{"product_id":"handbook-of-modeling-high-frequency-data-in-finance-hardback-9780470876886","title":"Handbook of Modeling High-Frequency Data in Finance (Hardback) 9780470876886","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eHandbook of Modeling High-Frequency Data in Finance\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eFrederi G. Viens (Author), Maria Cristina Mariani (Author), Ionut Florescu (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470876886, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 6 January 2012\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e464 pages, Tables: 150 B\u0026amp;W, 0 Color; Graphs: 125 B\u0026amp;W, 0 Color\u003cbr\u003e24.3 x 16.3 x 2.8 cm, 0.771 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\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 \u003cli\u003e \u003cp\u003eCalculation of option prices in the presence of jumps and transaction costs\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eUsing boosting for financial analysis and trading\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHandbook of Modeling High-Frequency Data in Finance\u003c\/i\u003e 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\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\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Finance \u0026amp; accounting [\u003ca title=\"See our other books on Finance \u0026amp; accounting\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Finance%20\u0026amp;%20accounting%20%5BKF%5D%22\"\u003eKF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52278087024920,"sku":"9780470876886","price":119.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470876886.jpg?v=1781458087","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/handbook-of-modeling-high-frequency-data-in-finance-hardback-9780470876886","provider":"Freshly Printed Books","version":"1.0","type":"link"}