This book is an ambitious effort by three well-known and
 well-respected scholars to fill an acknowledged void in the
 literature—a text covering the burgeoning field of empirical finance.
 As the authors note in the preface, there are several excellent books
 covering financial theory at a level suitable for a Ph.D. class or as
 a reference for academics and practitioners, but there is little or
 nothing similar that covers econometric methods and applications.
 Perhaps the closest existing text is the recent addition to the Wiley
 Series in Financial and Quantitative Analysis. written by Cuthbertson
 (1996). The major difference between the books is that Cuthbertson
 focuses exclusively on asset pricing in the stock, bond, and foreign
 exchange markets, whereas Campbell, Lo, and MacKinlay (henceforth CLM)
 consider empirical applications throughout the field of finance,
 including corporate finance, derivatives markets, and market
 microstructure. The level of anticipation preceding publication
 can be partly measured by the fact that at least three reviews
 (including this one) have appeared since the book arrived. Moreover,
 in their reviews, both Harvey (1998) and Tiso (1998) comment on the
 need for such a text, a sentiment that has been echoed by numerous
 finance academics.

/pdf/the-econometrics-of-financial-markets-gsz6uyl6wd.pdf

The econometrics of financial markets

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Investment in research and development (R&D) affects a country's total factor productivity. Recently new theories of economic growth have emphasized this link and have also identified a number of channels through which a country's R&D affects total factor productivity of its trade partners. Following these theoretical developments we estimate the effects of a country's R&D capital stock and the R&D capital stocks of its trade partners on the country's total factor productivity. We find large effects of both domestic and foreign R&D capital stocks on total factor productivity. The foreign R&D capital stocks have particularly large effects on the smaller countries in our sample (that consists of 22 countries). Moreover, we find that about one-quarter of the worldwide benefits of investment in R&D in the seven largest economies are appropriated by their trade partners.

International R&D Spillovers

https://www.nber.org/system/files/working_papers/w4444/w4444.pdf

International R&D spillovers

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

Review of the general linear model maximum likelihood estimation time series modelling dynamic modelling non-stationary and co-integration rational expectations state space models and the Kalman filter large non-linear models.

https://external.dandelon.com/download/attachments/dandelon/ids/DE0069631C63E27AB5F15C1257A3600444CC5.pdf

Applied econometric techniques

This new edition of the hugely successful Quantitative Financial Economics has been revised and updated to reflect the most recent theoretical and econometric/empirical advances in the financial markets. It provides an introduction to models of economic behaviour in financial markets, focusing on discrete time series analysis. Emphasis is placed on theory, testing and explaining ‘real-world’ issues. 
The new edition will include: Updated charts and cases studies. New companion website allowing students to put theory into practice and to test their knowledge through questions and answers. Chapters on Monte Carlo simulation, bootstrapping and market microstructure.

Quantitative Financial Economics: Stocks, Bonds and Foreign Exchange

http://web.ist.utl.pt/adriano.simoes/tese/referencias/Papers%20-%20Pedro/UK%20mutual%20fund%20performance%20Skill%20or%20luck.pdf

UK mutual fund performance: Skill or luck?

Using a comprehensive data set on (surviving and non-surviving) UK mutual funds (April 1975 - December 2002), we use a bootstrap methodology to distinguish between 'skill' and 'luck' for individual funds. This methodology allows for non-normality in the idiosyncratic risks of the funds - a major issue when considering those funds which appear to be either very good performers or very bad performers, since these are the funds which investors are primarily interested in identifying. Our study points to the existence of genuine stock picking ability among a small number of top performing UK equity mutual funds (i.e. performance which is not solely due to good luck). At the negative end of the performance scale, our analysis strongly rejects the hypothesis that poor performing funds are merely unlucky. These funds demonstrate 'bad skill'. Recursive estimation and Kalman 'smoothed' coefficients indicate temporal stability in the performance alpha of winner and loser portfolios.

/pdf/mutual-fund-performance-skill-or-luck-6r6lrff5sd.pdf

Mutual Fund Performance: Skill or Luck?

Using a high-quality weekly data set, the author provides several tests of the expectations hypothesis using the vector autoregression and cointegration methodologies, for several maturities between one-week and twelve-months, for the U.K. interbank market. On the basis of the Johansen cointegration analysis, there appears to be a 'break' in the term structure when both the six-month and twelve-month maturities are included as a pair. The latter may be due to either the presence of liquidity constraints or market segmentation or a time varying term premium, all of which would invalidate the assumptions underlying the expectations hypothesis. The author provides some tentative explanations of these diverse results. Copyright 1996 by Royal Economic Society.

Keith Cuthbertson

Papers

Applied econometric techniques

Quantitative Financial Economics: Stocks, Bonds and Foreign Exchange

UK mutual fund performance: Skill or luck?

Mutual Fund Performance: Skill or Luck?

The Expectations Hypothesis of the Term Structure: The UK Interbank Market