Introductory Econometrics for Finance

Introductory Econometrics for Finance

Chris Brooks

Language: English

Pages: 740

ISBN: 1107661455

Format: PDF / Kindle (mobi) / ePub

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.

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formula αˆ and β, 34 Introductory Econometrics for Finance Table 2.1 Sample data on fund XXX to motivate OLS estimation Year, t Excess return on fund XXX = r X X X,t − r f t Excess return on market index = rmt − r f t 1 2 3 4 5 17.8 39.0 12.8 24.2 17.2 13.7 23.2 6.9 16.8 12.3 to use to calculate the slope estimate, but the formula can also be written, more intuitively, as βˆ = (xt − x¯ )(yt − y¯ ) (xt − x¯ )2 (2.6) which is equivalent to the sample covariance between x and y divided

conclusion? 6. You estimate a regression of the form given by (3.52) below in order to evaluate the effect of various firm-specific factors on the returns of a sample of firms. You run a cross-sectional regression with 200 firms ri = β0 + β1 Si + β2 MBi + β3 PEi + β4 BETAi + u i (3.52) where: ri is the percentage annual return for the stock Si is the size of firm i measured in terms of sales revenue MBi is the market to book ratio of the firm PEi is the price/earnings (P/E) ratio of the firm

value of u, its previous values, uˆ t−1 , uˆ t−2 , . . . The first step is to consider possible Classical linear regression model assumptions and diagnostic tests Figure 4.3 Plot of uˆ t against uˆ t−1 , showing positive autocorrelation ût 141 + + – û t–1 – relationships between the current residual and the immediately previous one, uˆ t−1 , via a graphical exploration. Thus uˆ t is plotted against uˆ t−1 , and uˆ t is plotted over time. Some stereotypical patterns that may be found in

a problem! This view, associated with Sargan, Hendry and Mizon, suggests that serial correlation in the errors arises as a consequence of ‘misspecified dynamics’. For another explanation of the reason why this stance is taken, recall that it is possible to express the dependent variable as the sum of the parts that can be explained using the model, and a part which cannot (the residuals) yt = yˆ t + uˆ t (4.28) where yˆ t are the fitted values from the model (= βˆ 1 + βˆ 2 x2t + βˆ 3 x3t + · · ·

line. However, this assumption may not always be upheld. Whether the model should be linear can be formally tested using Ramsey’s (1969) RESET test, which is a general test for misspecification of functional

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