Risk framework analysis in the management of sovereign debt: The Argentine case
Bibliographic record
Abstract
The main objective of this paper is to develop a practical approach to Argentina's sovereign risk management. Through Contingent Claim Analysis (CCA), Gape, Gray, Lim and Xiao (2008)[1] developed a sovereign risk framework whereby we can construct a marked to market sovereign balance sheet and obtain a set of credit risk indicators that can help policy-makers: set thresholds for foreign reserves, design risk mitigation strategies and select best policy options. The main contribution is that instead of using a conventional index such as GBI-EM in order to estimate the volatility of domestic currency liabilities, we use 24 sovereign domestic currency bonds to construct an interest rate covariance matrix. That is, an interest rate sensitive sovereign portfolio, whose risk factor variations are represented by a vector of the portfolio PV01 (present value of a basis point change) with respect to each interest rate of the zero-coupon yield curve. Since zero-coupon rates are rarely directly observable, we must estimate them from market data. In this paper we implemented a widely-used parametric term structure estimation method called Nelson and Siegel. For Argentina we generated two yield curves, i.e., sets of fixed maturity interest rates determined by Badlar and CER.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".