1040353


Course
Empirical Finance - 2019

Faculty
Peter Feldhütter, Professor of Finance, Copenhagen Business School

Course Coordinator
Peter Feldhütter, Professor of Finance

Prerequisites
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.

Aim
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. The focus is on classic estimation methods, but the course will also, where relevant, outline recent developments.

Course content

Teaching style
Lectures with exercises

Lecture plan
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 - A5.32
• 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. - D4.20
• Linear Regressions
• Dummies in Regressions.
• Maximum Likelihood
• Maximum Likelihood estimation

Third and fourth lecture: Cross-sectional Asset Pricing - (3) D4.39 and (4) D4.20
• Violations of OLS Assumptions - HAC corrections: White (1980), Newey and West(1987)
• The expectation hypothesis: Campbell and Shiller (1991), Cochrane and Piazzesi (2005)
• CAPM testing (classical): Black, Jensen, and Scholes (1972), Fama and MacBeth (1973)
• The APT, looking for additional explanatory “factors.” Factor Analysis. Principal Components.
• Introduction to GMM.

Fifth lecture: Event studies - D4.20
• Event Studies in Finance: Campbell, Lo, and MacKinlay (1997), Loughran and Ritter (1995).

Sixth and seventh lecture: Liquidity - D4.20 both days
• Measuring liquidity: Roll (1984), Amihud(2002), Corwin and Schultz (2012), Feldhütter (2012), Schestag, Schuster, and Uhrig-Homburg (2016)
• Liquidity and asset pricing: Bao, Pan, and Wang(2011), Dick-Nielsen, Feldhütter, and Lando (2012)
• Liquidity and regulation: Bao, O’Hara, and Zhou (2017), Bessembinder, Jacobsen, Maxwell, and Venkataraman (2017)
• Liquidity and trading venues: Hendershott and Madhavan (2015)
• Liquidity and transparency: Goldstein, Hotchkiss, and Sirri (2007)

Eight lecture: Time series analysis -  D4.39
• Granger causality
• Unit root
• Cointegration
• Information share: Hasbrouck (1995), Blanco, Brennan, and Marsh (2005)
• Volatility modelling - ARCH

Learning objectives
Students should

• obtain an 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.

Exam
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.

Other
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.

Start date
03/09/2019

End date
29/10/2019

Level
PhD

ECTS
7.5

Language
English

Course Literature
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. 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). 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.

References
Joseph Adler. R in a nutshell. O’Reilly, 2010.

Euad Aleskerov, Ersel Hasan, and Dmitri Piontkovski. Linear Algebra for Economists. Springer, 2011.

Jack Bao, Maureen O’Hara, and Xing Zhou. The Volcker rule and corporate bond market-making in times of stress. Journal of Financial Economics, forthcoming, 2017.

Jack Bao, Jun Pan, and Jiang Wang. The illiquidity of corporate bonds. Journal of Finance, 66(3): 911-946, 2011.

Hendrik Bessembinder, Stacey Jacobsen, William Maxwell, and Kumar Venkataraman. Capital commitment and illiquidity in corporate bonds. Journal of Finance, forthcoming, 2017.

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.

Roberto Blanco, Simon Brennan, and Ian W. Marsch.An empirical analysis of the dynamic relation between investment-grade bonds and credit default swaps. Journal of Finance, 60(5): 2255-2281, 2005.

John Y. Campbell and Robert J. Shiller. Yield spreads and interest rate movements: a bird’s eye view. Review of Economic Studies, 58(3):495-514, 1991.

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.

John Cochrane and Monica Piazzesi. Bond risk premia. American Economic Review, 95(1): 138-160, 2005.

Shane A. Corwin and Paul Schultz. A simple way to estimate bid-ask spreads from daily high and low prices. Journal of Finance, 67(2): 719-760, 2012.

Jens Dick-Nielsen, Peter Feldhütter, and David Lando.Corporate bond liquidity before and after the onset of the subprime crisis. Journal of Financial Economics, 103: 471-492, 2012.

Eugene F Fama and J MacBeth. Risk, return and equilibrium, empirical tests. Journal of Political Economy, 81:607–636, 1973.

Peter Feldhütter. The same bond at different prices: identifying search frictions and selling pressures. Review of Financial Studies, 25:1155-1206, 2012.

Michael A. Goldstein, Edith S. Hotchkiss, and Erik R. Sirri. Transparency and liquidity: a controlled experiment on corporate bonds. Review of Financial Studies, 20(2):235-273, 2007.

William H Greene. Econometric Analysis. Pearson, seventh edition, 2012.

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.

Joel Hasbrouck. One security, many markets: determining the contributions to price discovery. Journal of Finance, 50(4): 1175-1199, 1995.

Terrence Hendershott and Ananth Madhavan.Click or call? Auction versus search in the over-the-counter market. Journal of Finance, 70(1): 419–447, 2015.

Ravi Jagannathan, Georgios Skoulakis, and Zhenyu Wang. Generalized method of moments: Applications in finance. Journal of Business & Economic Statistics, 20(4), October 2002.

Tim Loughran and Jay R. Ritter. The new issues puzzle. Journal of Finance, 50(1):23–51, 1995.

Craig MacKinlay. Event studies in economics and finance. Journal of Economic Literature, XXXV: 13–39, March 1997.

Craig MacKinlay and Matthew P Richardson. Using generalized method of moments to test mean-variance efficiency. Journal of Finance, 46:511–27, 1991.

Whitney K. Newey and Kenneth D. West. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3):703-708, 1987.

Richard Roll. A simple implicit measure of the effective bid-ask spread in an efficient market. Journal of Finance, 39(4):1127-1139, 1984.

Raphael Schestag, Philipp Schuster, and Marliese Uhrig-Homburg. Measuring liquidity in bond markets. Review of Financial Studies, 29(5):1170-1219, 2016.

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.

Halbert White. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4):817-838, 1980.

Fee
DKK 9,750

Minimum number of participants
8

Maximum number of participants
12

Location
Copenhagen Business School
Solbjerg Plads 3
2000 Frederiksberg

Tuesdays from 9-13 

Location: please see the lecture plan

 





Contact information
PhD Support
Bente S. Ramovic
bsr.research@cbs.dk
T
el: +45 38153138

Registration deadline
12/08/2019

Please not that the registration is binding after the registration deadline.
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