis 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.
目录
Table Of Contents:
Preface xi
Contributors xiii
PART ONE Analysis of Empirical Data 1(116)
1 Estimation of NIG and VG Models for High Frequency Financial Data 3(24)
Jose E. Figueroa-Lopez
Steven R. Lancette
Kiseop Lee
Yanhui Mi
1.1 Introduction 3(3)
1.2 The Statistical Models 6(3)
1.3 Parametric Estimation Methods 9(5)
1.4 Finite-Sample Performance via Simulations 14(4)
1.5 Empirical Results 18(6)
1.6 Conclusion, 22 References 24(3)
2 A Study of Persistence of Price Movement using High Frequency Financial Data 27(20)
Dragos Bozdog
Ionut Florescu
Khaldoun Khashanah
Jim Wang
2.1 Introduction 27(2)
2.2 Methodology 29(6)
2.3 Results 35(6)
2.4 Rare Events Distribution 41(3)
2.5 Conclusions 44(3)
References 45(2)
3 Using Boosting for Financial Analysis and Trading 47(28)
German Creamer
3.1 Introduction 47(1)
3.2 Methods 48(5)
3.3 Performance Evaluation 53(7)
3.4 Earnings Prediction and Algorithmic Trading 60(6)
3.5 Final Comments and Conclusions 66(9)
References 69(6)
4 Impact of Correlation Fluctuations on Securitized Structures 75(22)
Eric Hillebrand
Ambar N. Sengupta
Junyue Xu
4.1 Introduction 75(2)
4.2 Description of the Products and Models 77(2)
4.3 Impact of Dynamics of Default Correlation on Low-Frequency Tranches 79(8)
4.4 Impact of Dynamics of Default Correlation on High-Frequency Tranches 87(5)
4.5 Conclusion 92(5)
References 94(3)
5 Construction of Volatility Indices Using a Multinomial Tree Approximation Method 97(20)
Dragos Bozdog
Lonut Florescu
Khaldoun Khashanah
Hongwei Qiu
5.1 Introduction 97(2)
5.2 New Methodology 99(2)
5.3 Results and Discussions 101(9)
5.4 Summary and Conclusion 110(7)
References 115(2)
PART TWO Long Range Dependence Models 117(116)
6 Long Correlations Applied to the Study of Memory Effects in High Frequency (TICK) Data, the Dow Jones Index, and International Indices 119(44)
Ernest Barany
Maria Pia Beccar Varela
6.1 Introduction 119(3)
6.2 Methods Used for Data Analysis 122(6)
6.3 Data 128(4)
6.4 Results and Discussions 132(18)
6.5 Conclusion 150(13)
References 160(3)
7 Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales 163(56)
Alec N. Kercheval
Yang Liu
7.1 Introduction 163(2)
7.2 The Skewed t Distributions 165(11)
7.3 Risk Forecasts on a Fixed Timescale 176(9)
7.4 Multiple Timescale Forecasts 185(3)
7.5 Backtesting 188(15)
7.6 Further Analysis: Long-Term GARCH and Comparisons using Simulated Data 203(13)
7.7 Conclusion 216(3)
References 217(2)
8 Parameter Estimation and Calibration for Long-Memory Stochastic Volatility Models 219(14)
Alexandra Chronopoulou
8.1 Introduction 219(3)
8.2 Statistical Inference Under the LMSV Model 222(5)
8.3 Simulation Results 227(1)
8.4 Application to the S&P Index 228(1)
8.5 Conclusion 229(4)
References 230(3)
PART THREE Analytical Results 233(188)
9 A Market Microstructure Model of Ultra High Frequency Trading 235(8)
Carlos A. Ulibarri
Peter C. Anselmo
9.1 Introduction 235(2)
9.2 Microstructural Model 237(2)
9.3 Static Comparisons 239(2)
9.4 Questions for Future Research 241(2)
References 242(1)
10 Multivariate Volatility Estimation with High Frequency Data Using Fourier Method 243(52)
Maria Elvira Mancino
Simona Sanfelici
10.1 Introduction 243(3)
10.2 Fourier Estimator of Multivariate Spot Volatility 246(6)
10.3 Fourier Estimator of Integrated Volatility in the Presence of Microstructure Noise 252(11)
10.4 Fourier Estimator of Integrated Covariance in the Presence of Microstructure Noise 263(9)
