FIN9874, Credit Markets

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• 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

Get familiar with the two sample data sets (cdssampledata.xlsx, sampledata.xlsx). Understand the data entries. Use a programming language to generate some summary statistics before end of week 2. No need to sumbit anything. Just get prepared for the following projects.

• • 1. Implement the Merton model by matching market cap and stock return volatility following Bai and Wu (2016). Map the distance to default to actual default events (use cdssampledata.xlsx for default information) to generate default probability estimates (using grouping, kernel weighting, or logistic regression). Due end of week 4.

    2. Transform distance to default to credit spread valuation. Experiment long-short portfolio construction. Due end of week 5.

       • (optional) Repeat the Merton model implementation on a different data set (sampledata.xlsx) with bond spreads and returns over next month

       • (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, following Wu and Zaman (2024). Due 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. Discuss implications of 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 ("A look into the sample data") at the end of each section, I will show how to do some of the analysis in matlab.

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

• Class Contents

Introduction (slides, video)

1. Credit markets and instruments (slides): An introduction of the credit markets and instruments

   • The landscape (video): a general discussion on different instruments with tradeoff between risk and income

   • Cash instruments (video): an overview of cash instruments such as bonds, loans, and private credit

   • Synthetic instruments (video): an overview of synthetic instruments such as CDS, index CDS, credit ETFs, and total return swaps

   • Structured products (video): an overview of structured products such as ABS, MBS, and CLO, CDO, CMO tranches

   • Market structure (video): a short discussion on the market structure and the business model for being a successful dealer in the secondary market

   • A look into the sample data: summary stats 

2. The capital structure (slides): Introduce a valuation framework on the capital structure to predict credit risk and generate relative value opportunities.

   • week 2: Introduce the concept of the capital structure and the Merton structural model

     • Identify investment opportunities along the capital structure (video) : a general discussion how one identifies investment opportunites along the captial structure

     • The Merton structural model (video): We introduce the classic structural model by Merton, which treats equity as a call option on the firm value.

   • week 3: Apply the Merton model to predict default probabilities

     • Forecast default probabilities (video) : We explore the application of the structural model in forecasting default probabilities and generating credit ratings

     • Forecast default probabilities: Some technical details (video) : We go over some technical details in converting default events to default probability estimates, and converting distance to default estimates to default probability prediction over any horizons.

     • A look into the sample data : Implement the Merton model, and map the distance to default to default probabilities by grouping, kernel weighting, and logistic regression 

   • week 4: Apply the Merton model to generate relative values on credit instruments

     • Predict credit spreads (video): We explore the application of the structural model in generating fair valuation on credit spreads (on bonds or CDS). We also illustrate how to trade on the mispricing from the valuation.

     • Statistical arbitrage on relative valuation (video): We explore different types of statistical arbitrage trading based on the model valuation.

     • A look into the data (video): Implement the Merton model for credit valuation, with statistical bias correction

   • week 5: Apply the Merton model to disentangle risk exposures in stocks and bonds

     • Disentangle risk exposures (video): We discuss yet another application of the structural model in disentangling risk exposures in stocks and bonds.

     • Disentangle risk exposures: Applications (video): We use the structural risk decomposition to shed light on some observed market behaviors.

     • Beyond Merton (video): A general discussion on the role of a model and future directions for model development.

     • A look into the data: Implementing the Merton model for risk exposure construction 

3. The risk structure (slides): Explain the motivation of forming long-short portfolios to cancel out systematic risk exposures and discuss the construction of robust risk structures

   • week 6: Explain the motivation of forming long-short portfolios to cancel out systematic risk exposures and the evolution of risk factor structures.

     • The investment decision (video): A general discussion of the investment decision and how appropriate long-short portfolio construction can dramatically increase the benefit of diversification

     • The risk factor structure (video): We discuss different approaches in constructing risk factor structures for the purpose of constructing robust long-short portfolios

   • week 7: Use the corporate bond market as an example to examine the market's risk factor structure 

     • Features as loadings in the bond market (video): We use the US corporate bond market as an example and illustrate how to construct a robust risk factor structure

     • A look into the data: Bond return structures across maturity and default