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918762 |
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Course |
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| Empirical Finance - Fall 2017 | ||
Faculty |
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| Peter Feldhütter, Professor of Finance, Copenhagen Business School | ||
Course Coordinator |
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| Peter Feldhütter, Professor of Finance, Copenhagen Business School | ||
Prerequisites |
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Knowledge of asset pricing, corporate finance and econometrics at a M.Sc. level is expected. Otherwise, the course is designed as a first Ph.D. course in empirical finance. The course is open for other participants with an adequate background. Computer Tools In the analysis of data we will use the typical computer tools for doing such analysis. For any nontrivial empirical analysis we have to use other tools than Excel and similar spreadsheets. Any empirical researcher has to be familiar with a range of computer tools, and choose the right tool for a given estimation problem. We will use Matlab in this course. Matlab is a commercial program used in the industry and by many academics. There are a number of alternatives to Matlab that are free. One is octave, available at www.octave.org. Another is scilab, available at www.scilab.org. Finally there is R, a public domain package for doing statistics, available at www.r-project.org. Try to install one of these programs before the first lecture. Datasets In the course we will be looking at various examples. A number of datasets used in these examples will be put on the course homepage. These datasets will both be used in examples in class that you should try to replicate, and in the exercises you should turn in. Læringsmål/Learning Objectives: Students should • obtain a deep understanding of the various estimation methods discussed in the course such that they are able to understand studies using these methods • demonstrate capability to apply these methods in their research projects, including the organisation of a data set from the various databases available such that it is suitable for empirical testing • Be able to write up and present results of empirical investigations in the form expected in research papers. |
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Aim |
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| This course is a first course in empirical finance at the PhD level. The course attempts to lay the groundwork for students who will later do actual empirical research work. It is therefore a hands on course where the students will have to perform analysis on actual data, and where the examples are chosen to illustrate the typical questions asked in finance research. | ||
Course content |
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The lecture plan of the course encompasses 8 days of app. 4 teaching hours per day, scheduled for: The following provides an overview of the course. Some of the content may change depending on the interest of students, but the overview gives a good guidance of what to expect of the course. First lecture: Tool chest • Introduction. • Data gathering. • Introducing the computing tools. • Linear Algebra. • Using a matrix program for Linear Algebra. • The value of a good picture. Second lecture: Basic econometric analysis, finance applications. • Linear Regressions • Dummies in Regressions. • Maximum Likelihood • Maximum Likelihood estimation Third lecture: Cross-sectional Asset Pricing This lecture uses the problem of explaining the cross-section of asset returns, i.e. the relation between risk and return, what determines the riskiness of a single asset, as the unifying theme. We start with the classical tests of CAPM, show methodological innovations ending up in GMM estimation in the cross section, and introduce the Fama French factors. • Violations of OLS Assumptions - GLS, HAC corrections • CAPM testing (classical): Black et al. (1972), Fama and MacBeth (1973) • The APT, looking for additional explanatory “factors.” Factor Analysis. Principal Components. • Introduction to GMM. Chaussé (2010), Jagannathan, Skoulakis, and Wang (2002) • CAPM tests in a GMM setting: MacKinlay and Richardson (1991) • Gibbons-Ross-Shanken statistics. Fourth lecture: Estimating The Equity Premium This lecture uses estimation of, and understanding, the equity market premium, as the unifying theme. We start by looking at estimation of the equity market premium, before going to representative agent modelling, asking whether the estimated equity market premium can be explained (the equity premium puzzle). We then move to the more general framework of stochastic discount factor representations, and look at nonparametric investigations, such as second moment bounds. • Introduction: Estimating the Expected Market Risk Premium. • Representative agent estimations: Hansen and Singleton (1983) • Stochastic Discount factor representations of asset prices and their estimation. • Second moment bounds: Hansen and Jagannathan (1991) Fifth lecture: Event studies and Time Series (I) • Event Studies in Economics and Finance MacKinlay (1997). • Time series • Univariate time series modelling Sixth lecture: Time Series (II) • VAR • Cointegration • Volatility modelling – ARCH Seventh lecture: Predictability and High Frequency Data • Predictability • Trading strategies • Factor-mimicking portfolios. • Returning to the equity risk premium: Can we forecast it? • High frequency financial data • Realized Volatility etc Eight lecture. Liquidity. • Liquidity • Measuring liquidity using daily data. Roll, Amihud, Roundtrip costs etc • Measuring liquidity using high-frequency data • Liquidity and asset pricing |
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Teaching style |
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| Lectures with exercises | ||
Lecture plan |
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Learning objectives |
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Exam |
