Credit indices are financial instruments used to gauge the overall creditworthiness and risk of a particular market or sector. These indices provide investors with a snapshot of credit risk within a specific market, allowing them to assess the health and stability of that market.
There are two prevalent categories of credit indices. One of these is the Credit Default Swap Index (CDX). The CDX is created by assembling a basket of companies, typically within a specific sector, credit rating range, or geographic region, and subsequently issuing a Credit Default Swap (CDS) tied to the performance of these firms. The valuation of the CDX is, therefore, contingent on the market’s assessment of the creditworthiness of the entire basket of companies.
The second category of credit indices pertains to bond indices. These indices evaluate the performance of a bond portfolio, often determined by considering the credit ratings of the constituent bonds. They are typically divided into two categories: Investment Grade (IG) bonds, which are considered safer, and High Yield (HY) bonds, which are viewed as riskier components.
Reference [1] studied suitable models for forecasting bond indices. The author decomposed a bond index into two components: credit spread and the risk-free rate, and subsequently modeled each component separately. They pointed out,
Firstly from visually analyzing residuals and QQ-plots we conclude that autoregressive spread models are generally able to accurately forecast future spreads. Some tail behaviour is however more difficult to accurately capture. To make the model more accurate, one could implement more complex distributions for the innovations of the processes, such as skewed Student’s t distributions and different mixture models. The ability to predict credit indices could be improved by modelling the difference term in new ways, which was not explored in this thesis. The AR(1)-process did not capture all the properties of the historical difference term.
Most of the instability seems to come from the interest rate model. Therefore, we think the main focus for future research on creating stable credit index models should focus on the interest rate model, and how other types of interest models could impact the stability.
In brief, the credit spread can be accurately forecasted using an autoregressive model, while most of the instability originates from forecasting the risk-free interest rate.
We were surprised by how unstable models for forecasting the risk-free interest rate can be.
Let us know what you think in the comments below or in the discussion forum.
References
[1] Grimlund, Erik and Wallén, Melker, Credit Index Forecasting: Stability of an Autoregressive Model, 2023, KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
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