Frederi G. Viens, Maria C. Mariani, Ionut Florescu

Handbook of Modeling High-Frequency Data in Finance

• Gebundenes Buch

Produktbeschreibung

Written and edited by leading, international experts in the field, Handbook of Modeling High-Frequency Data in Finance 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, and systems and complex adaptive systems in finance, among others. By using examples derived from consulting projects, current research, and course instruction, each chapter offers practitioners a systematic understanding of the recent advances in high-frequency modeling related to real-world situations.

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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.

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Produktdetails

• Wiley Handbooks in Financial Engineering and Econometrics .
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 464
• Erscheinungstermin: 20. Dezember 2011
• Englisch
• Abmessung: 236mm x 157mm x 28mm
• Gewicht: 717g
• ISBN-13: 9780470876886
• ISBN-10: 0470876883
• Artikelnr.: 33609099

Autorenporträt

Frederi G. Viens, PhD, 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, 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.

Inhaltsangabe

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 Ionü Florescu Khaldoun Khashanah and Jim Wang 2.1 Introduction 27 2.2 Methodology 29 2.3 Results 35 2.4 Rare Events Distribution 41 2.5 Conclusions 44 References 45 3 Using Boosting for Financial Analysis and Trading 47 Germán Creamer 3.1 Introduction 47 3.2 Methods 48 3.3 Performance Evaluation 53 3.4 Earnings Prediction and Algorithmic Trading 60 3.5 Final Comments and Conclusions 66 References 69 4 Impact of Correlation Fluctuations on Securitized structures 75 Eric Hillebrand Ambar N. Sengupta and Junyue Xu 4.1 Introduction 75 4.2 Description of the Products and Models 77 4.3 Impact of Dynamics of Default Correlation on Low-Frequency Tranches 79 4.4 Impact of Dynamics of Default Correlation on High-Frequency Tranches 87 4.5 Conclusion 92 References 94 5 Construction of Volatility Indices Using A Multinomial Tree Approximation Method 97 Dragos Bozdog Ionü Florescu Khaldoun Khashanah and Hongwei Qiu 5.1 Introduction 97 5.2 New Methodology 99 5.3 Results and Discussions 101 5.4 Summary and Conclusion 110 References 115 Part Two Long Range Dependence Models 117 6 Long Correlations Applied to the Study of Memory Effects in High Frequency (TICK) Data the Dow Jones Index and International Indices 119 Ernest Barany and Maria Pia Beccar Varela 6.1 Introduction 119 6.2 Methods Used for Data Analysis 122 6.3 Data 128 6.4 Results and Discussions 132 6.5 Conclusion 150 References 160 7 Risk Forecasting with GARCH Skewed t Distributions and Multiple Timescales 163 Alec N. Kercheval and Yang Liu 7.1 Introduction 163 7.2 The Skewed t Distributions 165 7.3 Risk Forecasts on a Fixed Timescale 176 7.4 Multiple Timescale Forecasts 185 7.5 Backtesting 188 7.6 Further Analysis: Long-Term GARCH and Comparisons using Simulated Data 203 7.7 Conclusion 216 References 217 8 Parameter Estimation and Calibration for Long-Memory Stochastic Volatility Models 219 Alexandra Chronopoulou 8.1 Introduction 219 8.2 Statistical Inference Under the LMSV Model 222 8.3 Simulation Results 227 8.4 Application to the S&P Index 228 8.5 Conclusion 229 References 230 Part Three Analytical Results 233 9 A Market Microstructure Model of Ultra High Frequency Trading 235 Carlos A. Ulibarri and Peter C. Anselmo 9.1 Introduction 235 9.2 Microstructural Model 237 9.3 Static Comparisons 239 9.4 Questions for Future Research 241 References 242 10 Multivariate Volatility Estimation with High Frequency Data Using Fourier Method 243 MariaElviraMancinoandSimonaSanfelici 10.1 Introduction 243 10.2 Fourier Estimator of Multivariate Spot Volatility 246 10.3 Fourier Estimator of Integrated Volatility in the Presence of Microstructure Noise 252 10.4 Fourier Estimator of Integrated Covariance in the Presence of Microstructure Noise 263 10.5 Forecasting Properties of Fourier Estimator 272 10.6 Application: Asset Allocation 286 References 290 11 The ''Retirement'' Problem 295 Cristian Pasarica 11.1 Introduction 295 11.2 The Market Model 296 11.3 Portfolio and Wealth Processes 297 11.4 Utility Function 299 11.5 The Optimization Problem in the Case
(
T ]
0 299 11.6 Duality Approach 300 11.7 Infinite Horizon Case 305 References 324 12 Stochastic Differential Equations and Levy Models with Applications to High Frequency Data 327 Ernest Barany and Maria Pia Beccar Varela 12.1 Solutions to Stochastic Differential Equations 327 12.2 Stable Distributions 334 12.3 The Levy Flight Models 336 12.4 Numerical Simulations and Levy Models: Applications to Models Arising in Financial Indices and High Frequency Data 340 12.5 Discussion and Conclusions 345 References 346 13 Solutions to Integro-Differential Parabolic Problem Arising on Financial Mathematics 347 Maria C. Mariani Marc Salas and Indranil SenGupta 13.1 Introduction 347 13.2 Method of Upper and Lower Solutions 351 13.3 Another Iterative Method 364 13.4 Integro-Differential Equations in a Lévy Market 375 References 380 14 Existence of Solutions for Financial Models with Transaction Costs and Stochastic Volatility 383 Maria C. Mariani Emmanuel K. Ncheuguim and Indranil SenGupta 14.1 Model with Transaction Costs 383 14.2 Review of Functional Analysis 386 14.3 Solution of the Problem (14.2) and (14.3) in Sobolev Spaces 391 14.4 Model with Transaction Costs and Stochastic Volatility 400 14.5 The Analysis of the Resulting Partial Differential Equation 408 References 418 Index 421