This article discusses how stress testing can be used to identify and mitigate credit risk, and it includes a detailed explanation of different types and approaches involved in stress testing.
What is Stress Testing?
Stress testing refers to a technique used by banks, financial institutions, and regulatory authorities to assess the potential impact of adverse economic scenarios on the credit quality of a portfolio of loans, investments, or other credit exposures. The purpose of stress testing is to evaluate how well a financial institution can withstand unexpected and severe economic conditions without suffering excessive losses or capital depletion.
In simple words, stress testing is like putting a financial institution (such as a bank) through a really tough test to see if it can handle difficult future scenarios, such as a macroeconomic crisis, emerging climate risks, such as floods, famines, and hurricanes.
Types of Stress Testing
- Scenario Analysis : Banks use scenario analysis as a tool to imagine and evaluate diverse potential future scenarios. By simulating these hypothetical situations, banks can measure how each scenario might influence their financial well-being. This approach helps in proactive risk assessment.
- Reverse Stress Testing : In reverse stress testing, banks adopt a unique perspective. Banks start with a predetermined adverse outcome and work backward to identify the factors that could trigger such a situation. This method assists banks in pinpointing vulnerabilities within their operations and risk management practices. By understanding the root causes of potential crises, banks can take corrective actions to strengthen their overall resilience.
- Sensitivity Analysis : Sensitivity analysis involves evaluating a variety of influencing factors through testing to measure their impact on the bank's performance. By assessing how changes in these variables affect their financial outcomes, banks gain insights into their exposure to different types of risks.
Types of Risks for Stress Testing
There are four types of risks mainly used in stress testing - credit risk, market risk, sovereign risk, funding cost and net interest income risk, climate risk.
Credit Risk
It includes the following macroeconomic scenarios for stress testing.
- GDP
- Unemployment rate
- Inflation
- House prices
Market Risk
It involves the potential losses that could occur due to changes in market prices such as :
- Interest Rates
- Credit Spreads
- Exchange Rates
- Equities
- Commodities
- Counterparty Credit risk
Sovereign risk
It is related to the risk associated with investments in government bonds issued by different countries. In the context of sovereign risk, a "haircut" refers to a reduction in the value of financial instruments, such as government bonds or securities, that an investor holds. When conducting stress tests, financial institutions and regulatory bodies often apply haircuts to government bonds to simulate scenarios of economic distress or sovereign default.
Funding Cost and Net Interest Income Risk
In stress testing, funding cost risk is assessed by considering scenarios where funding costs rise significantly due to market disruptions, changes in interest rates, or shifts in investor sentiment. Higher funding costs can lead to reduced profitability, liquidity challenges, and strained capital adequacy.
Net interest income (NII) risk involves the potential impact of changes in interest rates on a financial institution's net interest income, which is the difference between the interest earned on assets (loans, investments) and the interest paid on liabilities (deposits, borrowings).
Climate Risk
Climate risk refers to the potential adverse impacts that changes in climate patterns and extreme weather events could have on the stability and resilience of financial institutions, particularly banks. In simple words, it evaluates how climate-related events such as floods, famines, hurricanes can affect the financial health and solvency of banks.
There are two types of climate risks involved for Stress Testing.
- Transition Climate Risk: It is the risk that financial institutions will suffer losses as a result of the transition to a low-carbon economy. This includes shifts in technology, changes in regulations, and the adoption of sustainable practices. For banks, this involves evaluating the potential impact on their portfolios as industries shift towards more sustainable practices.
- Physical Climate Risk: It is the risk that financial institutions will suffer losses as a result of extreme weather events or other climate-related disasters (e.g., hurricanes, famines, floods). For banks, this involves assessing the potential impact of these events on their assets, liabilities, and overall financial stability.
Stress Testing Scenarios
Here is the list of stress testing scenarios for credit risk.
- 10% drop in the S&P 500 index.
- 4% increase in the unemployment rate.
- 3% increase in the inflation rate.
- 100 basis point increase in the interest rate.
- 5% decline in real estate prices.
- 20% reduction in global trade volume.
- 15% depreciation of the domestic currency.
- Major cyberattack affecting multiple industries.
- Sudden exit of a key borrower with a large exposure.
- Trade sanctions imposed on a key trading partner.
- 50% reduction in tourism due to travel restrictions.
- Large-scale supply chain disruption causing business closures.
- 20% decrease in corporate bond prices affecting fixed-income portfolios.
- Major social unrest leading to business shutdowns.
Orderly Scenario : It assumes that environmental policies related to climate change will be put in place early on and will slowly get stricter over time. It is based on the Network for Greening the Financial System (NGFS) net zero 2050 scenario which limits global warming to 1.5°C by achieving net zero carbon emissions around 2050. Both physical and transition risks are low due to the smooth transition and reduced global warming.
Disorderly Scenario : It assumes delay of introduction of new climate policy until 2030. Strong actions are needed to limit global warming below 2°C, resulting in higher transition risks and increased physical risks, including extreme weather events.
Hot House World Scenario : It assumes no new climate policies. Global emissions grow until 2080, causing about 3°C warming. While transition risks are minimal, the economy suffers from extreme physical risks.
Different Approaches in Stress Testing
There are two common stress testing approaches which are bottom-up and top-down.
Bottom-Up Stress Test : It is a stress test performed by banks. Banks use their own models and means to perform the stress test. It is based on the banks' own customer data and external granular credit bureau data. It takes into account shocks at individual customer levels, and the outcomes are combined to provide an overall perspective of how these shocks influence the capital levels of the firm.
Top-Down Stress Test : It is a stress test performed by central banks (regulatory authorities). It is based on aggregated institution data. It involves consistent methodology and assumptions developed by the regulatory authority.
Hybrid Stress Test : It is a stress test that uses a combination of top-down and bottom-up approach. With this approach, the regulatory authorities give banks a detailed set of rules on the results that their own models produce.
Statistical Techniques for Stress Testing
In stress testing, you need to forecast the total exposure of a portfolio, given the projections of the macroeconomic variables such as GDP, Unemployment Rate, Inflation Rate and House Price Index. Various statistical techniques can be used to perform stress testing. Here are some commonly used techniques:
- Time Series Models: Historical credit and economic data can be analyzed using time series methods such as autoregressive integrated moving average (ARIMA) or GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models to capture the potential impact of economic fluctuations on credit portfolios.
- Random Forest Model: The main benefit of random forest is that it can handle non-linear relationships between dependent and independent variables. It can also perform out-of-sample prediction.
- Copula Models: Copula models can be used to capture dependencies between different variables, such as credit defaults and macroeconomic factors, in stress testing. This helps in assessing the impact of correlated risks.
- Bayesian Probabilistic Model: Bayes' theorem is useful when making predictions conditional on extreme events, as required under stress tests.
- Monte Carlo Simulation: This technique involves generating a large number of random scenarios based on probability distributions for key economic variables (e.g., GDP, interest rates). The credit portfolio is then simulated for each scenario to estimate potential losses and changes in credit metrics.
Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and HR.
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