HKMA updates HKTR technical specifications: UPI, validation rules and 2026 deadlines
Key dates for firms
- UAT effective date: 14 September 2026
- Production effective date: 21 November 2026
HKMA has updated its HKTR technical specifications for OTC derivatives reporting. These include introducing mandatory UPI validation, enhanced conditional field requirements and 25 additional validation rules.
The updates come after the Hong Kong regulator identified recurring ‘common reporting errors and data quality issues’ in live reporting.
On the surface these updates may look like technical housekeeping but they are another clear signal that regulators in Asia-Pacific are increasing their focus on data quality following last year’s rewrites.
What has changed in the HKMA HKTR technical specifications?
These changes fall into three key categories:
- Mandatory reporting of UPI data – the Unique Product Identifier (UPI) field has been updated from optional to mandatory for all asset classes. This was already a mandatory required field as specified in the HKMA Gazette, but to reinforce data quality, this is now technically mandated at system level.
- Enhanced data field requirements – 22 reporting fields have been upgraded from optional to conditional to improve data quality across all asset classes. These updates cover the following reporting elements:
- Strike Price
- Price
- Fixed/Floating Rate fields
- Exchange Rate
- Notional Quantity
- Option Type/Style
3. Additional validation rules
To support the enhanced data field requirements, the HKTR has developed a further 25 validation rules to strengthen submission quality. As outlined in the official letter published to the HKTR website on 19 May, these changes will take effect from 21 November 2026.
What does this mean for firms?
HKMA has acted swiftly in addressing the reporting issues identified, indicating that it is moving from rewrite implementation to data scrutiny and supervision. This aligns with the approach taken by other regulators in the region, with similar data quality exercises being undertaken by ASIC and MAS.
With greater supervisory scrutiny comes greater consequence for error. Remediation programmes are well known to be lengthy and expensive. Ensuring data quality from the outset has therefore become as much about managing risk and controlling costs as it has about meeting regulatory obligations.
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