The following measures walk through the IMCC calculation and can be found in the bookmark: Basel Framework → IMA → IMCC.
The calculations start with the P&L vectors. These can be queried using the PnL Expand measure.
The P&L vectors are defined per Liquidity Horizon, Risk Class, and Data Set. Aggregation across these levels is not defined, so each of these levels must be present in queries for this measure.
Additionally, PnL Expand expands the P&L vector along the Scenarios analysis hierarchy.
Next, the ES (Basic) measure calculates the expected shortfall from the P&L vector.
Again, there is no aggregation across the Liquidity Horizon, Risk Class, and Data Set levels and they must be present in queries for this measure.
The confidence level comes from the ima.es.confidence-level parameter, and defaults to 97.5%.
The ES (Liquidity Adj.) measure combines the expected shortfall across liquidity horizons, by performing the liquidity adjustment of MAR 33.4.
The Data Set and Risk Class levels must still be present in queries for this measure.
The value of T comes from the ima.base-horizon parameter, and defaults to 10.
The ES (Model Variation) measure is the ratio of the expected shortfall of the reduced, current dataset with the full, current dataset. As per MAR 33.5 (2)(b), this should be at least 75%.
The Risk Class level must still be present in queries for this measure.
The ES (Capital) measure combines the expected shortfall across datasets to calibrate it to the period of stress, according to MAR 33.6.
The Risk Class level must still be present in queries for this measure.
The ES (Capital Constrained) measure is the sum of ES (Capital) over the risk-classes, excluding the “allin” risk-class.
The Omega measure is the value of ω in the MAR 33.15 FAQ.
The IMCC measure uses the ES (Capital Constrained) and ES (Capital Unconstrained) measures to calculate IMCC in MAR 33.15.
The value of ρ comes from the ima.rho.imcc parameter, and defaults to 0.5.