Financial Signal Processing and Machine Learning, First Edition by Akansu, Ali N. -- Read -- Imperial Library of Trantor

Index

Cover 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 Reversion 3.3 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 References

Chapter 6: Approaches to High-Dimensional Covariance and Precision Matrix Estimations

6.1 Introduction 6.2 Covariance Estimation via Factor Analysis 6.3 Precision Matrix Estimation and Graphical Models 6.4 Financial Applications 6.5 Statistical Inference in Panel Data Models 6.6 Conclusions References

Chapter 7: Stochastic Volatility

7.1 Introduction 7.2 Asymptotic Regimes and Approximations 7.3 Merton Problem with Stochastic Volatility: Model Coefficient Polynomial Expansions 7.4 Conclusions Acknowledgements References

Chapter 8: Statistical Measures of Dependence for Financial Data

8.1 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 Acknowledgments Appendix 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 11.8 Special Types of Linear Regression 11.9 Robust Regression References, Further Reading, and Bibliography

Index End User License Agreement