Handbook of high-frequency trading and modeling in finance /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Florescu, Ionuţ, 1973- (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : Wiley, [2016]
Temas:
Investment analysis > Mathematical models.
Investments > Mathematical models.
Finance > Mathematical models.
Analyse financière > Modèles mathématiques.
Investissements > Modèles mathématiques.
Finances > Modèles mathématiques.
BUSINESS & ECONOMICS > Finance.
Finance > Mathematical models
Investment analysis > Mathematical models
Investments > Mathematical models
Acceso en línea:Texto completo

MARC

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245 0 0 |a Handbook of high-frequency trading and modeling in finance /  |c edited by Ionut Florescu, Maria C. Mariani, H. Eugene Stanley, Frederi G. Viens. 
264 1 |a Hoboken, NJ :  |b Wiley,  |c [2016] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a Includes index. 
588 0 |a Print version record and CIP data provided by publisher. 
504 |a Includes bibliographical references and index. 
505 0 |a Handbook of High-Frequency Trading and Modeling in Finance; Contents; Notes on Contributors; Editors; List of Contributors; Preface; 1 Trends and Trades; 1.1 Introduction; 1.2 A trend-based trading strategy; 1.2.1 signaling and trends; 1.2.2 gain over a subperiod; 1.3 CUSUM timing; 1.3.1 cusum process and stopping time; 1.3.2 a cusum timing scheme; 1.3.3 us treasury notes, cusum timing; 1.4 Example: Random walk on ticks; 1.4.1 random walk expected gain over a subperiod; 1.4.2 simple random walk, CUSUM timing; 1.4.3 lazy simple random walk, cusum timing; 1.5 CUSUM strategy Monte Carlo. 
505 8 |a 1.6 The effect of the threshold parameter1.7 Conclusions and future work; Appendix: Tables; References; 2 Gaussian Inequalities and Tranche Sensitivities; 2.1 Introduction; 2.2 The tranche loss function; 2.3 A sensitivity identity; 2.4 Correlation sensitivities; Acknowledgment; References; 3 A Nonlinear Lead Lag Dependence Analysis of Energy Futures: Oil, Coal, and Natural Gas; 3.1 Introduction; 3.1.1 causality analysis; 3.2 Data; 3.3 Estimation techniques; 3.4 Results; 3.5 Discussion; 3.6 Conclusions; Acknowledgments; References; 4 Portfolio Optimization: Applications in Quantum Computing. 
505 8 |a 4.1 Introduction4.2 Background; 4.2.1 Portfolios And Optimization; 4.2.2 Algorithmic Complexity; 4.2.3 Performance; 4.2.4 Ising Model; 4.2.5 Adiabatic Quantum Computing; 4.3 The models; 4.3.1 Financial Model; 4.3.2 Graph-Theoretic Combinatorial Optimization Models; 4.3.3 Ising And Qubo Models; 4.3.4 Mixed Models; 4.4 Methods; 4.4.1 Model Implementation; 4.4.2 Input Data; 4.4.3 Mean-Variance Calculations; 4.4.4 Implementing The Risk Measure; 4.4.5 Implementation Mapping; 4.5 Results; 4.5.1 The Simple Correlation Model; 4.5.2 The Restricted Minimum-Risk Model. 
505 8 |a 4.5.3 The WMIS Minimum-Risk, Max Return Model4.6 Discussion; 4.6.1 Hardware Limitations; 4.6.2 Model Limitations; 4.6.3 Implementation Limitations; 4.6.4 Future Research; 4.7 Conclusion; Acknowledgments; Appendix 4.A: WMIS Matlab Code; References; 5 Estimation Procedure for Regime Switching Stochastic Volatility Model and Its Applications; 5.1 Introduction; 5.1.1 the original motivation; 5.1.2 the model and the problem; 5.1.3 a brief historical note; 5.2 The methodology; 5.2.1 obtaining filtered empirical distributions at ; 5.2.2 obtaining the parameters of the markov chain. 
