modelos de capital econ%f4mico - vincent sapin

7/28/2019 Modelos de Capital Econ%F4mico - Vincent Sapin

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Economic capital models : Buildingblocks, strenghts and weaknessesFebraban International Congress of riskSao Paulo - October 20th, 2011

Vincent Sapin

The views expressed in this presentation are those of the authors anddo not necessarily represent those of the NBB


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Presentation

Overview

Context

General principles

Risk types (Credit, ALM, Trading, Operational,Funding, Business, Pension, Project, Reputation,Strategic, Liquidity)

Aggregation and disaggregation

Model risk

Stress-testing

Available Financial Resources

Comparison with insurance models

Conclusions

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Presentation

Context

This presentation gives a summary of the main

choices that have to be made in order to obtaina useful estimation of the economic capital

needs

With focus on quantitative aspects

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Presentation

General Principles

Risk identification

preliminary and crucial step

issues: exhaustiveness (cf. infra)

consistency within a Group

Purpose(s) of the model(s) clearly stated beforehand (e.g. reporting, monitoring,

solvency), e.g. : Point-in-time : quickly adaptative for risk monitoring; consistency

between risk measurement and price of hedging

Through-the-Cycle : more stable for solvency

Bottom-of-the-cycle : sufficient capital during a downturn

development / modeling in function of those goals

in practice, not always the case and/or not formalized

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Presentation

General Principles

Central development of models improve consistencyand efficiency, but :

applicability of central models to local entities? quid if material activity/exposure at the local level but not

significant at the group level

granularity (e.g. equity indices)

Similar risks of the parent company and of the

daughters (or of different business lines) should beadded up in an integrated way (positions on same risk

factors are added up) or VaR aggregation with crude correlation estimates ?

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Presentation

General Principles

Choice of a risk measure for Ecap :

Generally VaR (Fair Value based) = the maximum

potential loss calculated over a certain time horizonwith a certain confidence level

Loss = decrease in value

Over a certain time horizon

E.g. 1 day, 10 days, 1 year

With a certain confidence level

E.g. 99%, 99.97%

Expected Shortfall (tail-VaR), scenario analysis orstress-tests ?

use of the current level of risks, of a historical

average or limit level ?6


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Presentation

General Principles

Time horizon :

default value :1 year sometimes lowered thanks to 'Management actions' - see

later

Generally immediate shock on the risk factors

corresponding to 1 year (freeze of positions): simple but incomplete picture

Confidence Level (CL), e.g. VaR 99.97%

generally linked to external rating how to interpret deviations between targeted and observed rating ?

transformation of CL sometimes needed (e.g. trading)

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General Principles

Calibration (see purpose of the model)

Point-in-Time vs Through-the-Cycle ... vs Stressed e.g. : use of historical data collected during a smooth period will

probably not deliver a 99.97 confidence interval

How to assess potential regime switch vs temporary event(impact on the future values of parameters, e.g. spreads) ?

Potential significant impact

Real-world for risk measurement (Risk-neutral for valuation) :

implementation issues: e.g. availability of parameters

consistency issues :e.g. based on different historical

series (length or time period)

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Presentation

General Principles

Management actions:

Examples :

Trading and ALM : shock on a lower horizon than 1 year Credit exposure management (lowers exposure at

default)

Strong justification needed to avoid riskunderestimation (credible stop loss limits, evidencefrom the past, ...), and sometimes lacking

Keep in mind that they must be valid in the chosen high

confidence interval e.g. : are stop-loss limits realistic during a market crash ?

Only based on the past

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Presentation

General Principles

Estimates should be forward-looking

Going-concern (or liquidation) ?

if going-concern : Is the remaining capital after alarge loss sufficient to continue activities ?

Buffer for usual variation in activities

Buffer for cyclical effects (especially with PIT

measures)

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Presentation

Credit Risk

Risk types to be covered:

default, migration and spread risks (as Ecap is Fairvalue based)

Models (default and migration) usually based on Merton's theory (e.g

CreditMetrics) with a number of factors (e.g. :

sector/country/size) Importance of

granularity of inputs (e.g. transition matrices,

spread curves) the calibration: cf. general principles

the risk types covered, in particular for some

portfolios (e.g. sovereign)11


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Presentation

Credit Risk

Advantages :

only one figure summarizing the entire credit risk ofdifferent portfolios

sensitive to concentration/correlation in theportfolio

stability of the methodologies

Issues with this type of models: numerous strong assumptions (e.g. Normal

distribution of asset returns, no tail dependencies)

calibration: time period used ('normalcircumstances) ?

