Value at Risk Disclosures: The Case of Canada Revisited
Bibliographic record
Abstract
This paper is based on the empirical analysis that has been conducted by (Pérignon, Deng and Wang, 2007), to test whether the Royal bank of Canada (RBC) and the Bank of Montreal (BMO) are overstating their Value at Risk (VaR). This study is based on non-anonymous data of the daily VaR and P&L for both banks within the period starting from the year 2001 till the year 2010. The paper exhibits results contradicting those of (Pérignon, Deng and Wang, 2007) , it shows that RBC and BMO do not overstate their VaR; in other words the banks are accurate in disclosing their VaR measure according to the data derived from the analyses performed. The data used in this paper is based on the graphs extracted from the banks’ annual reports using the R software for computing statistics to transfer the graphs into time series data. After extracting the data conditional and unconditional coverage tests were performed to test the accuracy of disclosing daily VaR and profit and loss data. Two benchmarks have been developed; the Historical Simulation and the GARCH model to compare the commercial banks' VaR with the forecasted VaR depending on the benchmarks.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".