10.5 Forecasting Properties of Fourier Estimator 272(14)
10.6 Application: Asset Allocation 286(9)
References 290(5)
11 The "Retirement" Problem 295(32)
Cristian Pasarica
11.1 Introduction 295(1)
11.2 The Market Model 296(1)
11.3 Portfolio and Wealth Processes 297(2)
11.4 Utility Function 299(1)
11.5 The Optimization Problem in die Case π(τT) = 0 299(1)
11.6 Duality Approach 300(5)
11.7 Infinite Horizon Case 305(22)
References 324(3)
12 Stochastic Differential Equations and Levy Models with Applications to High Frequency Data 327(20)
Ernest Barany
Maria Pia Beccar Varela
12.1 Solutions to Stochastic Differential Equations 327(7)
12.2 Stable Distributions 334(2)
12.3 The Levy Flight Models 336(4)
12.4 Numerical Simulations and Levy Models: Applications to Models Arising in Financial Indices and High Frequency Data 340(5)
12.5 Discussion and Conclusions 345(2)
References 346(1)
13 Solutions to Integro-Differential Parabolic Problem Arising on Financial Mathematics 347(36)
Maria C. Mariani
Marc Salas
Indranil Sen Gupta
13.1 Introduction 347(4)
13.2 Method of Upper and Lower Solutions 351(13)
13.3 Another Iterative Method 364(11)
13.4 Integro-Differential Equations in a Levy Market 375(8)
References 380(3)
14 Existence of Solutions for Financial Models with Transaction Costs and Stochastic Volatility 383(38)
Maria C. Mariani
Emmanuel K. Ncheuguim
Indranil Sen Gupta
14.1 Model with Transaction Costs 383(3)
14.2 Review of Functional Analysis 386(5)
14.3 Solution of the Problem (14.2) and (14.3) in Sobolev Spaces 391(9)
14.4 Model with Transaction Costs and Stochastic Volatility 400(8)
14.5 The Analysis of the Resulting Partial Differential Equation 408(13)
References 418(3)
Index 421
Preface xi
Contributors xiii
PART ONE Analysis of Empirical Data 1(116)
1 Estimation of NIG and VG Models for High Frequency Financial Data 3(24)
Jose E. Figueroa-Lopez
Steven R. Lancette
Kiseop Lee
Yanhui Mi
1.1 Introduction 3(3)
1.2 The Statistical Models 6(3)
1.3 Parametric Estimation Methods 9(5)
1.4 Finite-Sample Performance via Simulations 14(4)
1.5 Empirical Results 18(6)
1.6 Conclusion, 22 References 24(3)
2 A Study of Persistence of Price Movement using High Frequency Financial Data 27(20)
Dragos Bozdog
Ionut Florescu
Khaldoun Khashanah
Jim Wang
2.1 Introduction 27(2)
2.2 Methodology 29(6)
2.3 Results 35(6)
2.4 Rare Events Distribution 41(3)
2.5 Conclusions 44(3)
References 45(2)
3 Using Boosting for Financial Analysis and Trading 47(28)
German Creamer
3.1 Introduction 47(1)
3.2 Methods 48(5)
3.3 Performance Evaluation 53(7)
3.4 Earnings Prediction and Algorithmic Trading 60(6)
3.5 Final Comments and Conclusions 66(9)
References 69(6)
4 Impact of Correlation Fluctuations on Securitized Structures 75(22)
Eric Hillebrand
Ambar N. Sengupta
Junyue Xu
4.1 Introduction 75(2)
4.2 Description of the Products and Models 77(2)
4.3 Impact of Dynamics of Default Correlation on Low-Frequency Tranches 79(8)
4.4 Impact of Dynamics of Default Correlation on High-Frequency Tranches 87(5)
4.5 Conclusion 92(5)
References 94(3)
5 Construction of Volatility Indices Using a Multinomial Tree Approximation Method 97(20)
Dragos Bozdog
Lonut Florescu
Khaldoun Khashanah
Hongwei Qiu
5.1 Introduction 97(2)
5.2 New Methodology 99(2)
5.3 Results and Discussions 101(9)
5.4 Summary and Conclusion 110(7)
References 115(2)
PART TWO Long Range Dependence Models 117(116)