Non-modeled risks (e.g. Spread) sufficiency of level of add-ons for non-modeled parts

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Credit Risk

Issues with this type of models (cond) :

Profit generally not modeled (non exhaustive cash

flows projection) are these profits integrated in AFR (through expected

profits) ?

approximations for some products

retail (e.g. mapping to an equity index?)

structured products (e.g. waterfall structure, transitionmatrices...)

high confidence interval (how to backtest ?) Intraday credit risk coming from settlement

activities

=> importance of stress testing/scenario analysis13


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Presentation

ALM

Requires an Economic Scenario Generator, to

simulate all material risk factors (IR, FX, EQ,

RE, volatility risk ...) in an (ideally) integratedway

Full revaluation per risk factor (non linearities)

Implicit options (mortgage, sight deposits,

saving accounts, ...) must be taken into

account

Requires a risk measure based on Fair value

changes (ernings approach creates

aggregation challenges)

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Presentation

ALM

Issues :

Degrees of freedom in interest rates dynamic principal component; 1-factor interest rate models;

Volatility and correlation estimation : choice ofhistorical period

Short term - e.g. 1 month - historical volatility

scaled with square root of time

Implied volatility risk missing

Normality hypothesis of risk factors

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Presentation

ALM

Issues (cond) :

Linearity of products revaluation

Loss aggregation :

Var-Covar based method (silo - no integration) withcorrelations estimated from the underlying risk factors

(hybrid method) Simple dependency models (no tail dependence)

Management actions - sometimes used - butdifficult to prove

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Trading Risk

Based on well known VaR framework used for internalmanagement or regulatory purposes

Transformations to respect ECAP general principles,e.g.

holding period : 1 or 10 days => 1 year

Confidence intervaI: 99% => 99.97%

Sometimes management actions/stop loss included,considering possible hedging or liquidation of trading

operations

Sometimes based on the VaR limit instead of current(average) VaR

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Presentation

Trading Risk

Issues management actions: enforceable? at what cost?

human action?

holding period and confidence interval of Ecap farfrom daily management

assumptions for the scaling, e.g. independence,

Normal distribution illiquidity in the market, e.g. bid-ask spread

volatility

intra-day positions

default/credit migration/spread risks

coverage of model risks : from pricers to VaR ?

length of historical period

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Operational risk

Two approaches, in function of Pillar 1 choice :

internal model (AMA) or not (BIA / TSA)

AMA banks : recalibration of the 99.9 % VaR(higher quantile, but also sometimes with more

severe extreme scenarios)

Non-AMA banks : use of crude estimates

BIA / TSA with changes (other historic period, floorfor low income activities, higher quantile)

First attempt of scenarios - based on RSA -sometimes used for benchmarking

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Operational risk

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Operational risk

Advantages of internal models :

improvement in the management of operational

risk: risk committee

loss data base

first quantifications

much better cartography of operational risks (entity andbusiness level)

=> virtuous circle (higher awareness)

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Presentation

Operational risk

Issues for AMA banks:

large uncertainty on the results

Low frequency / High impact events difficult to

estimate and sometimes considered by banks asimpossible

modeling techniques still evolving and under

discussions (LDA or scenario ?) rescaling of the quantile with flawed hypothesis

(log-Normal distribution)

Additional issues for non - AMA banks : Insufficient risk sensitiveness of some simplistic

methods based on the past => increase ofscenarios quality

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Presentation

Funding cost risk

Risk that the refinancing cost of the company

increases

New in Ecap models

Generally based on scenario analysis

evolution of funding requirements (balance-sheet

items)

shock on funding cost

Numerous assumptions, e.g. costs, volumes,

funding sources...

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Presentation

Funding cost risk

Issues:

Type of instrument used (short or long term):

definition of the scenario, e.g. idiosyncratic, systemic... Importance of recourse to ECB, FED ...

(remaining) availability of financing on the market

Impact on new production pricing

Choice of the shock on spread (historical vs expert...)

Volume :

liquidity needs in the trading room (margin calls) difficultto anticipate

interactions between different variables / second roundeffect (e.g. behavioural)

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Presentation

Business risk

Less advanced than operational risk

But should also be capitalized

Two types of approaches :

historical volatility

based on historical P&L series

cleaning : ex-ante (P&L) or ex-post (VaR)

volatility around an average (historical or budgeted)

Consultants benchmark

Sometimes use of scenarios to benchmarkresults

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Presentation

Business risk

Issues:

Short historical series

definitional problems : border with others risks not always clear, e.g. ALM and

strategic risks : E.g. Commercial margin risk Business or ALM risks ?

exhaustive trash ?

Limited risk sensitiveness => try to identifyexplanatory variables

no use test => pure capital add-on

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Pension and project risks

Pension risks :

Potentially important (defined benefits)

Common risk factors with market risk (integratedmeasurement ?)

Project risks : Investments for an important project are made, but the

project fails

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Presentation

Not (yet) capitalized risks

All material risks to be covered by capital

Exceptions to be justified with a validreasoning in case of stressed events

Reputation risk :

Often not capitalized as theoretically covered inother risk type (operational, liquidity ... risks)

But incorporation of reputational losses in other

risk module still to be proven ...

