Data Science For Financial Econometrics

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Data Science For Financial Econometrics


Data Science For Financial Econometrics
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Data Science For Financial Econometrics


Data Science For Financial Econometrics
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File Size : 47,7 Mb
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Author : Nguyen Ngoc Thach
language : en
Publisher: Springer
Release Date : 2020-11-14



Data Science For Financial Econometrics written by Nguyen Ngoc Thach and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-14 with Computers categories.


This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.

Data Science For Financial Econometrics


Data Science For Financial Econometrics
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File Size : 45,6 Mb
Total Download : 424

Author : Nguyen Ngoc Thach
language : en
Publisher: Springer Nature
Release Date :



Data Science For Financial Econometrics written by Nguyen Ngoc Thach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.




Big Data Science In Finance


Big Data Science In Finance
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File Size : 49,7 Mb
Total Download : 948

Author : Irene Aldridge
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-27



Big Data Science In Finance written by Irene Aldridge 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 2021-01-27 with Computers categories.


Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.

Financial Econometrics


Financial Econometrics
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File Size : 50,8 Mb
Total Download : 958

Author : Oliver Linton
language : en
Publisher: Cambridge University Press
Release Date : 2019-01-31



Financial Econometrics written by Oliver Linton and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-31 with Business & Economics categories.


Presents an up-to-date treatment of the models and methodologies of financial econometrics by one of the world's leading financial econometricians.

Big Data Science In Finance


Big Data Science In Finance
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File Size : 47,9 Mb
Total Download : 994

Author : Irene Aldridge
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-08



Big Data Science In Finance written by Irene Aldridge 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 2021-01-08 with Computers categories.


Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.

Handbook Of Research On Emerging Theories Models And Applications Of Financial Econometrics


Handbook Of Research On Emerging Theories Models And Applications Of Financial Econometrics
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File Size : 40,7 Mb
Total Download : 237

Author : Burcu Adıgüzel Mercangöz
language : en
Publisher: Springer Nature
Release Date : 2021-03-21



Handbook Of Research On Emerging Theories Models And Applications Of Financial Econometrics written by Burcu Adıgüzel Mercangöz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-21 with Business & Economics categories.


This handbook presents emerging research exploring the theoretical and practical aspects of econometric techniques for the financial sector and their applications in economics. By doing so, it offers invaluable tools for predicting and weighing the risks of multiple investments by incorporating data analysis. Throughout the book the authors address a broad range of topics such as predictive analysis, monetary policy, economic growth, systemic risk and investment behavior. This book is a must-read for researchers, scholars and practitioners in the field of economics who are interested in a better understanding of current research on the application of econometric methods to financial sector data.

Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes


Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes
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File Size : 43,7 Mb
Total Download : 763

Author : Cheng-few Lee
language : en
Publisher: World Scientific
Release Date : 2020-07-30



Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng-few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Business & Economics categories.


This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.