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858245 |
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Course |
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| Empirical Finance | ||
Faculty |
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| Bernt Arne Odegaard, Professor of Finance, University of Stavanger and Visiting Professor, Copenhagen Business School | ||
Course Coordinator |
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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. |
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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. 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. The first challenge for students in this course is to install a couple of programs on their computer. The first is R, a public domain package for doing statistics, available at www.r-project.org. The second is a matrix handler. Here there are a number of alternatives available. Matlab is the alternative for people with no money troubles, it is a commercial program used in the industry. At least two public domain alternatives to Matlab can be used. One is octave, available at www.octave.org. Another is scilab, available at www.scilab.org. Octave will be used for the examples shown, but if you are on a Windows machine scilab may be easier to install. Try to install 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. To make the course even more relevant most of these datasets are concerned with Norwegian financial markets. These datasets will both be used in examples in class that you should try to replicate, and in the exercises you should turn in. |
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Course content |
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First lecture: Toolchest • Introduction. • Data gathering. • Introducing the computing tools. • Linear Algebra. • Using a matrix program for Linear Algebra. • Some financial applications of matrix algebra. • The geometric interpretation of least squares. • R - the statistics tool • The value of a good picture. • Case: Skjeltorp and Ødegaard (2015) Second lecture: Basic econometric analysis, finance applications. • Linear Regressions • Financial application: Performance of equity portfolios. • Alpha estimation of portfolios: Ødegaard (2009b) • Dummies in Regressions. • Case: Oslo Stock Exchange and the Weather • Maximum Likelihood • Maximum Likelihood estimation • Discrete Choice as an alternative to regressions. • Example of discrete choice: Skjeltorp and Ødegaard (2015) 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, introduce the Fama French factors, and end up with the current “state of the art” in crossectional modelling. • 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. • The Fama and French controversy, two specific “factors” • Current Status of crossectional estimation. • Example Case: Næs, Skjeltorp, and Ødegaard (2008): The Norwegian Crossection. 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) • Case: Næs et al. (2008) (Norway) Fifth lecture: Event studies and Time Series (I) • Event Studies in Economics and Finance MacKinlay (1997). • Time series • What is special about time series? • Univariate time series modelling • ADL Sixth lecture: Time Series (II) • VAR • Cointegration • Volatility modelling – ARCH Seventh lecture: Predictability and High Frequency Data • Predictability • Trading strategies • Useful tool: Factor-mimicking portfolios. • Forecasting • Application: Forecasting the real economy with financial variables. Næs, Skjeltorp, and Ødegaard (2011) • Returning to the equity risk premium: Can we forecast it? • High frequency financial data • What is special about it? • Realized Volatility etc Eight lecture. Liquidity, Diff in Diff and Data Snooping. • Liquidity • Measuring liquidity using daily data. LOT, Roll, Amihud, etc. • Application: Estimation of trading costs. (Ødegaard, 2009a). • Measuring liquidity using high-frequency data. • Modelling the Interaction of stock prices and volume Hasbrouck (1991) (application of VAR) • Liquidity and asset pricing • Being clever with the data. • Diff in Diff - dealing with endogeneity problems • Example of diff in diff: Jørgensen, Skjeltorp, and Ødegaard (2016). • Asking existential questions • Data Snooping Biases. McLean and Pontiff (2013). |
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Teaching style |
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| Lectures with exercises | ||
Lecture plan |
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| The lecture plan of the course encompasses 4x2 days of app. 4 teaching hours per day | ||
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 analyze, and write up your analysis. You need to do the exercises as you would write the results in an academic paper: Tables summarizing 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 matlab/R code and output. |
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Other |
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Dates: 10-11 November 2016 1 - 2 December 2016 5 - 6 January 2017 26 - 27 January 2017 |
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Start date |
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| 10/11/2016 | ||
End date |
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| 27/01/2017 | ||
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).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.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. I have not asked the bookstore to stock them, as they are optional. | ||
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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| 10 | ||
Location |
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Location: Solbjerg Plads 3, room D4.39 10-11 November 2016 1 - 2 December 2016 5 - 6 January 2017 26 - 27 January 2017 |
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Contact information |
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Bente S. Ramovic bsr.research@cbs.dk Tel.: +45 3815 3138 |
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Registration deadline |
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| 20/10/2016 | ||
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The course is offered through The Nordic Finance Network, and the department of Finance at CBS will cover the course fee for PhD students from other NFN associated universities. Please note that the registration is binding after the registration deadline. |
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