6 Long Correlations Applied to the Study of Memory Effects in High Frequency (TICK) Data, the Dow Jones Index, and International Indices 119(44)
Ernest Barany
Maria Pia Beccar Varela
6.1 Introduction 119(3)
6.2 Methods Used for Data Analysis 122(6)
6.3 Data 128(4)
6.4 Results and Discussions 132(18)
6.5 Conclusion 150(13)
References 160(3)
7 Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales 163(56)
Alec N. Kercheval
Yang Liu
7.1 Introduction 163(2)
7.2 The Skewed t Distributions 165(11)
7.3 Risk Forecasts on a Fixed Timescale 176(9)
7.4 Multiple Timescale Forecasts 185(3)
7.5 Backtesting 188(15)
7.6 Further Analysis: Long-Term GARCH and Comparisons using Simulated Data 203(13)
7.7 Conclusion 216(3)
References 217(2)
8 Parameter Estimation and Calibration for Long-Memory Stochastic Volatility Models 219(14)
Alexandra Chronopoulou
8.1 Introduction 219(3)
8.2 Statistical Inference Under the LMSV Model 222(5)
8.3 Simulation Results 227(1)
8.4 Application to the S&P Index 228(1)
8.5 Conclusion 229(4)
References 230(3)
PART THREE Analytical Results 233(188)
9 A Market Microstructure Model of Ultra High Frequency Trading 235(8)
Carlos A. Ulibarri
Peter C. Anselmo
9.1 Introduction 235(2)
9.2 Microstructural Model 237(2)
9.3 Static Comparisons 239(2)
9.4 Questions for Future Research 241(2)
References 242(1)
10 Multivariate Volatility Estimation with High Frequency Data Using Fourier Method 243(52)
Maria Elvira Mancino
Simona Sanfelici
10.1 Introduction 243(3)
10.2 Fourier Estimator of Multivariate Spot Volatility 246(6)
10.3 Fourier Estimator of Integrated Volatility in the Presence of Microstructure Noise 252(11)
10.4 Fourier Estimator of Integrated Covariance in the Presence of Microstructure Noise 263(9)
10.5 Forecasting Properties of Fourier Estimator 272(14)
10.6 Application: Asset Allocation 286(9)
References 290(5)
11 The "Retirement" Problem 295(32)
Cristian Pasarica
11.1 Introduction 295(1)
11.2 The Market Model 296(1)
11.3 Portfolio and Wealth Processes 297(2)
11.4 Utility Function 299(1)
11.5 The Optimization Problem in die Case π(τT) = 0 299(1)
11.6 Duality Approach 300(5)
11.7 Infinite Horizon Case 305(22)
References 324(3)
12 Stochastic Differential Equations and Levy Models with Applications to High Frequency Data 327(20)
Ernest Barany
Maria Pia Beccar Varela
12.1 Solutions to Stochastic Differential Equations 327(7)
12.2 Stable Distributions 334(2)
12.3 The Levy Flight Models 336(4)
12.4 Numerical Simulations and Levy Models: Applications to Models Arising in Financial Indices and High Frequency Data 340(5)
12.5 Discussion and Conclusions 345(2)
References 346(1)
13 Solutions to Integro-Differential Parabolic Problem Arising on Financial Mathematics 347(36)
Maria C. Mariani
Marc Salas
Indranil Sen Gupta
13.1 Introduction 347(4)
13.2 Method of Upper and Lower Solutions 351(13)
13.3 Another Iterative Method 364(11)
13.4 Integro-Differential Equations in a Levy Market 375(8)
References 380(3)
14 Existence of Solutions for Financial Models with Transaction Costs and Stochastic Volatility 383(38)
Maria C. Mariani
Emmanuel K. Ncheuguim
Indranil Sen Gupta
14.1 Model with Transaction Costs 383(3)
14.2 Review of Functional Analysis 386(5)
14.3 Solution of the Problem (14.2) and (14.3) in Sobolev Spaces 391(9)
14.4 Model with Transaction Costs and Stochastic Volatility 400(8)
14.5 The Analysis of the Resulting Partial Differential Equation 408(13)
References 418(3)
Index 421
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