QUANTIFYING THE RISK ON BANKSâ RETURNS ARISING FROM FINANCIAL TECHNOLOGY ADOPTION: AN ASYMMETRIC GARCH APPROACH TO VALUE-AT-RISK
Bibliographic record
Abstract
The rapid global technology revolution has raised serious concerns on what could be its long run impact on banks, especially with its attendant technological unemployment. The on-going debate in literature whether and to what degree financial technology adoption will emit risk to bank profitability is examined in this study. The trade-off analysis and a family of symmetric and asymmetric GARCH approach to Value-at-Risk (VaR-GARCH) based on the camel and value at risk theoretical framework were used to determine potential risk and estimate the conditional variance of bank returns in a panel of thirty-four African countries for the period 2002-2018. The Kupiec log likelihood ratio test and mean relative scaled bias used to evaluate the models’ accuracy and efficiency levels respectively found that the best model to estimate the conditional variance of bank returns is the exponential GARCH (1, 1) with student-t distribution. The worse expected loss on banks’ return due to FinTechs adoption will not exceed 3.01% at 95% confidence interval. Therefore, with a higher FinTechs’ risk/standard-deviations than that of banks’ return and a high VaR value of bank returns, it implies that aside banks, FinTechs also emits risks to other sectors, therefore this study recommends that African economies will benefit from FinTechs diffusion through improved financial service delivery only when a substantial level of collaboration between bank financial institutions and FinTechs companies is reached.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".