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Not (yet) capitalized risks

Strategic risk:

Often not capitalized

Possibly covered in Business risk ?

Liquidity risk:

'Bank run' risk Capital not the first line of defense

Often not capitalized until now

Planned new acquisition:

included in capital planning ?

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Aggregation

Inter-risk aggregation: market, credit,

operational ...

Integrated models vs Silo-type models Silo approach (Var - CovarVar) :

division of risks according to the company's

activities: credit, market, operational, equity ...

set up of a correlation matrix based:

on expert opinions

on external publications on proxies for risks under consideration (e.g. based on

historical data)

on a mix of the three

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Presentation

Aggregation

Advantages of silo approach

single risk measure for the sum of risks and also

per risk type simple to implement

computation time

transparent (measure per risk type) Issues

applicability to local entities (distinction Group vs

Local correlations ?) tail dependencies

importance of calibration (cf. slide on general principles)

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Aggregation

Issues (cont'd) silo approach :

How to estimate inter-risk loss correlations (representativeness ofproxies) ?

link to confidence interval:

Isn't the Normal distribution used for the sum of the risks, when it's notthe case at individual risk level ? quid fat tails ?

average or stressed correlations (correlation increase in case of

stress)? under estimation of VaR ?

How to separate credit and market risks of structured products ?

second round effects, contagion (=> scenario?)

==> evolution towards an integrated approach (integrated simulationof all risk factors)

reliability of such estimates/ model risk =>implementation of add-ons ?

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Disaggregation

Allocation methods:

limited theoretical basis

proportionally to local entities (no incentive to locallydiversify)

change in the confidence interval at local level(compared to Group level)

Benefits of diversification (within a cross-border Group)

related to the transferability/solidarity assumption

this assumption should be proven/tested the retained method should be in line with it

concentration at local level should be taken into account

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Model Risk

In addition to the usual mitigation measures(statistical testing, independent validation,

stress-testing, use test, ...) :

Economic capital must be calculated for theresidual model risks (to cover for the un-avoideduncertainties)

Issues : clarification of the link with prudence margin in

lower level parameters

granularity in the application to foster continuousmodel improvement

better justification of the capital buffer =>

standardization of the process

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Stress Testing

Usually developed at the end of the process

Complement to the Ecap model => challenge

of assumptions and benchmarking of Ecapresults

Stressed VaR vs stressed loss (working

outside of the Ecap model !)

Historic and forward-looking scenarios +

reverse stress-tests

How to integrate stress test results in Ecap

estimation ?

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Stress Testing

Issues Still work in progress

Macro-economic environment difficult to forecast

and to translate into specific assumptions for inputparameters of specific models

Severity of the scenario (what is an adequate

stress scenario according to banks andsupervisors ? Has it to be realistic ?)

Generic economic scenario vs specific to thecompany concerned (e.g. business model)

Global stress-test still in development

Excuse not to improve the models ?

Consistency of hypotheses in retained scenarios

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Input to assess economic solvency and to define riskappetite

Generally based in a 1st step on the current regulatory definition of

capital

then modified to take into account specificities of the

economic capital approach=> consistency AFR vs ECAP to be checked as different

environments at stake

Reverse approach more suitable

prior definition of criteria for inclusion in AFR (i.e.permanence, loss absorption, availability)

better integration with the ECAP framework

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Examples of issues :

Transferability of own funds within a Group Estimated net earnings

Unrealized capital gains / losses

Coverage of EL by provision (consistency Ecap -AFR)

Intangible assets

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Conclusions

Modeling of 'traditional' risks more mature

Still a work in progress, given the huge methodological

challenges and data and IT issues Advantages :

Generalization of risk identification and measurement to allrisks (also less 'traditional')

Additional relevant - coherent and standardized - informationfor (Risk) Management (higher risk sensitiveness)

All risks summarized in one number

More resources devoted to Risk Management function

More objective measures, facilitating risk / return analysis, ...

Improvement of data quality

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Conclusions

Issues:

use of simplified models with constant parameters

(e.g. correlations) Silo approach

Willingness to correct simplistic assumptions that

leads to capital underestimation

Final Ecap number results from a multitude ofmodels feeding each others (from pricers to inter-risk correlation) : are models errors vanishing or

magnifying ?

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Conclusions

Issues:

importance of modeling limitation / choice

awareness and communication (e.g. assumptions,justification of calibration choices...)

with a high confidence level, crisis must be in thepossible scenarios and not only in the stress

testing large uncertainty in the results due to the high

confidence level and time horizon

Difficulties to perform conclusive backtests importance of different risks measures, i.e. models

complemented with benchmarks, stress tests,nominal limits ...

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O f f hi i


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Presentation

Out of scope of this presentation

Pillar 1 models

Data and process Documentation

Internal validation of models

Model management framework

Governance

Use test

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modelos de capital econ%f4mico - vincent sapin

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