LDPD (version 1.1.2)

ARestimate: Accuracy Ratio estimation

Description

Estimate AR (Accuracy Ratio) and mean portfolio PD (probability of default) based on conditional PDs and portfolio unconditional distribution.

Usage

ARestimate(pd.cond, portf.uncond, rating.type = "RATING")

Arguments

pd.cond

Conditional PD distribution (should be sorted from the worst to the best credit quality).

portf.uncond

Unconditional portfolio distribution (should be sorted from lowest credit quality to higher one).

rating.type

In case 'RATING', each item in the portf.uncond should contain number of companies in each rating class. In case 'SCORE', each item in the portf.uncond is an exact score.

Value

AR

Estimated accuracy ratio

CT

Mean PD in the portfolio

Details

Approach to AR estimation is consistent with the algorithm proposed by D.Tasche in the paper: Estimating discriminatory power and PD curves when the number of defaults is small. Working paper, Lloyds Banking Group, 2009. Mean portfolio PD (also known as Central Tendency of the portfolio) is estimated using unconditional portfolio distribution.

References

Tasche, D. (2009) Estimating discriminatory power and PD curves when the number of defaults is small. Working paper, Lloyds Banking Group. Tasche, D. (2013) The art of probability-of-default curve calibration. Journal of Credit Risk, 9:63-103.

See Also

QMMRecalibrate somers2

Examples

pd.cond <- c(0.1, 0.05, 0.025, 0.01, 0.001)  # PD for given rating class 
portf.uncond <- c(10, 20, 30, 50, 10)  # Number of borrowers in each rating class
ARestimate(pd.cond, portf.uncond, rating.type = "RATING")