Author: Cheng Few Lee
Publisher: World Scientific
ISBN: 9811202400
Category : Business & Economics
Languages : en
Pages : 5053
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Book Description
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.
Author:
Publisher:
ISBN: 9789811202445
Category :
Languages : en
Pages : 1365
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Book Description
"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 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"-- Provided by publisher.
Author: Cheng Few Lee
Publisher:
ISBN: 9789819809943
Category :
Languages : en
Pages : 0
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Book Description
This handbook (in 4 volumes) investigates important tools for empirical and theoretical research in finance and accounting. Based on editors' and contributors' years of experience working in the industry, teaching classes, conducting research, writing textbooks, and editing journals on the subject of financial econometrics, mathematics, statistics, and technology, this handbook will review, discuss, and integrate theoretical, methodological, and practical issues of financial econometrics, mathematics, statistics, and machine learning.Volume 1 lays the groundwork with key methodologies and innovative approaches. From financial econometrics to the application of machine learning in risk management, this volume covers critical topics such as optimal futures hedging and the impacts of CEO compensation on corporate innovation. It also delves into advanced techniques in option bound determination, the influence of economic institutions on banking stability, and the latest in mortgage loan pricing predictions using ML-RNN, along with systemic risk assessment using bivariate copulas.Volume 2 explores sophisticated financial theories and machine learning applications. Readers will encounter stochastic volatility models and the complexities of implied variance in option pricing, along with in-depth discussions on real and exotic options and the diversification benefits of U.S. international equity funds. This volume also highlights groundbreaking applications of machine learning for stock selection and credit risk assessment, significantly enhancing decision-making processes in the finance sector.Volume 3 addresses critical issues in corporate finance and risk analysis, with a strong focus on practical implications. It covers the role of international transfer pricing, corporate reorganization, and executive share option plans. Additionally, it presents empirical studies on mutual fund performance and market model forecasting. This volume introduces innovative approaches in hedging, capital budgeting, and nonlinear models in corporate finance research, providing valuable insights for professionals and academics alike.Volume 4 explores the integration of big data and advanced econometrics in finance. It examines the impact of lead independent directors on earnings management and the dynamic relationship between stock prices and exchange rates. Readers will find cutting-edge techniques in survival analysis, deep neural networks for credit risk, and volatility spillovers during market crises.Written in a comprehensive manner, the four volumes discuss how to use higher moment theory to analyze investment analysis and portfolio management. In addition, they also discuss risk management theory and its application.
Author: Cheng-Few Lee
Publisher: Springer
ISBN: 9781461477495
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
The Handbook of Financial Econometrics and Statistics provides, in four volumes and over 100 chapters, a comprehensive overview of the primary methodologies in econometrics and statistics as applied to financial research. Including overviews of key concepts by the editors and in-depth contributions from leading scholars around the world, the Handbook is the definitive resource for both classic and cutting-edge theories, policies, and analytical techniques in the field. Volume 1 (Parts I and II) covers all of the essential theoretical and empirical approaches. Volumes 2, 3, and 4 feature contributed entries that showcase the application of financial econometrics and statistics to such topics as asset pricing, investment and portfolio research, option pricing, mutual funds, and financial accounting research. Throughout, the Handbook offers illustrative case examples and applications, worked equations, and extensive references, and includes both subject and author indices.
Author: Cheng-Few Lee
Publisher: Springer
ISBN: 1493994298
Category : Business & Economics
Languages : en
Pages : 655
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Book Description
This rigorous textbook introduces graduate students to the principles of econometrics and statistics with a focus on methods and applications in financial research. Financial Econometrics, Mathematics, and Statistics introduces tools and methods important for both finance and accounting that assist with asset pricing, corporate finance, options and futures, and conducting financial accounting research. Divided into four parts, the text begins with topics related to regression and financial econometrics. Subsequent sections describe time-series analyses; the role of binomial, multi-nomial, and log normal distributions in option pricing models; and the application of statistics analyses to risk management. The real-world applications and problems offer students a unique insight into such topics as heteroskedasticity, regression, simultaneous equation models, panel data analysis, time series analysis, and generalized method of moments. Written by leading academics in the quantitative finance field, allows readers to implement the principles behind financial econometrics and statistics through real-world applications and problem sets. This textbook will appeal to a less-served market of upper-undergraduate and graduate students in finance, economics, and statistics.
Author: Matthew F. Dixon
Publisher: Springer Nature
ISBN: 3030410684
Category : Business & Economics
Languages : en
Pages : 565
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Book Description
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Author: Nguyen Ngoc Thach
Publisher: Springer Nature
ISBN: 3030488535
Category : Computers
Languages : en
Pages : 633
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Book Description
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.
Author: Yacine Ait-Sahalia
Publisher: Elsevier
ISBN: 0444535497
Category : Business & Economics
Languages : en
Pages : 385
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Book Description
Applied financial econometrics subjects are featured in this second volume, with papers that survey important research even as they make unique empirical contributions to the literature. These subjects are familiar: portfolio choice, trading volume, the risk-return tradeoff, option pricing, bond yields, and the management, supervision, and measurement of extreme and infrequent risks. Yet their treatments are exceptional, drawing on current data and evidence to reflect recent events and scholarship. A landmark in its coverage, this volume should propel financial econometric research for years. Presents a broad survey of current research Contributors are leading econometricians Offers a clarity of method and explanation unavailable in other financial econometrics collections
Author: Cheng-Few Lee
Publisher: Springer Science & Business Media
ISBN: 0387771174
Category : Business & Economics
Languages : en
Pages : 1700
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Book Description
Quantitative finance is a combination of economics, accounting, statistics, econometrics, mathematics, stochastic process, and computer science and technology. Increasingly, the tools of financial analysis are being applied to assess, monitor, and mitigate risk, especially in the context of globalization, market volatility, and economic crisis. This two-volume handbook, comprised of over 100 chapters, is the most comprehensive resource in the field to date, integrating the most current theory, methodology, policy, and practical applications. Showcasing contributions from an international array of experts, the Handbook of Quantitative Finance and Risk Management is unparalleled in the breadth and depth of its coverage. Volume 1 presents an overview of quantitative finance and risk management research, covering the essential theories, policies, and empirical methodologies used in the field. Chapters provide in-depth discussion of portfolio theory and investment analysis. Volume 2 covers options and option pricing theory and risk management. Volume 3 presents a wide variety of models and analytical tools. Throughout, the handbook offers illustrative case examples, worked equations, and extensive references; additional features include chapter abstracts, keywords, and author and subject indices. From "arbitrage" to "yield spreads," the Handbook of Quantitative Finance and Risk Management will serve as an essential resource for academics, educators, students, policymakers, and practitioners.
Author:
Publisher: Elsevier
ISBN: 0128202513
Category : Mathematics
Languages : en
Pages : 352
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Book Description
Financial, Macro and Micro Econometrics Using R, Volume 42, provides state-of-the-art information on important topics in econometrics, including multivariate GARCH, stochastic frontiers, fractional responses, specification testing and model selection, exogeneity testing, causal analysis and forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration, financial market jumps and co-jumps, among other topics. Presents chapters authored by distinguished, honored researchers who have received awards from the Journal of Econometrics or the Econometric Society Includes descriptions and links to resources and free open source R Gives readers what they need to jumpstart their understanding on the state-of-the-art
