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Financial signal processing and machine learning /
The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for th...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
West Sussex, United Kingdom :
Wiley : IEEE Press,
2016.
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| Colección: | Wiley - IEEE
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Title Page; Copyright; Table of Contents; List of Contributors; Preface; Chapter 1: Overview; 1.1 Introduction; 1.2 A Bird's-Eye View of Finance; 1.3 Overview of the Chapters; 1.4 Other Topics in Financial Signal Processing and Machine Learning; References; Chapter 2: Sparse Markowitz Portfolios; 2.1 Markowitz Portfolios; 2.2 Portfolio Optimization as an Inverse Problem: The Need for Regularization; 2.3 Sparse Portfolios; 2.4 Empirical Validation; 2.5 Variations on the Theme; 2.6 Optimal Forecast Combination; Acknowlegments; References; Chapter 3: Mean-Reverting Portfolios; 3.1 Introduction.
- 3.2 Proxies for Mean Reversion3.3 Optimal Baskets; 3.4 Semidefinite Relaxations and Sparse Components; 3.5 Numerical Experiments; 3.6 Conclusion; References; Chapter 4: Temporal Causal Modeling; 4.1 Introduction; 4.2 TCM; 4.3 Causal Strength Modeling; 4.4 Quantile TCM (Q-TCM); 4.5 TCM with Regime Change Identification; 4.6 Conclusions; References; Chapter 5: Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process; 5.1 Introduction; 5.2 Mathematical Definitions; 5.3 Derivation of Explicit KLT Kernel for a Discrete AR(1) Process; 5.4 Sparsity of Eigen Subspace; 5.5 Conclusions.
- Chapter 8: Statistical Measures of Dependence for Financial Data8.1 Introduction; 8.2 Robust Measures of Correlation and Autocorrelation; 8.3 Multivariate Extensions; 8.4 Copulas; 8.5 Types of Dependence; References; Chapter 9: Correlated Poisson Processes and Their Applications in Financial Modeling; 9.1 Introduction; 9.2 Poisson Processes and Financial Scenarios; 9.3 Common Shock Model and Randomization of Intensities; 9.4 Simulation of Poisson Processes; 9.5 Extreme Joint Distribution; 9.6 Numerical Results; 9.7 Backward Simulation of the Poisson-Wiener Process; 9.8 Concluding Remarks.
- AcknowledgmentsAppendix A; References; Chapter 10: CVaR Minimizations in Support Vector Machines; 10.1 What Is CVaR?; 10.2 Support Vector Machines; 10.3 v-SVMs as CVaR Minimizations; 10.4 Duality; 10.5 Extensions to Robust Optimization Modelings; 10.6 Literature Review; References; Chapter 11: Regression Models in Risk Management; 11.1 Introduction; 11.2 Error and Deviation Measures; 11.3 Risk Envelopes and Risk Identifiers; 11.4 Error Decomposition in Regression; 11.5 Least-Squares Linear Regression; 11.6 Median Regression; 11.7 Quantile Regression and Mixed Quantile Regression.