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Course evaluation will be based on student hand-ins to empirical problems. In the problems you are typically given a dataset which you need to analyse, and write up your analysis. You need to do the exercises as you would write the results in an academic paper: Tables summarising results, detailed descriptions of what is estimated in the table, and a text discussion of what the results mean. In an appendix you should provide the exact estimation in the form of code and output. |
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Other |
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Joseph Adler. R in a nutshell. O’Reilly, 2010. Euad Aleskerov, Ersel Hasan, and Dmitri Piontkovski. Linear Algebra for Economists. Springer, 2011. Fisher Black, Michael Jensen, and Myron Scholes. The capital asset pricing model, some empirical tests. In Michael C Jensen, editor, Studies in the theory of capital markets. Preager, 1972. John Y Campbell, Andrew W Lo, and A Craig MacKinlay. The econometrics of financial markets. Princeton University Press, 1997. Pierre Chaussé. Computing generalized method of moments and generalized empirical likelihood with r. Journal of Statistical Software, 34(11):1–35, 2010. John Cochrane. Asset Pricing. Princeton University Press, revised edition, 2005. Eugene F Fama and J MacBeth. Risk, return and equilibrium, empirical tests. Journal of Political Economy, 81:607–636, 1973. William H Greene. Econometric Analysis. Pearson, seventh edition, 2012. Jesper Schmidt Hansen. GNU Octave, Beginner’s Guide. PACKT Publishing, 2011. Lars Peter Hansen and Ravi Jagannathan. Implications of security market data for models of dynamic economies. Journal of Political Economy, 99(2):225–62, 1991. Lars Peter Hansen and Kenneth Singleton. Stochastic consumption, risk aversion and the temporal behavior of stock returns. Journal of Political Economy, 91:249–265, April 1983. Joel Hasbrouck. Measuring the information content of stock trades. Journal of Finance, 46(1): 179–207, March 1991. Ravi Jagannathan, Georgios Skoulakis, and Zhenyu Wang. Generalized method of moments: Applications in finance. Journal of Business & Economic Statistics, 20(4), October 2002. A Craig MacKinlay. Event studies in economics and finance. Journal of Economic Literature, XXXV: 13–39, March 1997. A Craig MacKinlay and Matthew P Richardson. Using generalized method of moments to test mean-variance efficiency. Journal of Finance, 46:511–27, 1991. James H Stock and Mark W Watson. Introduction to Econometrics. Addison Wesley, 2003. Ruey S Tsay. An Introduction to Analysis of Financial Data with R. Wiley, Hoboken, New Jersey, 2013. |
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Start date |
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| 07/09/2017 | ||
End date |
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| 02/11/2017 | ||
Level |
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| PhD | ||
ECTS |
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| 7.5 | ||
Language |
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| English | ||
Course Literature |
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| See the literature list below. Students are expected to have read the assigned literature before the lectures.There is no single textbook for this course. Most finance PhD students will want to have a copy of Cochrane (2005) on their shelf, so I recommend that you get that book. I will refer to that at times. Another, although now slightly dated book, is Campbell, Lo, and MacKinlay (1997), which contains a lot of useful information. A textbook useful for the time series part of the course is Tsay (2013). This is on the recommended list. I will in addition refer to a number of journal articles. Detailed lecture notes and slides will be put on the course homepage. The lecture notes will be relatively self-contained and complete. In this course we will refer to various econometric, mathematical and computing topics that may or may not be known to you from before. If these are completely new areas for you, you may want some guidance and references. I’m listing a reference or two to various textbook sources I find useful. • Linear Algebra. An easily accessible text is Aleskerov, Hasan, and Piontkovski (2011) (on the web for institutions with Springerlink). • Econometrics. One standard text for PhD level studies is Greene (2012), which contains most of what one need to check up on, but it not an introductory text. If you have had no econometrics before, I view Stock and Watson (2003) as a good introduction for finance students. • Computing. Most of the computer tools we work with have manuals online. But sometime it is helpful with more of a gentle overview and introduction to the tools. Let me mention a couple of possibilities for the various tools. • R: Adler (2010) is a relatively cheap overview of the usage of R. • Octave: Hansen (2011) is a similar book for Octave.These books are available from amazon and similar online retailers | ||
Fee |
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| 9,750 DKK | ||
Minimum number of participants |
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| 10 | ||
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Maximum number of participants |
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| 20 | ||
Location |
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Thursdays from 9-12:30 Weeks 36, 37, 38 Kilevej 14A - room K3.41 Weeks 39, 40, 41, 43 Kilevej 14A - room K4.74 Week 44 TBA |
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Contact information |
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PhD Support Bente S. Ramovic bsr.research@cbs.dk Tel: +45 3815 3138 |
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Registration deadline |
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| 15/08/2017 | ||
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The course will be offered through The Nordic Finance Network. Participants from other NFN associated universities are allowed to participate for free (please note this when you register) This course may be followed by a limited number of students from the Master’s in Advanced economics and Finance (Cand.Oecon.). To sign up send a one-page motivational letter and a grade transcript to ily.stu@cbs.dk no later than Monday 10 July 2017. Please note the registration is binding after the registration deadline |
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