Modeling Government Bond Curve Spreads and Their Drivers
Summary
The document considers how to study spreads between maturities on the same government issuer’s yield curve. It raises credit risk, liquidity, risk aversion, and crisis periods as possible explanatory factors, and asks whether differencing the series before an ordinary least squares regression could address nonstationarity. One response recommends examining changes, both for stationarity and to remove unobserved issuer characteristics that do not vary over time. It cites research on credit spread changes as relevant background.
A second response suggests adding macroeconomic variables such as growth, inflation, rate expectations, money supply, and quantitative easing, along with term premia and cross-market measures. It sees little need for principal component analysis when the selected variables represent distinct risk premia, though PCA can help reduce dimensionality among correlated predictors. These are suggestions rather than a complete empirical design: the document offers no data, regression results, stationarity tests, or guidance on identification and model diagnostics.
Key ideas
- Differencing spread and driver series may address stationarity and remove fixed issuer-level characteristics.
- Potential drivers include credit risk, liquidity, risk aversion, and crisis periods.
- Macroeconomic conditions, term premia, and cross-market curves may add explanatory factors.
- PCA is most useful for reducing dimensionality among correlated variables, rather than combining distinct risk drivers.
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Full text
# Bond Spread Drivers # Bond Spread Drivers I have some work to do on the drivers of government bond spreads - ie. across terms (not across governments) of the yield curve, say 5yr and 20yr bond spreads from the same government issuer - and am having a bit of conceptual difficulty with this. I have done some reading and get a sense that potential drivers could be: credit risk (measured by Credit default swap (CDS) rates); liquidity risk (measured by bid-ask apreads); risk aversion; crisis-period variable (a indicator variable assigned a value 1 during the latest financial crisis); Should I just take the difference of these variables (and assume the differencing renders the variables stationary) and then run OLS regression of the bond spreads on the drivers? Are there better ways to go about this? Would PCA have any application here? Thanks in advance for any advice ## Answer by Roberto Liebscher (score 2) https://quant.stackexchange.com/a/10972 A good piece of literature on this is Colin-Dufresne et al. (2001), "The Determinants of Credit Spread Changes", Journal of Finance, 56, 2177-2207. I think differencing all variables is a good idea in this case. Not only because of stationarity concerns but also because of unobserved time-invariant issuer-level characteristics. I do not see the case for a PCA here. Usually you use PCA to reduce dimensionality of sometimes highly correlated variables. Here you have variables at hand that seem to capture different risk premia. ## Answer by rrg (score 0) https://quant.stackexchange.com/a/39999 The list of risk drivers that you provide are good. I would add significant macroeconomic factors: - risk aversion indicator - growth and inflation - => rate expectations - money supply - QE Also add more curve factors: - duration or term premia i.e. 10y term premium ( ... ) - cross-market curves - swap spreads
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