FIN9874, Credit Markets

Professor:

Semester and Session:  

Office Hours: 

Contact information:

• Overview

The class provides students with a general framework for performing systematic investment in the credit market. It contains three major components: (1) the instruments that investors can use to gain credit exposures, (2) a valuation framework that allows investors to identify investment opportunities, and (3) a risk structure for constructing long-short credit portfolios to hedge and/or target specific risk exposures.

• Readings

There is a set of lecture notes (slides) that correspond to the video recordings. In addition, some discussions are based on several research papers. Students can read these papers to understand more technical and implementation details. They should also be helpful for completing the required projects.

• • Merton, 1974, On the pricing of corporate debt, Journal of Finance, 29(1), 449. This is the original classic structural model by Nobel Laureate Robert Merton. We introduce the valuation framework based on different implementations of this model. Many other papers talk about this model. You can read the original here.

  •  Bai and Wu, 2016, Anchoring corporate credit spreads to firm fundamentals, JFQA, 51(5), 1521.  This paper shows how to implement the Merton model to generate credit spread valuation and perform long-short credit investment.

  • Wu and Xu, 2023, Separate risk from optionality. This paper shows how to use the Merton model to generate structural risk exposure estimates and to disentangle linear return risk from optionality exposures. It shows that investors are averse to risk, but love optionality, leading to interesting and sometimes puzzling asset pricing behaviors. 

  • Wu and Zaman, 2024, Finding value in the US corporate bond market. This paper examines the risk structure of the US corporate bond market, and also proposes a statistical yield valuation model to identify mispricing opportunities.

• Class format: Online asynchronous

The class will be delivered via a combination of pre-recorded videos and live tutorial Q/A sessions. 

• Projects, grades, and class policies

  • Grading will be based on the completion of a project. The project is broken down into 4 segments, corresponding to different stages of the class

    1. Data analysis and summary statistics: Look into the data, understand the meaning of each entry, generate summary statistics to understand the general behaviors of each variables. Also learn to use a programming language. Due end of week 2.

    2. Implement the Merton model by matching market cap and stock return volatility following Bai and Wu (2016). Generate credit spread valuation and  experiment long-short portfolio construction. Due end of week 5.

       • (optional) Search the web for different implementation methods. Experiment with some of them. Compare results in their predictive correlation with credit spreads.

    3. Implement the Merton model by matching market cap and bond yield following Wu and Xu (2023). Generate and summarize delta and vega exposures. Due end of week 6.

       • (optional) Examine how different implementations change the delta and vega exposure estimates.

       • (optional) Estimate the joint return factor model as in Wu and Xu (2023)

    4. Examine bond return correlation as a function of distance in maturity and distance to default (from either implementation), following Wu and Zamaan. End before final.

       • (optional) Examine bond return correlation as a function of vega and delta exposure distances

       • (optional) Repeat the analysis on stock returns

  •  Present the results in a well-written paper format. Describe how you did the implementation (methodology). Summarize your findings. Make discussions from your findings (if any). Keep your writing simple, short, and clear. Use your own words. 

  • Each segment represents 25% of your grade. Doing each optional topic earns a maximum of 5% extra credit.

  • You can open the data in excel and do some summary analysis, but to complete the project, you will need to learn some programming language such as matlab, R, python. In Q/A sessions, I will show how to do some of the analysis in matlab and provide some sample code.

  • Feel free to search the web, ask AI, for help in coding. But I still want to see your personal analysis.

  • Sample data (excel, parquet, mat)

• Class Contents

Introduction (video)

1. Credit markets and instruments (slides)

   • The landscape (video)

   • Cash instruments (video)

   • Synthetic instruments (video)

   • Market structure (video)

   • A look into the sample data: summary stats (video) 

2. The capital structure (slides)

   • week 2

     1. Identify investment opportunities along the capital structure (video) 

     2. The Merton structural model (video)

   • week 3

     1. Forecast default probabilities (video) 

   • week 4

     1. Predict credit spreads (video) 

     2. A look into the data: implementing the Merton model for credit valuation (video)

   • week 5

     1. Disentangle risk exposures (video)

     2. A look into the data: implementing the Merton model for risk exposure construction (video)

3. The risk structure (slides)

   • week 6

     1. The investment decision (video)

     2. The risk factor structure (video)

   • week 7

     1. Features as loadings in the bond market (video)

     2. A look into the data: Bond return structures across maturity and default (video)