505 8 |a 5.3 Results obtained applying the model to real data5.3.1 part i: financial applications; 5.3.2 part ii: physical data application. temperature data; 5.3.3 part iii: analysis of seismometer readings during an earthquake; 5.3.4 analysis of the earthquake signal: beginning; 5.3.5 analysis: during the earthquake; 5.3.6 analysis: end of the earthquake signal, aftershocks; 5.4 Conclusion; Appendix 5.A:Theoretical results and empirical testing; 5.A.1 how does the particle filter work?; 5.A.2 theoretical results about convergence and parameter estimates; 5.A.3 markov chain parameter estimates. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Investment analysis  |x Mathematical models. 
650 0 |a Investments  |x Mathematical models. 
650 0 |a Finance  |x Mathematical models. 
650 6 |a Analyse financière  |x Modèles mathématiques. 
650 6 |a Investissements  |x Modèles mathématiques. 
650 6 |a Finances  |x Modèles mathématiques. 
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650 7 |a Investments  |x Mathematical models  |2 fast 
700 1 |a Florescu, Ionuţ,  |d 1973-  |e editor. 
758 |i has work:  |a Handbook of high-frequency trading and modeling in finance (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCH993RMVTqbgWX3gBPjFjy  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |t Handbook of high-frequency trading and modeling in finance.  |d Hoboken, NJ : John Wiley & Sons, Inc., [2016]  |z 9781118443989  |w (DLC) 2015043237 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4462505  |z Texto completo 
880 8 |6 505-00/(S  |a Appendix 6.C: Minimized loss function loss(ρ1, ρ2) for SV2FJ_2ρ model, SPY -- Appendix 6.D.1: Calibration of ξ under SV2FJ_2ρ model at 2-min frequency, E[Nt] = 0.08 -- Appendix 6.D.2: Calibration of ξ under SV2FJ_2ρ model at 2-min frequency, E[Nt] = 0.40 -- Appendix 6.D.3: Calibration of ξ under SV2FJ_2ρ model at 5-min frequency, E[Nt] = 0.08 -- Appendix 6.D.4: Calibration of ξ under SV2FJ_2ρ Model at 5-min frequency, E[Nt] = 0.40 -- Appendix 6.D.5: Calibration of ξ under SV2FJ_2ρ model at 10-min frequency, E[Nt] = 0.08 -- Appendix 6.D.6: Calibration of ξ under SV2FJ_2ρ model at 10-min frequency, E[Nt] = 0.40 -- References -- Notes -- Chapter 7 Hawkes Processes and Their Applications to High-Frequency Data Modeling -- 7.1 Introduction -- 7.2 Point processes -- 7.3 Hawkes processes -- 7.4 Statistical inference of Hawkes processes -- 7.5 Applications of Hawkes processes -- Appendix 7.A: Point Processes -- Appendix 7.B: A Brief History of Hawkes processes -- References -- Notes -- Chapter 8 Multifractal Random Walk Driven by a Hermite Process: -- 8.1 Introduction -- 8.2 Preliminaries -- 8.3 Multifractal random walk driven by a Hermite process -- 8.4 Financial applications -- 8.5 Concluding remarks -- References -- Notes -- Chapter 9 Interpolating Techniques and Nonparametric Regression Methods Applied to Geophysical and Financial Data Analysis -- 9.1 Introduction -- 9.2 Nonparametric regression models -- 9.3 Interpolation methods -- 9.4 Conclusion -- Acknowledgments -- References -- Chapter 10 Study of Volatility Structures in Geophysics and Finance Using Garch Models -- 10.1 Introduction -- 10.2 Short memory models -- 10.3 Long memory models -- 10.4 Detection and estimation of long memory -- 10.5 Data collection, analysis, and result -- 10.6 Discussion and conclusion -- References. 
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