Title: Allocation of Economic Capital
1Allocation of Economic Capital
Preben Munch Thomsen pt_at_danskebank.dk Credit Risk
Modelling Risk Management
2Motivation and Summary
- As the Basel II framework defines the standard
for capital adequacy, the most valuable outcome
of an internal credit risk model is actually not
the EC level, but rather the capital allocation
key. - In the light of portfolio optimization it is
very important that capital allocation is
calculated as the marginal contribution to the
total risk and thereby catching concentration
and diversification. - To serve all applications, it is furthermore
essential that EC is allocated to the lowest
possible level facility level.
3Use of allocated credit EC and allocation model
- Performance (BU-, branch-, and customer level)
- Risk based pricing Corporate loans and
derivative contracts - ICAAP
- Pillar II Stresstests
- Industry analysis
- Risk management reporting
- Monthly BU reports
- Large counterparty watch list
4 Calculation of Credit Risk EC / principle
5Capital allocation is a 2-step process
- The credit risk model allocates EC to
individually simulated obligors and pools - Capital allocation model allocates EC to facility
level - Developed internally by Danske Bank
- Consist of a capital allocation function build
upon typical risk parameters PD, EaD, LGD, M, a
concentration term and some regression parameters
- Regression parameters are estimated from data on
large obligors including allocated EC from the
Credit Risk Model.
6Allocation inside the Portfolio Credit Risk Model
- Allocation to
- Large individual obligors 70 of portfolio
EaD-wise - Pools of facilities for small and medium-size
companies and households - Coherent capital allocation with a coherent risk
measure - Ensures full allocation
- Additive (Eulers theorem)
- Allocate diversification benefits to obligors
that contributes to portfolio diversification
7Expected Shortfall vs. VaR
ES is the common risk measure for allocation in a
credit risk model
- Coherent opposite to VaR.
- Main difference is that ES (as opposed to VaR) is
sub additive - which reflects that risk can be reduced
by diversification - Average measure Increased stability at obligor
level (compared to VaR with equal number of
simulations)
VaR_at_99.97
EL
EC_at_99.97
8Expected Shortfall choice of quantile
- Theoretically, use ES_at_99.85 as it approximately
match the VaR 99.97 loss at portfolio level. - However, level problematic with respect to
stability of the regression parameters Less
statistical significant and too sensitive to
portfolio changes. - ES_at_95 chosen as compromise
- The 95 quantile is far from the tail of the
loss distribution.
9Capital allocation function
Scaling
Single-name
Maturity
Rating
Loss amount
Concentration
- a ensures that aggregation of all facilities
EC match the portfolio EC - Increased maturity
increases migration risk and hence EC - 9 country
weights - based on obligors turnover
distribution - Allocation function modelled at
obligor level and applied at facility level
10Regression process- Model issues / Log
transformation
- Log-transform is good
- Ensures usable fit
- Optimization easy (linear model)
- Transformation of data of unlike characteristic
- Large and small EC values ends up on same scale
- Heavy-tailed data countered
- Log-transform is bad
- Error minimized in wrong space (OLS), i.e.
log(EC)-space. Error tolerence in EC-space very
un-even for large and small EC values.
Fixed tolerencein log(EC)-space
11Regression process- Model issues / Weighted
Least Squares (WLS)
- WLS optimization- data points treated with
individual weight in error function as opposed to
OLS.
The weights are related to noise variance for the
given data point
- Strong EC weighting
- Proportional EC weighting
- (compromise for better numerical properties)
12Regression process- Model performance
- Model fit is satisfactory and optimized in the
space of interest by means of WLS
- All variables are reliable
13Regression process- Model performance
- Predicted EC values versus the true EC values in
both the log-transformed space and the direct EC
space - The residuals must ideally be without pattern.
14Application of the allocation function and
Single-name concentration
- Application of the allocation function at
facility level requires special treatment of
single-name concentration
- Calculate multiplier for single-name
concentration (SNC) for each obligor - Use allocation function on facility level
corrected by each obligors SNC multiplier
- Allocation model characteristics
- EC increases more than linear with LGD times
exposure.
15Allocation model characteristics - Rating
dependency
- EC for a B3 rated customer is 400 hundred times
higher than for an A1
- EC depends less on PD than EL does
16Allocation model characteristics - Geographic
concentration
- The dependency between geographic concentration
and EC
- Corporate customers are sensitive to geography
- Retail customers diversify the portfolio
independent of geography
17Allocation model characteristics - Maturity as
proxy for migration risk
- Migration loss counts for 22 of the total loss
- Maturity therefore contributes significantly to
explaining EC - The difference between the shortest possible
maturity (1 year) and the longest (5 years), is
45 percent with respect to EC
18Summary and Conclusion
- To reap the gain of a portfolio credit risk model
and use it actively for risk management, it is
essential that EC is allocated marginally to each
loan, customer, and business unit. - The allocation model described in this
presentation estimates the marginal loss at
facility level from the tail of the loss
distribution of a banks credit portfolio
generated by simulating a large number of
scenarios in a credit risk model. - The allocation process consists of two steps
- Allocating to large obligor level with ES_at_95 and
- Allocating to facility level with a regression
based allocation function, having PD, LGD, EaD,
M, and a country based concentration term as
drivers. - Questions and comments.
- How do other banks allocate capital?