Lessons drawn on the Banking Turmoil in the US and Europe

Our Ref.: B1/15C

15 December 2023

The Chief Executive
All Authorized Institutions
Dear Sir / Madam,
Lessons drawn on the Banking Turmoil in the US and Europe I am writing to share with the industry those risk management areas that the Hong Kong Monetary Authority (HKMA) expects authorized institutions (AIs) to pay particular attention to given the lessons that could be learned from the banking turmoil in March 2023. The March banking turmoil is the most significant system-wide banking stress since the Great Financial Crisis of 2008 in terms of scale and scope. Although the Hong Kong banking sector remained strong and resilient throughout that incident, the HKMA has undertaken a review to identify areas that may warrant additional supervisory and risk management attention, especially in view of the changing operating environment for banks. Our review has reaffirmed that robust risk governance remains the backbone of safe and sound banking. It has also highlighted that AIs should step up their management of interest rate risk and liquidity risk. This letter serves to assist AIs by setting out the HKMA’s supervisory expectations for these areas. For the avoidance of doubt, the relevant requirements are not new and are already stipulated in various existing HKMA guidance to the industry. Where necessary, AIs should make reference to the relevant Supervisory Policy Manual modules and the associated circulars. Risk governance

1. Risk governance framework - The board of directors and senior
   management bear the ultimate responsibility for an AI’s safety and

soundness, and for ensuring that the primacy of risk governance is effectively communicated across the institution, including through a strong “tone from the top”. In particular, the March banking turmoil has reinforced the importance for an AI to:

• put in place a risk governance framework that enables it to remain
  agile and responsive to both internal and external changes, as well as adjust its risk management approach to cope with evolving circumstances;
• take remedial actions without delay when it observes any inadequacies
  in its risk management framework or practices, with priority given to governance and cultural deficiencies in particular;
• actively review the implications of incidents or risk management
  issues experienced by its peers, and take timely actions to address the key learnings; and
• respond proactively to supervisory observations issued by the HKMA,
  and ensure any follow-up actions are duly completed within the agreed timeframes. Interest rate risk management

2. Management of interest rate risk in the banking book (IRRBB) – The
   HKMA has already fully implemented the IRRBB standards promulgated by the Basel Committee on Banking Supervision, and notes that AIs’ exposures to IRRBB, or interest rate risk more broadly, are generally not high. Notwithstanding these, the HKMA sees merits for AIs to manage IRRBB proactively and enhance their ability to respond to rapidly changing market conditions. For instance, an AI can:

• make effective use of early warning triggers to inform the
  management on when mitigating measures should be taken to reduce the institution’s IRRBB before the supervisory outlier threshold is reached (i.e. IRRBB causing an AI’s economic value of equity to decline by more than 15% of its Tier 1 capital under a set of standard supervisory scenarios of interest rate shocks);

• adopt interest rate shock scenarios in addition to the standard
  supervisory scenarios having regard to its IRRBB profile and market developments; and

• build up its ability to reposition the balance sheet and adjust its IRRBB
  profile in an expeditious manner.

3. Behavioural models for measuring IRRBB – Many AIs measure IRRBB
   by adopting behavioural models to capture how customers respond to interest rate changes. These AIs should ensure that their models are conceptually sound and prudently calibrated, given that inappropriate inputs especially behavioural assumptions can result in inaccurate estimates of their exposures to IRRBB. Accordingly, these AIs should establish a robust framework for managing the risks associated with the use of behavioural models, including to segment customers with sufficient granularity for behavioural analysis, regularly review key model assumptions, and establish model performance indicators to detect changes in customer behaviour that may affect the accuracy of the IRRBB measurements in a timely manner.
4. Investment in debt securities – AIs’ accounting classification of their debt
   securities investment determines how fair value changes are recognised in the calculation of their capital adequacy ratios (CARs). Specifically, fair value changes are timely reflected and fully captured in AIs’ CARs for debt securities classified as fair value through profit and loss (FVPL) or fair value through other comprehensive income (FVOCI). This may provide a more realistic representation of AIs’ capital strength, but the volatility of their CARs may increase as a result. The opposite applies to debt securities which are measured at amortised cost and are intended to be held to maturity (HTM). During times of heightened uncertainty, AIs with significant holdings of HTM debt securities may attract market scrutiny around questions such as how the AIs may be financially impacted by the unrealised fair value losses associated with such holdings. AIs should therefore be alert to how their level of transparency and disclosures may affect market sentiment. Currently, financial reporting standards already require AIs to disclose the fair value of their HTM debt securities when it is not reasonably close to the corresponding carrying

