High Dimensionality In Statistics And Portfolio Optimization

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High Dimensionality In Statistics And Portfolio Optimization

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Author : Konstantin Glombek
language : en
Publisher: BoD – Books on Demand
Release Date : 2012

High Dimensionality In Statistics And Portfolio Optimization written by Konstantin Glombek and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.

Statistical Portfolio Estimation

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Author : Masanobu Taniguchi
language : en
Publisher: CRC Press
Release Date : 2017-09-01

Statistical Portfolio Estimation written by Masanobu Taniguchi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-01 with Mathematics categories.

The composition of portfolios is one of the most fundamental and important methods in financial engineering, used to control the risk of investments. This book provides a comprehensive overview of statistical inference for portfolios and their various applications. A variety of asset processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many important problems can be covered. This book can primarily be used as a reference by researchers from statistics, mathematics, finance, econometrics, and genomics. It can also be used as a textbook by senior undergraduate and graduate students in these fields.

Modern Nonparametric Robust And Multivariate Methods

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Author : Klaus Nordhausen
language : en
Publisher: Springer
Release Date : 2015-10-05

Modern Nonparametric Robust And Multivariate Methods written by Klaus Nordhausen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-05 with Mathematics categories.

Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the occasion of his 65th birthday and is intended for researchers as well as PhD students with a good knowledge of statistics.

Ica For Data Analysis And Interpretation

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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-04-13

Ica For Data Analysis And Interpretation written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-13 with Science categories.

ICA for Data Analysis and Interpretation delves into the fascinating world of Independent Component Analysis (ICA), a powerful signal processing technique that has revolutionized data analysis across diverse fields. This comprehensive guide provides a thorough exploration of ICA, from its mathematical foundations to its wide-ranging applications. Within the pages of this book, readers will embark on a journey through the theoretical underpinnings of ICA, gaining a deep understanding of the statistical models and algorithms that drive its success. The book delves into various ICA algorithms, comparing their strengths and limitations, and equipping readers with the knowledge to select the most appropriate algorithm for their specific needs. Moving beyond theory, the book showcases the practical applications of ICA in various domains. Readers will learn how ICA can be harnessed to separate signals, extract meaningful features from data, and uncover hidden patterns in complex datasets. Real-world examples and case studies illustrate the transformative power of ICA in fields such as speech enhancement, image processing, financial analysis, and neuroscience. With its clear explanations, insightful examples, and comprehensive coverage, ICA for Data Analysis and Interpretation is an invaluable resource for researchers, practitioners, and students seeking to master the art of ICA. Whether you're a data scientist, engineer, or simply someone fascinated by the power of data analysis, this book will provide you with the knowledge and tools to unlock the full potential of ICA. Discover the transformative power of ICA and gain the skills to uncover hidden insights from your data. ICA for Data Analysis and Interpretation is your essential guide to this cutting-edge technique, empowering you to solve complex problems and make informed decisions in an increasingly data-driven world. If you like this book, write a review on google books!

Statistical Models And Methods For Financial Markets

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Author : Tze Leung Lai
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-08

Statistical Models And Methods For Financial Markets written by Tze Leung Lai and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-08 with Business & Economics categories.

The idea of writing this bookarosein 2000when the ?rst author wasassigned to teach the required course STATS 240 (Statistical Methods in Finance) in the new M. S. program in ?nancial mathematics at Stanford, which is an interdisciplinary program that aims to provide a master’s-level education in applied mathematics, statistics, computing, ?nance, and economics. Students in the programhad di?erent backgroundsin statistics. Some had only taken a basic course in statistical inference, while others had taken a broad spectrum of M. S. - and Ph. D. -level statistics courses. On the other hand, all of them had already taken required core courses in investment theory and derivative pricing, and STATS 240 was supposed to link the theory and pricing formulas to real-world data and pricing or investment strategies. Besides students in theprogram,thecoursealso attractedmanystudentsfromother departments in the university, further increasing the heterogeneity of students, as many of them had a strong background in mathematical and statistical modeling from the mathematical, physical, and engineering sciences but no previous experience in ?nance. To address the diversity in background but common strong interest in the subject and in a potential career as a “quant” in the ?nancialindustry,thecoursematerialwascarefullychosennotonlytopresent basic statistical methods of importance to quantitative ?nance but also to summarize domain knowledge in ?nance and show how it can be combined with statistical modeling in ?nancial analysis and decision making. The course material evolved over the years, especially after the second author helped as the head TA during the years 2004 and 2005.

