The Use of Principal Component Analysis (PCA) in Building Yield Curve Scenarios and Identifying Relative-Value Trading Opportunities on the Romanian Government Bond Market
Bibliographic record
Abstract
Based on previous research addressing the use of principal component analysis (PCA) in modeling the dynamics of sovereign yield curves, in this paper, we investigate certain characteristics of the Romanian government bond market. We perform PCA on data between March 2019 and March 2022, with emphasis on periods marked by extreme market stress, such as the outbreak of the COVID-19 pandemic in March 2020 or the Russian military invasion in Ukraine in February 2022. We find that on 25 March 2022, the first principal component explained 80.83% of the yield curve changes, the first two 91.92%, and the first three 96.87%, consistent with previous results from the literature, which state that the first three PCs generally explain around 95% of the variability in the term structure. In addition, we observe that principal components’ coefficients (factor loadings) at 2 years were lower than those at 10 years, suggesting that in case of market sell-offs, yields at 10 years increase more than those at 2 years, leading to yield curve steepenings. Interestingly, we observe that the explanatory power of the first PC increases significantly following extreme market events, when interest rates’ movements tend to become more synchronized, leading to higher correlations between tenors. We also employ PCA to check for relative-value (RV) trading signals and to assess the historical plausibility of yield curve shocks. We found that while both explanatory power and shape plausibility were characteristics of the yield curve dynamics during the outbreak of the COVID-19 pandemic, the magnitude of the market movement registered in mid-March 2020 was unlikely from a historical perspective. Finally, we use a forecasting model to derive the entire structure of the Romanian yield curve while also incorporating the trader’s view on a few benchmark yields.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".