{"product_id":"handbook-of-modeling-highfrequency-data-in-finance-9780470876886","title":"Handbook of Modeling HighFrequency Data in Finance","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eCUTTING-EDGE DEVELOPMENTS IN HIGH-FREQUENCY FINANCIAL ECONOMETRICS\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e   \u003cp\u003eIn 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. \u003ci\u003eHandbook of Modeling High-Frequency Data in Finance\u003c\/i\u003e addresses the many theoretical and practical questions raised by the nature and intrinsic properties of this data.\u003c\/p\u003e \u003cp\u003eA 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDesigning new methodology to discover elasticity and plasticity of price evolution\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eConstructing microstructure simulation models\u003c\/p\u003e \u003c\/li\u003e \u003cl\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eContributors xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Analysis of Empirical Data 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Estimation of NIG and VG Models for High Frequency Financial Data 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJosé E. Figueroa-López Steven R. Lancette Kiseop Lee and Yanhui mi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 The Statistical Models 6\u003c\/p\u003e \u003cp\u003e1.3 Parametric Estimation Methods 9\u003c\/p\u003e \u003cp\u003e1.4 Finite-Sample Performance via Simulations 14\u003c\/p\u003e \u003cp\u003e1.5 Empirical Results 18\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 22\u003c\/p\u003e \u003cp\u003eReferences 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Study of Persistence of Price Movement using High Frequency Financial Data 27\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDragos Bozdog Ionuţ Florescu Khaldoun Khashanah and Jim Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 27\u003c\/p\u003e \u003cp\u003e2.2 Methodology 29\u003c\/p\u003e \u003cp\u003e2.3 Results 35\u003c\/p\u003e \u003cp\u003e2.4 Rare Events Distribution 41\u003c\/p\u003e \u003cp\u003e2.5 Conclusions 44\u003c\/p\u003e \u003cp\u003eReferences 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Using Boosting for Financial Analysis and Trading 47\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGermán Creamer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 Methods 48\u003c\/p\u003e \u003cp\u003e3.3 Performance Evaluation 53\u003c\/p\u003e \u003cp\u003e3.4 Earnings Prediction and Algorithmic Trading 60\u003c\/p\u003e \u003cp\u003e3.5 Final Comments and Conclusions 66\u003c\/p\u003e \u003cp\u003eReferences 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Impact of Correlation Fluctuations on Securitized structures 75\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eEric Hillebrand Ambar N. Sengupta and Junyue Xu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 75\u003c\/p\u003e \u003cp\u003e4.2 Description of the Products and Models 77\u003c\/p\u003e \u003cp\u003e4.3 Impact of Dynamics of Default Correlation on Low-Frequency Tranches 79\u003c\/p\u003e \u003cp\u003e4.4 Impact of Dynamics of Default Correlation on High-Frequency Tranches 87\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 92\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Construction of Volatility Indices Using A Multinomial Tree Approximation Method 97\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDragos Bozdog Ionuţ Florescu Khaldoun Khashanah and Hongwei Qiu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 97\u003c\/p\u003e \u003cp\u003e5.2 New Methodology 99\u003c\/p\u003e \u003cp\u003e5.3 Results and Discussions 101\u003c\/p\u003e \u003cp\u003e5.4 Summary and Conclusion 110\u003c\/p\u003e \u003cp\u003eReferences 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Long Range Dependence Models 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Long Correlations Applied to the Study of Memory Effects in High Frequency (TICK) Data the Dow Jones Index and International Indices 119\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eErnest Barany and Maria Pia Beccar Varela\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 119\u003c\/p\u003e \u003cp\u003e6.2 Methods Used for Data Analysis 122\u003c\/p\u003e \u003cp\u003e6.3 Data 128\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussions 132\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 150\u003c\/p\u003e \u003cp\u003eReferences 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Risk Forecasting with GARCH Skewed t Distributions and Multiple Timescales 163\u003ci\u003e\u003cbr\u003e \u003c\/i\u003e\u003c\/b\u003e\u003ci\u003eAlec N. Kercheval and Yang Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 163\u003c\/p\u003e \u003cp\u003e7.2 The Skewed t Distributions 165\u003c\/p\u003e \u003cp\u003e7.3 Risk Forecasts on a Fixed Timescale 176\u003c\/p\u003e \u003cp\u003e7.4 Multiple Timescale Forecasts 185\u003c\/p\u003e \u003cp\u003e7.5 Backtesting 188\u003c\/p\u003e \u003cp\u003e7.6 Further