Well, today I am not going to discuss what is Risk or what are the types of Risk as is but let’s have a look at how Risk can be defined through Model Development and the types of Risk.
Models are used by many organizations, and it is on the rise, why? Because they help predict the future better including some of the black swan cases (the key here is “some” :))
A model is created by defining inputs and their assumptions and then a particular best-fit methodology is chosen including the expert judgment to get the result.
Input+Assumptions->Methodology+ Expert Judgement-> Output
Let’s discuss some of the Risk Modelling domains:
- Market Risk
- Credit Risk
- Liquidity Risk
Market Risk:
Market Risk arises with changes in interest rates and the positions of the securities in the market. With volatility in the market, the valuation and NII of the portfolio changes. Certain Shock Scenarios are therefore assessed on a regular basis in a bank to assess the impact on the balance sheet (EVE) and income statement (NII).
Interest Rate Risk:
The volatility in the interest rate leads to large exposures in the bank and it is required to manage the interest rate risk in the banking book. The banks should identify, measure, monitor, and control of IRRBB as well as its supervision.
Credit Risk:
Credit risk accounts for the bulk of most banks’ risk-taking activities and hence their regulatory capital requirements.
There are two approaches for credit risk, ECL estimation:
- Standardised Approach
- Advanced Internal ratings-based approach
There are various losses a firm/bank can face:
- Expected Loss
- Unexpected Loss
- Exceptional Stress Losses
How is Expected Credit Loss is calculated?
Expected Credit Loss= PD*LGD*EAD
PD: Probability Of Default
LGD: Loss Given Default
The proportion of the total exposure that cannot be recovered by the lender once a default has occurred.
EAD: Exposure at default
The total value that a bank is exposed to when a borrower defaults.
Important Term:
LTV: Loan To Value
Percentage of the purchase amount funded by the bank is LTV. E.g., only 80% of the flat/home value is funded by the bank than that 80% is LTV.
E.g.; 80% of $10,000,000 = $8000000
PD: e.g. 25% given that historically one in every four people defaults.
EAD:
Now say, only $1800000 amount is not paid by the customer:
EAD=$1800000
Then, LGD= 1800000/8000000= 22.5%
Now,
Expected Loss= 25%*22.5%*$1800000
=$101250
Model Risk:
Risk arises while using the model, depending on the Risk Rank of the model it has varying adverse impact based on the incorrect/misused model output. Consequences:
- Financial Loss
- Poor Business and Strategic Decision making
- Impact on Bank’s Reputation
Managing Model Risk:
- Identify the sources of risk and quantify the magnitude
- Model should be assessed individually as well as with its dependencies (Upstream models).
- E.g., assess the impact if the upstream model has errors. Reliance on common assumptions, data, or methodologies; and any other factors could adversely affect several models and their outputs at the same time.
This can be managed by:
Model Validation team/Strong Model Governance which can identify and challenge model limitations and assumptions and produce appropriate changes.
That’s all Folks!
Interested in:
- SAS Base and Advanced Course
- Market Risk
- Credit Risk
Reach out on: [email protected]
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Best,
#YNWA