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作者 Akansu, Ali N
書名 Financial Signal Processing and Machine Learning
出版項 Chicester : John Wiley & Sons, Incorporated, 2016
©2016
國際標準書號 9781118745632 (electronic bk.)
9781118745540
book jacket
版本 1st ed
說明 1 online resource (384 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
系列 Wiley - IEEE Ser
Wiley - IEEE Ser
附註 Intro -- 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
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
鏈接 Print version: Akansu, Ali N. Financial Signal Processing and Machine Learning Chicester : John Wiley & Sons, Incorporated,c2016 9781118745540
主題 Machine learning
Electronic books
Alt Author Kulkarni, Sanjeev R
Malioutov, Dmitry M
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