Handbook of Econometrics

Handbook of Econometrics

Author:

Publisher: Elsevier

Published: 2020-11-25

Total Pages: 594

ISBN-13: 0444636544

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Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and "hot" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on models of human choice behavior and social interactions, econometricians have created new subfields now sufficiently mature to require sophisticated literature summaries. - Presents a broader and more comprehensive view of this expanding field than any other handbook - Emphasizes the connection between econometrics and economics - Highlights current topics for which no good summaries exist


Machine Learning for Factor Investing

Machine Learning for Factor Investing

Author: Guillaume Coqueret

Publisher: CRC Press

Published: 2023-08-08

Total Pages: 498

ISBN-13: 1000912825

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Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.


Risk and Asset Allocation

Risk and Asset Allocation

Author: Attilio Meucci

Publisher: Springer Science & Business Media

Published: 2009-05-22

Total Pages: 547

ISBN-13: 3642009646

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Discusses in the practical and theoretical aspects of one-period asset allocation, i.e. market Modeling, invariants estimation, portfolia evaluation, and portfolio optimization in the prexence of estimation risk The book is software based, many of the exercises simulate in Matlab the solution to practical problems and can be downloaded from the book's web-site


Empirical Asset Pricing

Empirical Asset Pricing

Author: Wayne Ferson

Publisher: MIT Press

Published: 2019-03-12

Total Pages: 497

ISBN-13: 0262039370

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An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.


Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data

Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data

Author: Norman R. Swanson

Publisher: MDPI

Published: 2021-08-31

Total Pages: 196

ISBN-13: 303650852X

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Recently, considerable attention has been placed on the development and application of tools useful for the analysis of the high-dimensional and/or high-frequency datasets that now dominate the landscape. The purpose of this Special Issue is to collect both methodological and empirical papers that develop and utilize state-of-the-art econometric techniques for the analysis of such data.


The Elements of Financial Econometrics

The Elements of Financial Econometrics

Author: Jianqing Fan

Publisher: Cambridge University Press

Published: 2017-03-23

Total Pages: 394

ISBN-13: 1107191173

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A compact, master's-level textbook on financial econometrics, focusing on methodology and including real financial data illustrations throughout. The mathematical level is purposely kept moderate, allowing the power of the quantitative methods to be understood without too much technical detail.


Quantitative Equity Portfolio Management

Quantitative Equity Portfolio Management

Author: Ludwig B. Chincarini

Publisher: McGraw Hill Professional

Published: 2010-08-18

Total Pages: 691

ISBN-13: 0071492380

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Quantitative Equity Portfolio Management brings the orderly structure of fundamental asset management to the often-chaotic world of active equity management. Straightforward and accessible, it provides you with nuts-and-bolts details for selecting and aggregating factors, building a risk model, and much more.


Bayesian Statistics 9

Bayesian Statistics 9

Author: José M. Bernardo

Publisher: Oxford University Press

Published: 2011-10-06

Total Pages: 717

ISBN-13: 0199694583

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Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.


The Oxford Handbook of Economic Forecasting

The Oxford Handbook of Economic Forecasting

Author: Michael P. Clements

Publisher: OUP USA

Published: 2011-07-08

Total Pages: 732

ISBN-13: 0195398645

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Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.


Large Dimensional Factor Analysis

Large Dimensional Factor Analysis

Author: Jushan Bai

Publisher: Now Publishers Inc

Published: 2008

Total Pages: 90

ISBN-13: 1601981449

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Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.