Module: | MODULE C: TREASURY MANAGEMENT
Q490: Consider the following statements regarding the statistical mechanics, regulatory mandates, and limitations of Value at Risk:
1. For regulatory capital calculations under the Internal Models Approach, the RBI strictly mandates banks to compute Value at Risk using a one-tailed 99% confidence interval and a 10-day holding period.
2. Backtesting is a mandatory rigorous validation process comparing daily Value at Risk estimates against actual trading profit and loss, where exceptions trigger a higher regulatory capital multiplier.
3. The Historical Simulation method of computing Value at Risk utilizes actual historical market price movements, effectively bypassing the need to assume a normal statistical distribution of returns.
4. A primary limitation of standard Value at Risk is its failure to capture the magnitude of losses located in the extreme tail, a flaw mathematically addressed by utilizing Expected Shortfall.
2. Backtesting is a mandatory rigorous validation process comparing daily Value at Risk estimates against actual trading profit and loss, where exceptions trigger a higher regulatory capital multiplier.
3. The Historical Simulation method of computing Value at Risk utilizes actual historical market price movements, effectively bypassing the need to assume a normal statistical distribution of returns.
4. A primary limitation of standard Value at Risk is its failure to capture the magnitude of losses located in the extreme tail, a flaw mathematically addressed by utilizing Expected Shortfall.
✅ Correct Answer: C
Value at Risk (VaR) is the premier statistical metric used by treasuries, quantifying the maximum expected financial loss a portfolio could suffer over a target horizon at a specified confidence level under normal market conditions.
For banks authorized to use the Internal Models Approach to calculate market risk capital charges, Basel and RBI guidelines strictly mandate calculating VaR using a 99% one-tailed confidence interval over a 10-day holding period.
To ensure these internal models are not artificially under-reporting risk, regulators mandate "Backtesting." This involves checking yesterday's VaR against today's actual P&L; if actual losses exceed the predicted VaR (an "exception"), it implies the model is flawed.
Too many exceptions in a 250-day window penalize the bank with a higher capital scaling multiplier.
VaR can be calculated via Parametric (Variance-Covariance), Monte Carlo, or Historical Simulation methods.
Historical Simulation is popular because it uses actual past market data, bypassing the flawed assumption that financial returns always follow a perfect "normal" (bell-curve) distribution.
However, VaR fundamentally fails to predict "Black Swan" events; it states the probability of exceeding a loss but not the severity of the loss in that extreme 1% tail.
This structural flaw is resolved by calculating "Expected Shortfall" (ES), which averages the severe losses that exist beyond the VaR threshold.
A: This option incorrectly excludes statement 3. The distinct advantage of the Historical Simulation VaR model—that it does not rely on the assumption of normally distributed returns—is factually precise.
B: This option incorrectly excludes statement 1. The specific regulatory parameters of a 99% confidence interval and a 10-day holding period are rigid, non-negotiable Basel mandates for capital calculation.
C: This is the correct option.
All four statements flawlessly map the regulatory parameters, the backtesting penalty mechanism, the Historical Simulation advantage, and the Expected Shortfall tail-risk solution.
D: This option incorrectly isolates statements 1 and 3, completely ignoring the mandatory backtesting framework and the critical limitation addressed by Expected Shortfall.
For banks authorized to use the Internal Models Approach to calculate market risk capital charges, Basel and RBI guidelines strictly mandate calculating VaR using a 99% one-tailed confidence interval over a 10-day holding period.
To ensure these internal models are not artificially under-reporting risk, regulators mandate "Backtesting." This involves checking yesterday's VaR against today's actual P&L; if actual losses exceed the predicted VaR (an "exception"), it implies the model is flawed.
Too many exceptions in a 250-day window penalize the bank with a higher capital scaling multiplier.
VaR can be calculated via Parametric (Variance-Covariance), Monte Carlo, or Historical Simulation methods.
Historical Simulation is popular because it uses actual past market data, bypassing the flawed assumption that financial returns always follow a perfect "normal" (bell-curve) distribution.
However, VaR fundamentally fails to predict "Black Swan" events; it states the probability of exceeding a loss but not the severity of the loss in that extreme 1% tail.
This structural flaw is resolved by calculating "Expected Shortfall" (ES), which averages the severe losses that exist beyond the VaR threshold.
A: This option incorrectly excludes statement 3. The distinct advantage of the Historical Simulation VaR model—that it does not rely on the assumption of normally distributed returns—is factually precise.
B: This option incorrectly excludes statement 1. The specific regulatory parameters of a 99% confidence interval and a 10-day holding period are rigid, non-negotiable Basel mandates for capital calculation.
C: This is the correct option.
All four statements flawlessly map the regulatory parameters, the backtesting penalty mechanism, the Historical Simulation advantage, and the Expected Shortfall tail-risk solution.
D: This option incorrectly isolates statements 1 and 3, completely ignoring the mandatory backtesting framework and the critical limitation addressed by Expected Shortfall.