Determinants of credit spread changes for the financial sector
Bibliographic record
Abstract
Purpose Most of the credit spread literature deals with the determinants of credit spread changes for individual bonds. The purpose of this paper is to investigate the explanatory power of credit spread changes and their determinants for portfolios. Design/methodology/approach Using ordinary least squares (OLS) regressions and monthly data from 1990 to 1997, this paper tests several new potential determinants (e.g. portfolio diversification) and expectations (and realizations) for some previously identified determinants (e.g. gross domestic product (GDP)) of credit spread changes for portfolios of financials as derived from spot curves. Findings Strong empirical support is reported that default risk and undiversified risk are priced in credit spreads. The paper finds that forecasts for GDP and inflation are better determinants of credit spread changes than the realized values previously used in the literature, which is consistent with the notion that term structures convey expectations about future interest rates. Research limitations/implications Interesting issues for future research include the sensitivity of the results to the use of other procedures for deriving zero‐coupon spot rates, and whether forecasts of macrovariables (such as GDP) are better determinants of credit spreads for other industrial categories, such as utilities and industrials. Practical implications The findings provide guidance for the management of risk for fixed income portfolios, for the pricing of fixed income securities differentiated by the difficulties encountered in achieving well‐diversified portfolios, and for assessing the performance of credit spread portfolios managed by financial institutions. Originality/value The empirical model, which achieves substantial explanatory power while being parsimonious, is the first to support the usage of forecasts instead of realized values in determining credit spreads, and to show that undiversifiable risk is an important component of the credit spreads of portfolios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".