Analysis: Long-Term GARCH and Comparisons using Simulated Data 203\u003c\/p\u003e \u003cp\u003e7.7 Conclusion 216\u003c\/p\u003e \u003cp\u003eReferences 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Parameter Estimation and Calibration for Long-Memory Stochastic Volatility Models 219\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAlexandra Chronopoulou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 219\u003c\/p\u003e \u003cp\u003e8.2 Statistical Inference Under the LMSV Model 222\u003c\/p\u003e \u003cp\u003e8.3 Simulation Results 227\u003c\/p\u003e \u003cp\u003e8.4 Application to the S\u0026amp;P Index 228\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 229\u003c\/p\u003e \u003cp\u003eReferences 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Analytical Results 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 A Market Microstructure Model of Ultra High Frequency Trading 235\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCarlos A. Ulibarri and Peter C. Anselmo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 235\u003c\/p\u003e \u003cp\u003e9.2 Microstructural Model 237\u003c\/p\u003e \u003cp\u003e9.3 Static Comparisons 239\u003c\/p\u003e \u003cp\u003e9.4 Questions for Future Research 241\u003c\/p\u003e \u003cp\u003eReferences 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multivariate Volatility Estimation with High Frequency Data Using Fourier Method 243\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMariaElviraMancinoandSimonaSanfelici\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 243\u003c\/p\u003e \u003cp\u003e10.2 Fourier Estimator of Multivariate Spot Volatility 246\u003c\/p\u003e \u003cp\u003e10.3 Fourier Estimator of Integrated Volatility in the Presence of Microstructure Noise 252\u003c\/p\u003e \u003cp\u003e10.4 Fourier Estimator of Integrated Covariance in the Presence of Microstructure Noise 263\u003c\/p\u003e \u003cp\u003e10.5 Forecasting Properties of Fourier Estimator 272\u003c\/p\u003e \u003cp\u003e10.6 Application: Asset Allocation 286\u003c\/p\u003e \u003cp\u003eReferences 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The ‘‘Retirement’’ Problem 295\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCristian Pasarica\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 295\u003c\/p\u003e \u003cp\u003e11.2 The Market Model 296\u003c\/p\u003e \u003cp\u003e11.3 Portfolio and Wealth Processes 297\u003c\/p\u003e \u003cp\u003e11.4 Utility Function 299\u003c\/p\u003e \u003cp\u003e11.5 The Optimization Problem in the Case π (τT ] ≡ 0 299\u003c\/p\u003e \u003cp\u003e11.6 Duality Approach 300\u003c\/p\u003e \u003cp\u003e11.7 Infinite Horizon Case 305\u003c\/p\u003e \u003cp\u003eReferences 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Stochastic Differential Equations and Levy Models with Applications to High Frequency Data 327\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eErnest Barany and Maria Pia Beccar Varela\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Solutions to Stochastic Differential Equations 327\u003c\/p\u003e \u003cp\u003e12.2 Stable Distributions 334\u003c\/p\u003e \u003cp\u003e12.3 The Levy Flight Models 336\u003c\/p\u003e \u003cp\u003e12.4 Numerical Simulations and Levy Models: Applications to Models Arising in Financial Indices and High Frequency Data 340\u003c\/p\u003e \u003cp\u003e12.5 Discussion and Conclusions 345\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Solutions to Integro-Differential Parabolic Problem Arising on Financial Mathematics 347\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMaria C. Mariani Marc Salas and Indranil SenGupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 347\u003c\/p\u003e \u003cp\u003e13.2 Method of Upper and Lower Solutions 351\u003c\/p\u003e \u003cp\u003e13.3 Another Iterative Method 364\u003c\/p\u003e \u003cp\u003e13.4 Integro-Differential Equations in a Lévy Market 375\u003c\/p\u003e \u003cp\u003eReferences 380\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Existence of Solutions for Financial Models with Transaction Costs and Stochastic Volatility 383\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMaria C. Mariani Emmanuel K. Ncheuguim and Indranil SenGupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Model with Transaction Costs 383\u003c\/p\u003e \u003cp\u003e14.2 Review of Functional Analysis 386\u003c\/p\u003e \u003cp\u003e14.3 Solution of the Problem (14.2) and (14.3) in Sobolev Spaces 391\u003c\/p\u003e \u003cp\u003e14.4 Model with Transaction Costs and Stochastic Volatility 400\u003c\/p\u003e \u003cp\u003e14.5 The Analysis of the Resulting Partial Differential Equation 408\u003c\/p\u003e \u003cp\u003eReferences 418\u003c\/p\u003e \u003cp\u003eIndex 421 \u003c\/p\u003e\u003c\/l\u003e\n\u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":53515419943255,"sku":"9780470876886","price":134.06,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/handbook-of-modeling-highfrequency-data-in-finance-9780470876886","provider":"Book Curl","version":"1.0","type":"link"}