Handbook of High-Frequency Trading and Modeling in Finance

A comprehensive collection of up–to–date empirical and analytical research within high–frequency finance Reflecting the fast pace and ever–evolving nature of the financial industry, the Handbook of High–Frequency Trading and Modeling in Finance details how high–frequency analysis presents new systematic approaches to implementing quantitative activities with high–frequency financial data. Introducing the mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as the portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high–frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies.The Handbook of High–Frequency Trading and Modeling in Finance also features: Contributions by well–known experts within the academic, industrial, and regulatory fields  A well–structured outline on the various data analysis methodologies used to identify new trading opportunities Newly–emerging quantitative tools that address growing concerns relating to high–frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit–order books and broader market indicators; and option markets Multiple practical applications using real–world  data to help readers better understand the presented material The Handbook of High–Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also an ideal supplement for graduate and MBA–level courses on quantitative finance, volatility, and financial econometrics. INDICE: 1. Trends and trades Carlisle Michael, Olympia Hadjiliadis, Ioannis Stamos .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 .1.6 The Effect of the Threshold Parameter .1.7 Conclusions and Future Work .Appendix .References .2. Gaussian Inequalities and Tranche Sensitivities Becker Claas and Ambar N. Sengupta .2.1 Introduction .2.2 The tranche loss function .2.3 A Sensitivity Identity .2.4 Correlation Sensitivities .Acknowledgment .References .3. A Non–linear Lead Lag Dependence Analysis of Energy Futures: Oil, Coal and Natural Gas Germán G. Creamer and Bernardo Creamer .3.1 Introduction .4.1.1.1 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 Marzec Michael .4.1 Introduction .4.2 BACKGROUND .4.2.1 Portfolios & 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 & QUBO Models .4.3.4 Mixed Models .4.4 METHODOLOGY .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 .4.5.3 The WMIS minimum–risk, max return model .4.6 DISCUSSION .4.6.1 Hardware Limitations .4.6.2 Model Limitations .4.6.3 Implementation Limitations .4.6.4 Future Research .4.6.5 Conclusion .Acknowledgements .Appendix A WMIS Matlab Code .References .5. Estimation procedure for regime switching stochastic volatility model and its applications Ionut Florescu and Forrest Levin .5.1 RESULTS .5.1.1 The original motivation .5.1.2 The model and the problem .5.1.3 A brief historical note .5.1.4 The methodology .5.1.5 Obtaining —ltered empirical distributions at t1; : : : ; tT .5.1.6 Obtaining the parameters of the Markov Chain .5.2 Results obtained applying the model to real data .5.2.1 Part I: Financial applications .5.2.1.1 The events as they happened .5.2.1.2 Data analysis and results .5.2.2 Part II: Physical data application. Temperature data .5.2.3 Part III: Analysis of seismometer readings during an Earthquake .5.2.4 Analysis of the earthquake signal. Beginning .5.2.5 Analysis: During the earthquake .5.2.6 Analysis: End of the earthquake signal, aftershocks .5.3 Conclusion .Appendices .References .6. Detecting Jumps in High–Frequency Prices under Stochastic Volatility: a Review and a Data–Driven Approach Tsai Ping–Chen and Mark B. Shackleton .6.1 Introduction .6.2 Review on the Intraday Jump Tests .6.2.1 Realized volatility measure and the BNS tests .6.2.2 The ABD and LM tests .6.3 A Data–driven Testing Procedure .6.3.1 SPY data and micro–structure noise .6.3.2 A generalized testing procedure .6.4 Simulation Study .6.4.1 Model specification .6.4.2 Simulation results .6.5 Empirical Results .6.6 Conclusion .Appendices .References .7. Hawkes processes and their Applications to High Frequency Data Modeling Law Baron and Viens Frederi .7.1 Introduction .7.2 Point Processes .7.3 Hawkes Processes .7.3.1 Branching Structure Representation .7.3.2 Stationarity .7.3.3 Convergence .7.3.3.1 Law of Large Numbers for Multivariate Linear Hawkes processes .7.3.3.2 Functional Central Limit Theorem for Multivariate Linear Hawkes processes .7.3.3.3 Functional Central Limit Theorem for Univariate Non–linear Hawkes processes .7.3.3.4 Convergence of Nearly Unstable Univariate Linear Hawkes processes .7.4 Statistical Inference of Hawkes Processes .7.4.1 Simulation .7.4.1.1 Inverse CDF Transform .7.4.1.2 Ogata s Modified Thinning .7.4.1.3 Simulation by Branching Structure .7.4.2 Estimation .7.4.2.1 Maximum Likelihood Estimation (MLE) .7.4.2.2 Expectation Maximization (EM) .7.4.2.3 Generalized Method of Moments (GMM) .7.4.2.4 Nonparametric Estimation .7.4.3 Hypothesis Testing .7.4.3.1 Random Time Change .7.4.3.2 Approximate Thinning .7.5 Applications of Hawkes processes .7.5.1 Modeling Order Arrivals .7.5.2 Modeling Price Jumps .7.5.2.1 Single Asset .7.5.2.2 Two Assets .7.5.3 Modeling Jump–Diffusion .7.5.4 Measuring Endogeneity (Reflexivity) .Appendices .References .8. Multifractal Random Walk Driven by a Hermite Process Fauth Alexis and Tudor Ciprian .8.1 Introduction .8.2 Preliminaries .8.2.1 Fractional Brownian motion and Hermite processes .8.2.2 Wiener integrals with respect to the Hermite process .8.2.3 Infinitely divisible cascading noise .8.3 Multifractal Random Walk driven by a Hermite process .8.3.1 Definition and existence .8.4 Definition and existence .8.4.1 Simulation of the HMRW .8.4.2 Financial statistics .8.5 Concluding Remarks .References .9. Interpolating techniques and non–parametric regression methods applied to geophysical and financial data analysis K. Basuand M.C. Mariani .9.1 Introduction .9.2 Nonparametric Regression Models .9.2.1 Local Polynomial Regression .9.2.1.1 Simple Regression .9.2.1.2 Multiple Regression .9.2.2 Lowess/Loess Method .9.2.3 Numerical Applications .9.2.3.1 Application to geophysics .9.2.3.2 Application to financial data sampled with high frequency .9.3 Interpolation Methods .9.3.1 Nearest–neighbor interpolation .9.3.2 Bilinear Interpolation .9.3.2.1 Algorithm .9.3.2.2 Unit square .9.3.2.3 Nonlinear .9.3.3 Bicubic Interpolation .9.3.4 Biharmonic Interpolation .9.3.5 Thin plate splines .9.3.5.1 Physical analogy .9.3.5.2 Smoothness measure .9.3.5.3 Radial basis function .9.3.6 Numerical Applications .9.4 Conclusion .Acknowledgments .References .10. Study of Volatility Structures in Geophysics and Finance Using Garch Models M.C Mariani and F. Biney .10.1 Introduction .10.2 Short Memory Models .10.2.1 ARMA(p,q) Model .10.2.2 GARCH(p,q) Model .10.2.3 IGARCH(1,1) Model .10.3 Long Memory Models .10.3.1 ARFIMA(p,d,q) model .10.3.2 ARFIMA(p,d,q)–GARCH(r,s) .10.3.3 Intermediate memory process .10.3.4 FIGARCH model .10.4 Detection and Estimation of Long Memory .10.4.1 Augmented Dickey Fuller test(ADF Test) .10.4.2 KPSS Test .10.4.3 Whittle method .10.5 Data Collection, Analysis and Result .10.5.1 Analysis on Dow Jones Index (DJIA) Returns .10.5.2 Model selection and specification: conditional mean .10.5.3 Conditional mean model (Returns) .10.5.4 Model Diagnostics: ARMA(2; 2) .10.5.5 Test for Arch effect .10.5.6 Model selection and specification: conditional variance .10.5.7 Standardized Residuals Test .10.5.8 Model Diagnostics .10.5.9 Returns and Variance equation .10.5.10 Standardized Residuals Test .10.5.11 Model Diagnostic of Conditional Returns with conditional variance .10.5.12 One–step ahead Prediction of last 10 observations .10.5.13 Analysis on High–frequency, Earthquake and Explosives series .10.6 Discussion and conclusion .References .11. Scale invariance and Lévy models applied to earthquakes and financial high frequency data I. Florescu, M. P. Beccar–Varela, I. SenGupta .11.1 Introduction .11.2 Governing equations for the deterministic model .11.2.1 Application to geophysical (earthquake data) .11.2.2 Results .11.3 Lévy flights and application to Geophysics .11.3.1 Truncated Lévy Flight distribution .11.3.2 Results .11.4 Application to the High Frequency Market Data (HFD) .11.4.1 Methodology .11.4.2 Results .11.5 Brief program code description .11.6 Conclusion .Appendix .References .12. Analysis of Generic Diversity in the Fossil Record, Earthquake series and High–frequency financial data Beccar Varela M.P., F. Biney, M.C. Mariani, I. SenGupta, M. Sphak, P. Bezdek .12.1 Introduction .12.2 Statistical preliminaries and results .12.2.1 Sum of exponential random variables with different parameters .12.2.1.1 General version for n random variables .12.3 Statistical and numerical analysis .12.4 Analysis with Lévy distribution .12.4.1 Characterization of stable distributions .12.4.2 Truncated Lévy Flight (TLF) distribution .12.4.2.1 Kurtosis .12.4.2.2 Infinite divisibility .12.4.3 Data analysis with TLF distribution .12.4.4 Sum of Lévy random variables with different parameters .12.5 Analysis of the Stock Indices, high–frequency (tick) data and explosive series .12.6 Results and discussion .Acknowledgments .Appendix .References

• ISBN: 978-1-118-44398-9
• Editorial: Wiley–Blackwell
• Encuadernacion: Cartoné
• Páginas: 456
• Fecha Publicación: 20/05/2016
• Nº Volúmenes: 1
• Idioma: Inglés