Financial Signal Processing And Machine Learning

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Author : Ali N. Akansu
language : en
Publisher: John Wiley & Sons
Release Date : 2016-04-20

Financial Signal Processing And Machine Learning written by Ali N. Akansu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-20 with Technology & Engineering categories.

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 the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Proceedings Of Third International Conference On Computing And Communication Networks

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Author : Giancarlo Fortino
language : en
Publisher: Springer Nature
Release Date : 2024-10-21

Proceedings Of Third International Conference On Computing And Communication Networks written by Giancarlo Fortino and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-21 with Technology & Engineering categories.

This book includes selected peer-reviewed papers presented at third International Conference on Computing and Communication Networks (ICCCN 2023), held at Manchester Metropolitan University, UK, during 17–18 November 2023. The book covers topics of network and computing technologies, artificial intelligence and machine learning, security and privacy, communication systems, cyber-physical systems, data analytics, cybersecurity for Industry 4.0, and smart and sustainable environmental systems.

Financial Data Analytics

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Author : Sinem Derindere Köseoğlu
language : en
Publisher: Springer Nature
Release Date : 2022-04-25

Financial Data Analytics written by Sinem Derindere Köseoğlu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-25 with Business & Economics categories.

​This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.

Machine Learning And Modeling Techniques In Financial Data Science

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Author : Chen, Haojun
language : en
Publisher: IGI Global
Release Date : 2025-01-22

Machine Learning And Modeling Techniques In Financial Data Science written by Chen, Haojun and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Business & Economics categories.

The integration of machine learning and modeling in finance is transforming how data is analyzed, enabling more accurate predictions, risk assessments, and strategic planning. These advanced techniques empower financial professionals to uncover hidden patterns, automate complex processes, and enhance decision-making in volatile markets. As industries increasingly rely on data-driven insights, the adoption of these tools contributes to greater efficiency, reduced uncertainty, and competitive advantage. This technological shift not only drives innovation within financial sectors but also supports broader economic stability and growth by improving forecasting and mitigating risks. Machine Learning and Modeling Techniques in Financial Data Science provides an updated review and highlights recent theoretical advances and breakthroughs in professional practices within financial data science, exploring the strategic roles of machine learning and modeling techniques across various domains in finance. It offers a comprehensive collection that brings together a wealth of knowledge and experience. Covering topics such as algorithmic trading, financial technology (FinTech), and natural language processing (NLP), this book is an excellent resource for business professionals, leaders, policymakers, researchers, academicians, and more.

Fundamental Statistical Inference

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Author : Marc S. Paolella
language : en
Publisher: John Wiley & Sons
Release Date : 2018-06-19

Fundamental Statistical Inference written by Marc S. Paolella and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-19 with Mathematics categories.

A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student's t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided. The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution. Presented in three parts—Essential Concepts in Statistics; Further Fundamental Concepts in Statistics; and Additional Topics—Fundamental Statistical Inference: A Computational Approach offers comprehensive chapters on: Introducing Point and Interval Estimation; Goodness of Fit and Hypothesis Testing; Likelihood; Numerical Optimization; Methods of Point Estimation; Q-Q Plots and Distribution Testing; Unbiased Point Estimation and Bias Reduction; Analytic Interval Estimation; Inference in a Heavy-Tailed Context; The Method of Indirect Inference; and, as an appendix, A Review of Fundamental Concepts in Probability Theory, the latter to keep the book self-contained, and giving material on some advanced subjects such as saddlepoint approximations, expected shortfall in finance, calculation with the stable Paretian distribution, and convergence theorems and proofs.