Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

This bestselling and thoroughly classroom-tested textbook is a complete resource for finance students. A comprehensive and illustrated discussion of the most common empirical approaches in finance prepares students for using econometrics in practice, while detailed case studies help them understand how the techniques are used in relevant financial contexts. Worked examples from the latest version of the popular statistical software EViews guide students to implement their own models and interpret results. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Building on the successful data- and problem-driven approach of previous editions, this third edition has been updated with new data, extensive examples and additional introductory material on mathematics, making the book more accessible to students encountering econometrics for the first time. A companion website, with numerous student and instructor resources, completes the learning package.

Introductory Econometrics for Finance

A complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides.

/pdf/introductory-econometrics-for-finance-5bha97pwpi.pdf

This textbook provides an introduction to econometrics through a grounding in probability theory and statistical inference. The emphasis is on the concepts and ideas underlying probability theory and statistical inference, and on motivating the learning of them both at a formal and an intuitive level. It encourages the mastering of fundamental concepts and theoretical perspectives which guide the formulation and solution of problems in econometric modelling. This makes it an ideal introduction to empirical econometric modelling and the more advanced econometric literature. It is recommended for use on courses giving students a thorough grounding in econometrics at undergraduate or graduate level.

Statistical Foundations of Econometric Modelling

This paper constructs a new data set of repeated sales of artworks and estimates an annual index of art prices for the period 1875-2000. Contrary to earlier studies, we find art outperforms fixed income securities as an investment, though it significantly under-performs stocks in the US. Art is also found to have lower volatility and lower correlation with other assets, making it more attractive for portfolio diversification than discovered in earlier research. There is strong evidence of underperformance of masterpieces, meaning expensive paintings tend to underperform the art market index. The evidence is mixed on whether the "law of one price" holds in the New York auction market.

/pdf/art-as-an-investment-and-the-underperformance-of-1e3xv0eil1.pdf

Art as an Investment and the Underperformance of Masterpieces

The valuation of Canadian paintings is analysed empirically. Using a sample of auction prices for major Canadian painters for the period 1968-2001, we run hedonic regressions to analyse the influence of various factors, including painter identity, on auction prices, as well as to construct a market price index. This index is used in a second-stage analysis in which we analyse the properties of Canadian art viewed as an investment asset. We apply standard asset pricing theory, as incorporated in the capital asset pricing model (CAPM), to the analysis of price movements in the market for Canadian paintings.

https://papers.ssrn.com/sol3/Delivery.cfm/caje_241.pdf?abstractid=563587&mirid=4

Asset Pricing Theory and the Valuation of Canadian Paintings

The valuation of Canadian paintings is analyzed empirically. Using a sample of auction prices for major Canadian painters for the period 19682001, we run hedonic regressions to analyze the in‡uence of various factors, including painter identity, on auction prices, as well as to construct a market price index. This index is used in a second stage analysis in which we analyze the properties of Canadian art viewed as an investment asset. We consider the extent to which standard asset pricing theory, as incorporated in the capital asset pricingmodel (CAPM), can account for price movements in the market for Canadian paintings.

Asset pricing theory and the valuation of Canadian paintings

We develop new tests of the capital asset pricing model that take account of and are valid under the assumption that the distribution generating returns is elliptically symmetric; this assumption is necessary and sufficient for the validity of the CAPM. Our test is based on semiparametric efficient estimation procedures for a seemingly unrelated regression model where the multivariate error density is elliptically symmetric, but otherwise unrestricted. The elliptical symmetry assumption allows us to avoid the curse of dimensionality problem that typically arises in multivariate semiparametric estimation procedures, because the multivariate elliptically symmetric density function can be written as a function of a scalar transformation of the observed multivariate data. The elliptically symmetric family includes a number of thick-tailed distributions and so is potentially relevant in financial applications. Our estimated betas are lower than the OLS estimates, and our parameter estimates are much less consistent with the CAPM restrictions than the corresponding OLS estimates. Copyright © 2002 John Wiley & Sons, Ltd.

/pdf/testing-the-capital-asset-pricing-model-efficiently-under-4t4sdiz92d.pdf

Testing the capital asset pricing model efficiently under elliptical symmetry: a semiparametric approach

We develop new tests of the capital asset pricing model which are optimal under the assumption that the distribution generating returns is elliptically symmetric; this assumption is necessary and sufficient for the validity of the CAPM. Our test is based on semiparametric efficient estimation procedures for a seemingly unrelated regression model where the multivariate error density is elliptically symmetric, but otherwise unrestricted. The elliptical symmetry assumption allows us to avoid the curse of dimensionality problem that typically arises in multivariate semiparametric estimation procedures, because the multivariate elliptically symmetric density function can be written as a function of a scalar transformation of the observed multivariate data. The elliptically symmetric family includes a number of thick-tailed distributions and so is potentially relevant in financial applications. Our estimated betas are lower than the OLS estimates, and our parameter estimates are much less consistent with the CAPM restrictions than the corresponding OLS estimates.

/pdf/testing-the-capital-asset-pricing-model-efficiently-under-2jfveism5m.pdf

Douglas J. Hodgson

Papers

Asset Pricing Theory and the Valuation of Canadian Paintings

Asset pricing theory and the valuation of Canadian paintings

Testing the capital asset pricing model efficiently under elliptical symmetry: a semiparametric approach

Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach

Adaptive estimation of cointegrating regressions with ARMA errors