Last Updated
Data Source
Saudi Exchange
Method
Nelson-Siegel

Methodology

Data Collection: Taken from Saudi Exchange (Tadawul) government bond and sukuk transactions at 5:00 PM KSA.

Data Filtering: Only confirmed trades are considered for bootstrapping and curve-fitting.

Historical Aggregation: When fewer than 3 bonds trade on a given day, we aggregate trades from up to 7 prior days (unique ISINs only) to ensure adequate data points for curve fitting.

Curve Fitting: Nelson-Siegel parametric model with volume-weighted optimization. Bonds are weighted by relative trading volume; SAMA reverse repo rate provides a short-end anchor.

Temporal Smoothing: Alpha (confidence score) ranges from 0.0 to 1.0 based on: bond count (20%), volume (50%), maturity coverage (15%), fit quality (10%), and same-day trading proportion (5%). Smoothed curve = α × today’s curve + (1-α) × previous curve.

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Appropriate Use

Follow judgement:

  • IAS 19 discount rate determination for employee benefit obligations
  • IFRS compliance and financial reporting
  • Actuarial valuations requiring SAR-denominated discount rates
  • Benchmarking and economic analysis

Not Appropriate For

Any other purpose not listed above, including:

  • Trading, investment decisions, or portfolio management
  • Pricing derivatives or structured products
  • High-frequency or intraday applications
  • Regulatory capital calculations requiring approved vendor data

Disclaimers

Intended users: This data is provided exclusively for Numerica clients and their professional advisors working on IFRS/IAS 19 compliance matters.

No warranty: No representations or warranties regarding accuracy, completeness, or timeliness. Yield curves are fitted models; thin trading may affect reliability.

Professional judgment required: Users must exercise independent professional judgment and verify data before use in financial reporting. Ultimate responsibility for discount rate selection rests with the reporting entity and their auditors.

No liability: Numerica Consulting shall not be liable for losses, damages, or adverse consequences arising from use of this data.

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