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Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497
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Book Description
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.
Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497
Get Book Here
Book Description
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.
Author: John Y. Campbell
Publisher: University of Chicago Press
ISBN: 0226092127
Category : Business & Economics
Languages : en
Pages : 444
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Book Description
Economic growth, low inflation, and financial stability are among the most important goals of policy makers, and central banks such as the Federal Reserve are key institutions for achieving these goals. In Asset Prices and Monetary Policy, leading scholars and practitioners probe the interaction of central banks, asset markets, and the general economy to forge a new understanding of the challenges facing policy makers as they manage an increasingly complex economic system. The contributors examine how central bankers determine their policy prescriptions with reference to the fluctuating housing market, the balance of debt and credit, changing beliefs of investors, the level of commodity prices, and other factors. At a time when the public has never been more involved in stocks, retirement funds, and real estate investment, this insightful book will be useful to all those concerned with the current state of the economy.
Author: Álvaro Cartea
Publisher: Cambridge University Press
ISBN: 1316453650
Category : Mathematics
Languages : en
Pages : 360
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Book Description
The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms, then this is the book for you.
Author: Laurent E. Calvet
Publisher: Academic Press
ISBN: 0080559964
Category : Business & Economics
Languages : en
Pages : 273
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Book Description
Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on insights from the use of multifractals in the natural sciences and mathematics and provides a unified treatment of the use of multifractal techniques in finance. A large existing literature (e.g., Engle, 1982; Rossi, 1995) models volatility as an average of past shocks, possibly with a noise component. This approach often has difficulty capturing sharp discontinuities and large changes in financial volatility. Their research has shown the advantages of modelling volatility as subject to abrupt regime changes of heterogeneous durations. Using the intuition that some economic phenomena are long-lasting while others are more transient, they permit regimes to have varying degrees of persistence. By drawing on insights from the use of multifractals in the natural sciences and mathematics, they show how to construct high-dimensional regime-switching models that are easy to estimate, and substantially outperform some of the best traditional forecasting models such as GARCH. The goal of Multifractal Volatility is to popularize the approach by presenting these exciting new developments to a wider audience. They emphasize both theoretical and empirical applications, beginning with a style that is easily accessible and intuitive in early chapters, and extending to the most rigorous continuous-time and equilibrium pricing formulations in final chapters. - Presents a powerful new technique for forecasting volatility - Leads the reader intuitively from existing volatility techniques to the frontier of research in this field by top scholars at major universities - The first comprehensive book on multifractal techniques in finance, a cutting-edge field of research
Author: Stefan Nagel
Publisher: Princeton University Press
ISBN: 0691218706
Category : Business & Economics
Languages : en
Pages : 156
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Book Description
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
Author: M. Ali Khan
Publisher: Springer Science & Business Media
ISBN: 3662070715
Category : Business & Economics
Languages : en
Pages : 441
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Book Description
Apart from the underlying theme that all the contributions to this volume pertain to models set in an infinite dimensional space, they differ on many counts. Some were written in the early seventies while others are reports of ongoing research done especially with this volume in mind. Some are surveys of material that can, at least at this point in time, be deemed to have attained a satisfactory solution of the problem, while oth ers represent initial forays into an original and novel formulation. Some furnish alternative proofs of known, and by now, classical results, while others can be seen as groping towards and exploring formulations that have not yet reached a definitive form. The subject matter also has a wide leeway, ranging from solution concepts for economies to those for games and also including representation of preferences and discussion of purely mathematical problems, all within the rubric of choice variables belonging to an infinite dimensional space, interpreted as a commodity space or as a strategy space. Thus, this is a collective enterprise in a fairly wide sense of the term and one with the diversity of which we have interfered as little as possible. Our motivation for bringing all of this work under one set of covers was severalfold.
Author:
Publisher: Elsevier
ISBN: 0444633898
Category : Business & Economics
Languages : en
Pages : 749
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Book Description
Handbook of Behavioral Economics: Foundations and Applications presents the concepts and tools of behavioral economics. Its authors are all economists who share a belief that the objective of behavioral economics is to enrich, rather than to destroy or replace, standard economics. They provide authoritative perspectives on the value to economic inquiry of insights gained from psychology. Specific chapters in this first volume cover reference-dependent preferences, asset markets, household finance, corporate finance, public economics, industrial organization, and structural behavioural economics. This Handbook provides authoritative summaries by experts in respective subfields regarding where behavioral economics has been; what it has so far accomplished; and its promise for the future. This taking-stock is just what Behavioral Economics needs at this stage of its so-far successful career. - Helps academic and non-academic economists understand recent, rapid changes in theoretical and empirical advances within behavioral economics - Designed for economists already convinced of the benefits of behavioral economics and mainstream economists who feel threatened by new developments in behavioral economics - Written for those who wish to become quickly acquainted with behavioral economics
Author: Stephen Satchell
Publisher: Springer
ISBN: 1137554177
Category : Science
Languages : en
Pages : 416
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Book Description
Over the last 20 years hedge funds and derivatives have fluctuated in reputational terms; they have been blamed for the global financial crisis and been praised for the provision of liquidity in troubled times. Both topics are rather under-researched due to a combination of data and secrecy issues. This book is a collection of papers celebrating 20 years of the Journal of Derivatives and Hedge Funds (JDHF). The 18 papers included in this volume represent a small sample of influential papers included during the life of the Journal, representing industry-orientated research in these areas. With a Preface from co-editor of the journal Stephen Satchell, the first part of the collection focuses on hedge funds and the second on markets, prices and products.
Author: Henry Schellhorn
Publisher: SIAM
ISBN: 1611977908
Category : Computers
Languages : en
Pages : 267
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Book Description
This textbook covers the latest advances in machine learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the (usually confidential) techniques used by asset managers result in better investments than the more standard techniques. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative. Coverage includes an original machine learning method for strategic asset allocation; the no-arbitrage theory applied to a wide portfolio of assets as well as other asset management methods, such as mean-variance, Bayesian methods, linear factor models, and strategic asset allocation; recent techniques such as neural networks and reinforcement learning, and more classical ones, including nonlinear and linear programming, principal component analysis, dynamic programming, and clustering. The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. The book is easy to read yet rigorous and contains a large number of exercises. Machine Learning for Asset Management and Pricing is intended for graduate students and researchers in finance, economics, financial engineering, and data science focusing on asset pricing and management. It will also be of interest to finance professionals and analysts interested in applying machine learning to investment strategies and asset management. This textbook is appropriate for courses on asset management, optimization with applications, portfolio theory, and asset pricing.
Author: Michael Woodford
Publisher: Elsevier
ISBN: 9780444501561
Category :
Languages : en
Pages : 822
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Book Description
