Macro-Stress Testing NPLs in the Banking Sector in Namibia
Bibliographic record
Abstract
This paper examined the impact of macroeconomic variables, namely real GDP growth, house price growth and changes in the repo rate on the non-performing loan (NPL) ratio in Namibia using data from 2004Q1 to 2020Q1. The study used a vector auto-regressive (VAR) model and impulse response analysis to estimate the impact of changes in macroeconomic conditions on NPLs, and further conducted stress testings on NPL ratio over 4 - 6 quarter horizons. Empirical evidence from this study shows that macroeconomic variables such as real GDP growth rate, the house price growth rate and the repo rate have a statistically significant impact, and material impact on the non-performing loans in the banking sector in Namibia. Largely, a positive growth rate shock in a quarter will reduce NPL ratio by more than half percentage point over two quarters. Similarly, a positive shock of about 4.0 percent in a quarter will reduce NPL ratio by more than 1.2 percentage points over four quarter horizons. Macro-stress-testing results revealed that a deterioration of the GDP growth rate by more than one standard deviation will increase the NPL ratio from 2.46 to 2.78 over four quarter horizons. Meanwhile, the combined effects of deteriorating the GDP growth rate and falling house prices further exacerbated the vulnerability of the banking sector. This study contributes to our understanding of the interplay between macroeconomic conditions and financial sector resilience in Namibia. In practical application, it shows that macro-stress testing techniques are useful to study the importance of macro-financial and feedback effects from the financial sector to the real economy. Technically, central banks must develop models that capture important macro-financial and feedback effects, and regulatory attention must be devoted to monitoring spillover effects from worsening financial conditions to the real economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".