Annex - Observations and Feedback from the LFIR Test and Review

Annex Email: HKMA E-mail Alert of 30 March 2026 (05:00 p.m. HKT)

Document Information

Title: Annex - Observations and Feedback from the LFIR Test and Review

Type: Annex

URL: https://brdr.hkma.gov.hk/eng/doc-ldg/docId/20260327-3-EN

Email Received: 2026-03-30 19:07

Summary Created: 2026-03-30 14:02

English Summary (4707 chars)
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Management Summary
  • Purpose / Background: This document summarizes observations and feedback from the HKMA’s Liquidity Funding in Resolution (LFIR) test and review. It evaluates Authorized Institutions' (AIs) ability to project liquidity needs and execute funding strategies under "fast-moving" resolution scenarios.
  • One-line conclusion: AIs must transition from high-level, manual liquidity projections to granular, automated, and stress-tested capabilities that account for modern "digitally amplified" bank runs and cross-jurisdictional collateral readiness.
  • Key Changes:
  • Shift toward more aggressive, front-loaded deposit run-off assumptions (15–30%+ daily rates for uninsured deposits).
  • Requirement for longer-term liquidity forecasting (beyond 90 days) to cover post-stabilization restructuring.
  • Need for enhanced modeling of "second-order" impacts (e.g., FMI access, replacement costs, and margin calls).
  • Greater emphasis on operational readiness for collateral mobilization, specifically for non-standard/illiquid assets.
  • Expectation for near-real-time (T+1) data generation through automated architectures rather than manual processes.
  • Key Dates / Deadlines: Ongoing; AIs are expected to execute enhancement plans and align with LFIR-1 expectations as part of the formal resolution planning program.
  • Applicability / Impact scope: All AIs subject to resolution planning requirements, particularly those with material entities or complex cross-border structures.
  • Recommended management actions:
  • Conduct sensitivity analysis on persistent post-resolution deposit run-offs.
  • Invest in automated data extraction and collateral workflow systems to meet T+1 reporting goals.
  • Integrate Treasury, Risk, Legal, and Operations to standardize modeling of resolution-related costs and liquidity benefits.
  • Establish and test "end-to-end" collateral mobilization pathways with relevant central banks.
  • Develop visual dashboards and stylized balance sheets for rapid executive decision-making.
Detailed Summary

1) Document overview
The report provides findings from HKMA’s LFIR testing. Its purpose is to identify gaps in AIs’ readiness to maintain liquidity during a resolution event, focusing on scenario modeling, data granularity, and management action feasibility.

2) Main requirements

  • Scenario Design: Must align with HKMA’s preferred resolution strategy; scenarios must account for rapid, multi-week lead-ups followed by immediate resolution entry.
  • Liquidity Projections: Must identify key drivers (deposit runs, collateral calls) and maintain projections for at least 90 days post-entry, extending to multi-year horizons for restructuring phases.
  • Management Actions: Must be realistic regarding execution timing; exclude long-lead-time assets (e.g., property) in fast-moving scenarios.
  • Collateral Readiness: AIs must demonstrate operational capability to mobilize collateral, including testing with relevant central banks.

3) Key changes (vs previous requirements)

  • Refined Run-off Profiles: Moving away from generalized rates to segment-specific (insured vs. uninsured, operational vs. non-operational) assumptions.
  • Technological Expectations: A clear pivot from manual Excel-based reconciliation to automated, scalable data architectures capable of T+1 reporting.
  • Strategic Horizon: Expansion of focus from initial liquidity stabilization to long-term post-stabilization solvency/liquidity sustainability.

4) Important dates & transition
No specific regulatory deadline provided; however, AIs are in a continuous implementation phase. The HKMA Resolution Office will continue bilateral engagement and future testing.

5) Impact and risks

  • Operational: High burden for departments to automate collateral and liquidity reporting.
  • Financial: Risk of underestimating resolution costs (staff retention, FMI access, restructuring).
  • Compliance: Inaccurate modeling of "management actions" may lead to regulatory findings regarding the feasibility of the resolution plan.

6) Compliance action checklist

  • [ ] Review current deposit run-off models against recent (2023) "digitally amplified" run data.
  • [ ] Implement automated liquidity reporting dashboards for senior management.
  • [ ] Verify legal and operational readiness for collateral mobilization at non-HK branches/subsidiaries.
  • [ ] Standardize "resolution costs" library to ensure consistent cross-departmental inputs.

