Faculty
Peter Feldhütter, Professor of Finance, Copenhagen Business School

 

Prerequisites
Knowledge of asset pricing, corporate finance and econometrics at a M.Sc. level is expected. Otherwise, the course is designed as a first PhD 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. There are a number of alternatives to Matlab that are free, such as Julia, R and Python. 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 course on fixed income at the PhD level. The course attempts to lay the groundwork for students who will later do actual empirical research work in fixed income. 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
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.

 

• Violations of OLS Assumptions - HAC corrections: White (1980), Newey and West(1987)
• The expectation hypothesis: Campbell and Shiller (1991), Cochrane and Piazzesi (2005), Cieslak and Povala (2015), Bauer and Hamiltion (2018)
• Pricing the cross-section of corporate bonds: Gebhardt, Hvidkjaer, and Swaminathan (2005), Jostova, Nikolova, Philipov, and Stahel (2013), Bai, Bali, and Wen (2019), Chung, Wang, and Wu (2019)

•  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)

 

Teaching style
Lectures with exercises.

 

Lecture plan
The lecture plan of the course encompasses 2 days of approximately 5 teaching hours per day, scheduled for: 

 

Day 1 08-03-2023
Lecturer: Peter Feldhütter
Lectures (5 hours)

 

Day 2 22-03-2023
Lecturer: Peter Feldhütter
Lectures and student presentations (5 hours)

 

Learning objectives
• 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 2 hand-ins to empirical problems (up to 10 pgs). 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. 

 

Submission dates:
22 March 2023 - hand-in 1
5 April 2023 - hand-in 2

 

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.

 

Course Literature
• 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 130(1): 95-113, 2018. 
• 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 73(4): 1615-1661, 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. 
• 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. 
• 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. 
• 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. 
• Halbert White. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4):817-838, 1980.