amount. In addition to this, AIs should closely monitor the unrealised losses and also seriously consider disclosing their CARs adjusted for these losses when they are significant. Furthermore, AIs should also incorporate the likelihood and potential impact of incurring losses from selling HTM debt securities into their internal processes including stress testing, capital adequacy assessment and capital target setting and monitoring. Liquidity risk management
5. Deposit concentration – AIs should carefully manage the risk of deposit
concentration, noting depositors with a similar profile are likely to act in a similar pattern during times of heightened uncertainty, and can exacerbate the speed and severity of bank runs if one occurs. To address this risk, AIs should perform sufficiently granular analyses of their deposit composition across various dimensions (e.g. by individual and group of related depositors, and by geographical location and economic sector of depositors), with a view to detecting any undue concentration and potential vulnerability to a particular risk driver. Based on the analyses, AIs should put in place proper controls to contain liquidity risk arising from deposit concentration, including setting appropriate concentration limits and taking account of concentration risk in other relevant processes (e.g. pricing of deposits and stress testing).

6. Contingency funding management – AIs should have in place policies and
   procedures for exercising all contingency funding options, including tapping the HKMA’s liquidity facilities. These policies and procedures should be regularly reviewed and tested, and to the extent possible, with real transactions to verify operational readiness. This will help ensure that AIs can swiftly access funding in times of liquidity stress. AIs should also be able to generate key liquidity information (e.g. deposit movements and cash flow positions) at high frequency and with short notice in order to support their continual monitoring and assessment of funding needs.
7. Digitalisation of banking services – The growing digitalisation of banking
   services is impacting how customers behave, and in turn, the speed with which liquidity risk materialises. Accordingly, AIs should assess the

potential changes in depositors’ behaviour arising from banking digitalisation under both business as usual and stressed scenarios, and develop capabilities to monitor and deal with volatilities in fund flows initiated electronically. AIs should also adopt adequate measures to monitor and mitigate liquidity risk emerging from these volatilities, such as monitoring payment flows both during and outside normal business hours.
8. Social media monitoring – Social media can rapidly influence market
sentiment and confidence surrounding an institution. If improperly managed, the reputation risk facing an AI could be significant, and even trigger severe liquidity outflows. An AI is therefore expected to put in place a framework to detect for and address emerging concerns or negative sentiment surrounding the AI on social media in a timely manner. The framework should clearly specify the types of social media covered, the scope of keywords that will be monitored as well as the frequency of monitoring. These factors should be reviewed regularly and updated as circumstances change. Furthermore, AIs should establish an effective mechanism for escalating material negative publicity to management for attention, such that more time is available for management to evaluate and handle the situation as necessary. It is also desirable for AIs to develop potential responses to various scenarios that they may encounter amid the rising impact of social media.
9. Composition of High Quality Liquid Assets (HQLA) – The stock of HQLA
(or liquefiable assets for AIs required to calculate the Liquidity Maintenance Ratio) is intended to defend against the potential onset of liquidity stress. This suggests that AIs’ holdings of HTM debt securities as HQLA may need to be monetised by way of outright sale or repurchase agreement for liquidity purpose before their contractual maturity. While the existing liquidity rules and accounting standards do not preclude designation of HTM debt securities as HQLA, AIs should be fully aware of the respective features of HQLA and HTM debt securities, and take into account the risk implications brought about by substantial holdings of HTM debt securities in their HQLA portfolios (e.g. potential financial impact arising from monetisation of HTM securities that may exacerbate a liquidity stress situation). As a safeguard, AIs should limit the

proportion of HQLA held in the form of HTM debt securities, with a view to avoiding excessive concentration in them. AIs should review their risk governance framework and relevant risk management systems and, where necessary, take steps to address any potential weaknesses when benchmarked against the above supervisory expectations. Meanwhile, to facilitate the timely monitoring of AIs’ positions in the above-mentioned risk areas, the HKMA is reviewing the submission deadlines of relevant returns and surveys and will consult the industry on any proposed revisions in due course. Should your institution have any questions about this circular, please contact Mr Argus Leung on 2878 1626 or Mr Michael Tse on 2878 1928. Yours faithfully, Raymond Chan Executive Director (Banking Supervision)