LEADER 05830nam 2200805Ia 450 001 9910811779903321 005 20200520144314.0 010 $a9786613332844 010 $a9781283332842 010 $a1283332841 010 $a9781118204580 010 $a1118204581 010 $a9781118204566 010 $a1118204565 010 $a9781118204634 010 $a1118204638 035 $a(CKB)2550000000064746 035 $a(EBL)818537 035 $a(OCoLC)768572208 035 $a(SSID)ssj0000554839 035 $a(PQKBManifestationID)11344552 035 $a(PQKBTitleCode)TC0000554839 035 $a(PQKBWorkID)10517290 035 $a(PQKB)11036417 035 $a(MiAaPQ)EBC818537 035 $a(Au-PeEL)EBL818537 035 $a(CaPaEBR)ebr10510644 035 $a(CaONFJC)MIL333284 035 $a(iGPub)WILEYB0016091 035 $a(Perlego)1013263 035 $a(EXLCZ)992550000000064746 100 $a20110921d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHandbook of modeling high-frequency data in finance /$fFrederi G. Viens, Maria C. Mariani, Ionut Florescu 205 $a1. 210 $aHoboken, NJ $cWiley$dc2012 215 $a1 online resource (457 p.) 225 1 $aWiley handbooks in financial engineering and econometrics ;$v4 300 $aDescription based upon print version of record. 311 08$a9780470876886 311 08$a0470876883 320 $aIncludes bibliographical references and index. 327 $aHandbook of Modeling High-Frequency Data in Finance; Contents; Preface; Contributors; Part One Analysis of Empirical Data; 1 Estimation of NIG and VG Models for High Frequency Financial Data; 1.1 Introduction; 1.2 The Statistical Models; 1.3 Parametric Estimation Methods; 1.4 Finite-Sample Performance via Simulations; 1.5 Empirical Results; 1.6 Conclusion; References; 2 A Study of Persistence of Price Movement using High Frequency Financial Data; 2.1 Introduction; 2.2 Methodology; 2.3 Results; 2.4 Rare Events Distribution; 2.5 Conclusions; References 327 $a3 Using Boosting for Financial Analysis and Trading3.1 Introduction; 3.2 Methods; 3.3 Performance Evaluation; 3.4 Earnings Prediction and Algorithmic Trading; 3.5 Final Comments and Conclusions; References; 4 Impact of Correlation Fluctuations on Securitized structures; 4.1 Introduction; 4.2 Description of the Products and Models; 4.3 Impact of Dynamics of Default Correlation on Low-Frequency Tranches; 4.4 Impact of Dynamics of Default Correlation on High-Frequency Tranches; 4.5 Conclusion; References; 5 Construction of Volatility Indices Using A Multinomial Tree Approximation Method 327 $a5.1 Introduction5.2 New Methodology; 5.3 Results and Discussions; 5.4 Summary and Conclusion; References; Part Two Long Range Dependence Models; 6 Long Correlations Applied to the Study of Memory Effects in High Frequency (TICK) Data, the Dow Jones Index, and International Indices; 6.1 Introduction; 6.2 Methods Used for Data Analysis; 6.3 Data; 6.4 Results and Discussions; 6.5 Conclusion; References; 7 Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales; 7.1 Introduction; 7.2 The Skewed t Distributions; 7.3 Risk Forecasts on a Fixed Timescale 327 $a7.4 Multiple Timescale Forecasts7.5 Backtesting; 7.6 Further Analysis: Long-Term GARCH and Comparisons using Simulated Data; 7.7 Conclusion; References; 8 Parameter Estimation and Calibration for Long-Memory Stochastic Volatility Models; 8.1 Introduction; 8.2 Statistical Inference Under the LMSV Model; 8.3 Simulation Results; 8.4 Application to the S&P Index; 8.5 Conclusion; References; Part Three Analytical Results; 9 A Market Microstructure Model of Ultra High Frequency Trading; 9.1 Introduction; 9.2 Microstructural Model; 9.3 Static Comparisons; 9.4 Questions for Future Research 327 $aReferences10 Multivariate Volatility Estimation with High Frequency Data Using Fourier Method; 10.1 Introduction; 10.2 Fourier Estimator of Multivariate Spot Volatility; 10.3 Fourier Estimator of Integrated Volatility in the Presence of Microstructure Noise; 10.4 Fourier Estimator of Integrated Covariance in the Presence of Microstructure Noise; 10.5 Forecasting Properties of Fourier Estimator; 10.6 Application: Asset Allocation; References; 11 The "Retirement" Problem; 11.1 Introduction; 11.2 The Market Model; 11.3 Portfolio and Wealth Processes; 11.4 Utility Function 327 $a11.5 The Optimization Problem in the Case p(t,T] o 0 330 $aCUTTING-EDGE DEVELOPMENTS IN HIGH-FREQUENCY FINANCIAL ECONOMETRICS In recent years, the availability of high-frequency data and advances in computing have allowed financial practitioners to design systems that can handle and analyze this information. Handbook of Modeling High-Frequency Data in Finance addresses the many theoretical and practical questions raised by the nature and intrinsic properties of this data. A one-stop compilation of empirical and analytical research, this handbook explores data sampled with high-frequency finance in financial engineering, stati 410 0$aWiley handbooks in financial engineering and econometrics ;$v4. 606 $aFinance$xEconometric models 606 $aEconometric models 615 0$aFinance$xEconometric models. 615 0$aEconometric models. 676 $a332.01/5195 686 $aBUS027000$2bisacsh 700 $aViens$b Frederi G.$f1969-$01609150 701 $aFlorescu$b Ionut$f1973-$0525052 701 $aMariani$b Maria C$0862418 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910811779903321 996 $aHandbook of modeling high-frequency data in finance$93936264 997 $aUNINA