Testing Granger Causality Between Stock Returns and CDS Spread Changes
Summary
The discussion considers whether stock prices and credit default swap (CDS) spreads should be modeled in levels or differences when testing for Granger causality. The questioner uses log stock returns and first differences of CDS spreads because both level series are nonstationary, and asks whether cointegration must be checked before fitting a vector autoregression (VAR).
The response emphasizes sound time-series practice: account for the series’ integration and cointegration properties, and consider a vector error correction model (VECM) when log stock prices are cointegrated. In that setup, the model’s dependent variables can be log returns while retaining the long-run relation. The intended interpretation—whether stock returns help predict spread changes—is considered reasonable after the relevant causality test. The exchange offers conceptual guidance rather than an empirical analysis or detailed testing procedure; readers must establish the time-series properties of their own data before choosing a specification.
Key ideas
- Check integration and cointegration properties before selecting a causality model.
- A VECM can represent cointegrated log stock prices while modeling changes as dependent variables.
- The intended question can be framed as whether stock returns predict CDS spread changes.
- Statistical validity should guide model choice before interpreting the economic results.
Tags
Full text
# Granger causality with stocks and CDS # Granger causality with stocks and CDS I would like to take a closer look at stock prices and CDS spreads of different entities. Because both of them are nonstationary in levels, I use log stock returns and the first difference of the CDS. My question: Can I run the Granger causality test on the Vector Autoregressive model (VAR) with the included variables in differences? And do I have to check for cointegration at first (and use a VECM)? Help is very much appreciated. ## Answer by Richard Hardy (score 1, accepted) https://quant.stackexchange.com/a/34476 Regarding testing for Granger causality in presence or absence of cointegration, I find the extensive blog post by Dave Giles "Testing for Granger Causality" very helpful. > [M]y question is whether it makes sense to model stock prices <...> instead of log stock returns (not diff), when we are interested in returns. I get that when I am interested in X, I should just leave it in levels even if nonstationary. I wanted to ask whether it still makes sense to model it like that when I am interested in the return itself. Let me contrast the subject-matter problem to the statistical problem. From the statistical perspective, to ensure the validity of your results, you need to follow sound statistical practice (e.g. as described in Dave Giles' blog). How to interpret the results comes in second. Fortunately, if you assume (and preferably validate the assumption by testing) that the logs of stock prices are cointegrated, you can use VECM where the dependent variables will be the log-returns. > Just to be sure about it: Is that more or less correct? <...> "Stock returns Granger cause spread changes"; this is the interpretation I had in mind (well, after checking whether there is Granger causality of course). The data to be used are stock prices and spreads. I think your interpretation is fine.
Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.