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What is the market risk modeling process and how can it help banks?

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Market risk modeling is a vital tool for banks to measure and manage the potential losses they may face from changes in market factors, such as interest rates, exchange rates, stock prices, and commodity prices. By using various techniques and models, banks can quantify their exposure to market risk, assess the impact of different scenarios, and optimize their portfolio allocation and hedging strategies. In this article, you will learn about the market risk modeling process and how it can help banks improve their performance and resilience.

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Brian Lo

Former - Group Head of Market & Liquidity Risk in DBS Bank (PhD 1990); Founder and Director, N-Category Advisers

1 Identify sources of market risk

The first step in the market risk modeling process is to identify the sources of market risk that affect the bank's assets, liabilities, and off-balance sheet positions. These sources may include general market risk due to movements in market-wide factors, such as interest rates, exchange rates, or equity indices; specific market risk due to movements in factors that are specific to a particular instrument, issuer, or sector; and non-linear market risk due to non-linear or asymmetric relationships between market factors and the value of certain instruments.

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Brian Lo

Former - Group Head of Market & Liquidity Risk in DBS Bank (PhD 1990); Founder and Director, N-Category Advisers
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Key risk identification process is about discovery of risk factors affecting (economic) values on and off balance sheets. Prevailing management performance metrics are important to serve their purpose but not necessarily economic values at risk measurements e.g. Net Interest Income of Net Interest Margins.

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2 Collect and analyze data

The second step in the market risk modeling process is to collect and analyze data that reflects the bank's exposure to market risk. This data may include historical information on past behavior and trends of the market factors and the bank's positions, current values and characteristics of the market factors and the bank's positions, as well as forward-looking data on expected or projected values and characteristics of the market factors and the bank's positions. This data can be collected over different time horizons, such as daily, weekly, monthly, or yearly.

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Brian Lo

Former - Group Head of Market & Liquidity Risk in DBS Bank (PhD 1990); Founder and Director, N-Category Advisers
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Second step which is very fundamental is 'risk mapping' aka Risk Metrics terminology a few decades ago. That is to measure the risk exposures as a sensitivity to risk factors. This can be direct or indirect (through models). Data capture therefore has 2 parts: risk factors and history (including processed) and risk exposure (current or last quarter end for reporting and disclosure).

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3 Choose and apply models

The third step in the market risk modeling process is to select and utilize the appropriate models that can capture and quantify the bank's exposure to market risk. These models may include sensitivity analysis, which measures how the value of the bank's positions changes in response to a small change in one or more market factors. Value-at-risk (VaR) is a model that estimates the maximum amount of loss that the bank can expect to incur over a given period of time and at a given confidence level. Stress testing is a model that assesses the impact of extreme or adverse changes in one or more market factors or scenarios on the value of the bank's positions. Additionally, expected shortfall (ES) is a model that calculates the average amount of loss that the bank can expect to incur beyond the VaR level, based on the tail of the distribution of the market factors and their positions.

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Brian Lo

Former - Group Head of Market & Liquidity Risk in DBS Bank (PhD 1990); Founder and Director, N-Category Advisers
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It is an industry norm arising from capturing portfolio market risk of the trading book (note the accountants' definitions vs regulators definitions not exactly the same). Infrastructure must allow aggregation of risk exposures and then perform all the 'what - if' perturbation to give potential economic value impact to portfolio. The scenarios handling capability is key for stress testing, VaR (normal and stress) and ES (normal and stress) etc.

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4 Evaluate and validate results

The fourth step in the market risk modeling process is to evaluate and validate the results of the models and compare them with the bank's risk appetite, limits, and objectives. This may involve backtesting, which compares the actual outcomes of the market factors and the bank's positions with the predicted outcomes of the models over a period of time. Additionally, benchmarking can be used to compare results with other models, methods, or sources such as peer banks or industry standards. Finally, reporting can communicate the results of the models and their implications to stakeholders for decision making and oversight.

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Brian Lo

Former - Group Head of Market & Liquidity Risk in DBS Bank (PhD 1990); Founder and Director, N-Category Advisers
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You may select market risk models for internal use (that's called pillar 2 approach) that is fit for purpose ie commensurate with complexity (including non-linearity) of the products and exposures, especially when some products' simulated value has to be pricing/valuation model dependent. However the most relevant benchmark is the regulatory models specifications (called pillar 1). Market risk models has to be tracked for performance ie backtesting and reviewed regularly; alternative treatment of risk factors data and exposure models often are part of the review. Peer benchmark relatively easy to carry out from annual report or disclosure but key question is to understand relevance of comparisons.

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5 Monitor and update models

The fifth step in the market risk modeling process is to monitor and update the models and their inputs, parameters, and assumptions on a regular basis, to keep up with changes in the market conditions, the bank's positions, and the regulatory environment. This could involve data quality control to ensure accuracy of the data that feeds into the models, such as market prices, rates, or volatilities. Additionally, model performance review is necessary to evaluate the models and their outputs. Finally, model enhancement is important to improve the models and incorporate new data, information, or knowledge. All of these processes are essential for maintaining a successful market risk modeling process.

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6 How it can help banks

The market risk modeling process can be beneficial to banks in several ways, such as reducing losses, enhancing performance, increasing resilience, complying with regulations, and improving governance. Quantifying and managing their exposure to market risk can help banks reduce their potential losses and protect their capital and profitability. Banks can also optimize their portfolio allocation and hedging strategies to enhance their performance and return on investment. Furthermore, assessing the impact of different scenarios and stress testing their positions can increase their resilience and preparedness for market shocks and crises. Additionally, following the standards and guidelines of the regulators and supervisors can help banks comply with the regulations and avoid penalties or sanctions. Finally, reporting and communicating their market risk exposure and management to the stakeholders can improve their governance and transparency.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What is the market risk modeling process and how can it help banks?

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1 Identify sources of market risk

2 Collect and analyze data

3 Choose and apply models

4 Evaluate and validate results

5 Monitor and update models

6 How it can help banks

7 Here’s what else to consider

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