Sources Allocation on Risk Performance of Egyptian Insurance Companies
Bibliographic record
Abstract
The main objective of current paper was to investigate the relationship between sources allocations measured by (external sources to total assets- free investment and allocated investment to total assets) on risk performance measured by financial risk (standard deviation of return on equity) business risk (standard deviation of return on assets) using ARDL and GARCH model, using a sample of 19 Egyptian insurance companies over a 21 year period form 1999 – 2019, the findings indicate that There is a significant negative linear relationships between the independent variable in terms of capital structure and assets structure on dependent variable standard deviation of return on equity (Y1) at a Significant level less than (0.05). Also there is a significant positive linear relationship between the independent variable in terms of assets structure and dependent variable standard deviation of return on assets (Y2) at a significant level less than (0.001). with regard to ARCH and GARCH result shows the There is a significant positive effect of the ARCH term, measured as the lag of the squared residual from the mean equation, and the GARCH term, Last period’s forecast variance, at a significant level less than (0.05). It means that the high volatility in the conditional variance of standard deviation of return on assets y2. also The sum of the two parameters of: (α+β) in the GARCH model (1, 1) whether the random error are distributed according to normal distribution approaches the positive one, which indicates that the two conditions of non-negative variance, and the variance is not inflated are satisfied, and that Indication of the continuity of volatility shocks in the standard deviation of return on assets (Y2).
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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 | 0.000 | 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 | 0.000 | 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".