Overview

Despite the different nature of financial engineering and electrical engineering, both areas are intimately connected on a mathematical level. The foundations of financial engineering lie on the statistical analysis of numerical time series and the modeling of the behavior of the financial markets in order to perform predictions and systematically optimize investment strategies. Similarly, the foundations of electrical engineering, for instance, wireless communication systems, lie on statistical signal processing and the modeling of communication channels in order to perform predictions and systematically optimize transmission strategies. Both foundations are the same in disguise. It is often the case in science that the same or very similar methodologies are developed and applied independently in different areas. A Signal Processing Perspective of Financial Engineering is about investment in financial assets treated as a signal processing and optimization problem. It explores such connections and capitalizes on the existing mathematical tools developed in wireless communications and signal processing to solve real-life problems arising in the financial markets in an unprecedented way. It provides straightforward and systematic access to financial engineering for researchers in signal processing and communications so that they can understand problems in financial engineering more easily and may even apply signal processing techniques to handle some financial problems.

Full Product Details

Author:   Yiyong Feng ,  Daniel P. Palomar
Publisher:   now publishers Inc
Imprint:   now publishers Inc
Dimensions:   Width: 15.60cm , Height: 1.40cm , Length: 23.40cm
Weight:   0.365kg
ISBN:  

9781680831184

ISBN 10:   1680831186
Pages:   256
Publication Date:   11 August 2016
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1. Introduction Part I. Financial Modeling and Order Execution 2. Modeling of Financial Time Series 3. Modeling Fitting: Mean and Covariance Matrix Estimators 4. Order Execution Part II. Portfolio Optimization (Risk-Return Trade-off) 5. Portfolio Optimization with Known Parameters 6. Robust Portfolio Optimization 7. Multi-Portfolio Optimization 8. Index Tracking 9. Risk Parity Portfolio Optimization Part III. Statistical Arbitrage (Mean-Reversion) 10. Statistical Arbitrage Abbreviations and Notation Acknowledgments References

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