Value at Risk Disclosures: The Case of Canada Revisited
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it