Reviewing expectations for FRTB standards and lessons across jurisdictions
Suresh Srinivasan, FRTB Americas Implementation Lead, HSBC
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How does the implementation of internal model approach (IMA) affect financial institutions?
Under the IMA, financial institutions are allowed to use their own internal models to calculate their market risk capital requirements. This approach allows for greater flexibility and may result in lower capital requirements compared to the standardized approach. However, to use the IMA, financial institutions need to have robust risk management systems and processes in place and must comply with stringent regulatory requirements.
The implementation of the IMA requires significant investment in technology and resources to ensure the accuracy and validity of the internal models used. This includes the development and maintenance of models, as well as the validation and testing of these models by independent internal and external auditors.
The use of Expected Shortfall (ES) as the risk measure under the Internal Models Approach (IMA) can be beneficial to banks, as it provides a more accurate and comprehensive measure of market risk than Value-at-Risk (VaR), which is used in Basel 2.5.
What are the benefits of implementing a P&L attribution test?
The P&L Attribution Test helps banks to identify any weaknesses in internal models for estimating market risk, and to make adjustments to improve their accuracy.
The P&L Attribution Test provides regulators with greater visibility into banks’ risk models and how they are being used to estimate market risk.
If banks are able to demonstrate the accuracy and reliability of their internal models through the P&L Attribution Test, they may be able to reduce their regulatory capital requirements.
How can institutions leverage technology to address changes?
Technology plays a critical role in the implementation of FRTB. Banks need to invest in technology to enable FRTB and also address related topics such as calculations, reporting, data management and data quality.
FRTB requires banks to use advanced models to calculate market risk and credit risk for their trading books. Banks are required to implement risk calculation engines that can handle complex models and scenarios and provide accurate and reliable risk measures. These engines need to be scalable and flexible to accommodate changes in the market and regulatory requirements.
Implement a comprehensive data management system that can provide accurate and timely data for risk calculations. This requires banks to implement robust data management technologies that can integrate with various data sources and provide data quality checks, data normalization, and data governance capabilities.
FRTB requires banks to produce a wide range of reports to comply with regulatory requirements. Banks need to implement reporting technologies that can produce accurate and timely reports for various stakeholders, including regulators, senior management, and traders.
How can financial institutions model non-modellable risk factors?
Modelling NMRFs can be challenging for financial institutions, as these risks are often unpredictable and difficult to quantify. Non-modellable risk factors (NMRFs) are risks that cannot be directly modeled using traditional statistical methods. Examples of NMRFs include geopolitical risks, natural disasters, and market shocks.
To work around NMRF, banks can use stress testing which involves subjecting the bank’s portfolio to extreme scenarios to assess the potential impact of NMRFs on operations. This approach can help banks to identify vulnerabilities in their portfolios and to develop risk mitigation strategies.
Banks can also use historical simulations. This involves simulating a range of historical scenarios to assess the potential impact on the portfolio. Historical simulation can be used to capture the impact of past NMRFs and can be a useful tool for assessing the potential impact of future NMRFs.
It’s worth noting that these techniques are generally used in conjunction with the DRC, as the DRC provides a capital charge for NMRFs that cannot be directly modelled or hedged. By using these techniques, financial institutions can gain a better understanding of the potential impact of NMRFs on their portfolio and manage these risks more effectively.