7) Appendices/attachments summary
(N/A: The document is a singular report; all sections are integrated into the main analysis above.)

中文摘要 (1848 chars)
快速切換摘要區塊
管理層摘要
  • 目的/背景 本文總結了認可機構(AIs)在參與「處置情境下流動性資訊申報」(LFIR)測試與審查後的觀察與反饋,旨在優化機構在金融危機或進入處置程序時的流動性評估能力。
  • 一句話結論 機構需針對「快速流動性流失」、「資產抵押變現能力」及「數據自動化」進行精確化建模與優化,以符合處置期間的流動性需求評估要求。
  • 關鍵變更
  1. 強調存款流失假設需參考數位化時代(如美國 2023 年案例)的「前置式」高流失率。
  2. 要求將預測期間延長至處置後的數年(涵蓋重組階段),而非僅關注初步穩定期。
  3. 對第二層流動性影響(如衍生品保證金追繳、評級下調後的資金成本)需建立更細緻的模型。
  4. 必須提升數據顆粒度與自動化水準,目標達成 T+1 的流動性指標產出。
  • 重要日期 / 截止日 由香港金管局(HKMA)透過雙邊溝通與個別機構協商執行進度。
  • 適用對象 / 影響範圍 參與處置規劃(Resolution Planning)之認可機構,特別是具備重要境外分支機構及複雜資產負債結構的機構。
  • 管理層建議行動
  1. 針對「非受保存款」進行壓力測試敏感度分析,以應對處置初期的急劇流失。
  2. 建立關鍵財務數據的視覺化儀表板,以利緊急決策。
  3. 提升非流動性資產的變現預測真實性(考慮處置折價與法規執行時間)。
  4. 強化抵押品管理流程,特別是針對非標準抵押品的法律檢視與操作流程自動化。
  5. 優化跨部門協作機制,確保Treasury、Risk、Legal 與 IT 在處置場景下的資料一致性。
詳細摘要

1) 文檔概述
本文件針對認可機構在處置情境下的流動性申報(LFIR)測試進行總結,指出機構在情境設定、現金流預測、管理行動及執行效能上的常見問題,並提供監管改進建議。

2) 主要要求

  • 情境設計 須與 HKMA 的首選處置策略對齊,並針對不同實體(Material entities)進行深入的分解分析。
  • 流動性預測 需包含存款流失、衍生品保證金補繳、支付結算系統準入限制及業務連續性營運成本。
  • 管理行動 評估資產出售、回購(Repo)及母行支持的執行可行性,特別是考慮到處置場景下的資產折價與執行時間差。
  • 數據與報表 須具備產出高顆粒度報表的能力,並能以視覺化圖表呈現處置進程中的流動性軌跡。

3) 關鍵變更(對比既有要求)

  • 存款假設 從過往中性假設轉向參考數位化提款特性,承認「前置式」高額提款(首 1-2 日率達 15-30%)的可能性。
  • 時間視野 將預測範疇從僅關注「處置前與初步穩定期」,延伸至「處置後數年的重組階段」。
  • 自動化要求 由過往手動數據彙整轉向要求更接近 T+1 的自動化與即時化產出目標。

4) 重要日期與過渡安排

  • 目前處於持續改進階段;HKMA 處置辦公室將進行雙邊諮詢,並隨機構能力提升進行後續測試。

5) 對機構的影響與風險

  • 營運風險 因過度依賴手動流程,可能導致危機時刻無法產出準確的流動性評估。
  • 財務風險 對資產變現流動性的過度樂觀評估可能導致流動性缺口誤判。
  • 合規風險 無法滿足處置期間的快速監管資料提交要求,可能引發監管進一步干預。

6) 合規動作清單(Checklist)

  • [ ] 執行敏感度分析: 測試非受保存款流失對持續營運的影響。
  • [ ] 建立視覺化工具: 建置處置期間現金流 trajectory 與資產負債平衡表看板。
  • [ ] 優化抵押品清單: 針對非標準抵押品執行法規檢視與操作測試。
  • [ ] 整合跨部門資源: 確保數據來源(Treasury/Risk/IT)在處置情境定義上的一致性。
  • [ ] 提升數據架構: 投資自動化 collaterals 工作流程,減少手動調整。

7) 附件/附錄摘要

  • 本文檔本身即為針對 LFIR 測試反饋的 Annex 文件,內容包含五大章節(A-E),詳述了情境設計、預測模型、管理行動與未來改進方向,已於上述摘要中涵蓋。