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Handbook of Modeling High-Frequency Data in Finance

Handbook of Modeling High-Frequency Data in Finance

  • Author:
  • Publisher: John Wiley & Sons
  • ISBN: 9780470876886
  • Published In: November 2011
  • Format: Hardback , 456 pages
  • Jurisdiction: International ? Disclaimer:
    Countri(es) stated herein are used as reference only

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  • Description 
  • Contents 
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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.

  • 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, Ionut¸ 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, Ionut¸ 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
    Maria Elvira Mancino and Simona Sanfelici

    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 π(t ,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

  • 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.

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