Estimation of foreign exchange exposure for public-private partnership infrastructure projects
Bibliographic record
Abstract
Economic foreign exchange (FX) exposure is an important risk factor which affects Public-private partnership (PPP) projects in developing countries.The risk exists because PPP projects typically sell their outputs domestically and generate revenues in local currency, while their financing costs and operating and maintenance costs are often denominated in hard currencies.Traditionally, FX risk is tested through the use of risk factors on revenue and costs or by adopting conservative assumptions in the cash flow.While this method provides a range of the risk value based on scenarios it does not give the potential FX risk exposure.What constitutes minimum and maximum risk values is often defined on the basis of subjective judgments.This research contributes to the solution of this problem with a methodology to quantify annual economic FX exposure in project companies financed under project finance modality.The application of the developed FX index to describe the project feasibility on economic FX exposure is superior as it is an extra tool which is linked to the financial models without the ambiguities to incorporate risk factors in the cash flow.It is a unique mathematical process for dimensioning currency risk on a various set of cash flow positions.A first-order second-moment reliability method based on the Hashofer-Lind reliability index beta is undertaken to reflect the uncertainties of market risks with impact on the cash flow of the PPP project.The FX index is modelled via an expanding dispersion ellipsoid in the original space of random variables.The input variables in the proposed foreign exchange exposure (FEE) model include inflation rates, interest rates and foreign exchange rates.The variables form the ellipsoid of an n-dimensional shape.It reflects not only the effect of the mean values but also the covariances of the random variables influencing a defined investability domain.The computation of the FX index involves eigenvalues and eigenvectors, rotation of the reference frame, and transformed space for the random variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".