Direct answer
To assess Impact Levels, you need (1) a clear definition of what “Impact Level” means in your context, (2) the event or information inputs it is based on, (3) the provenance of those inputs, and (4) timeliness and quality checks that confirm the data matches the time and instrument you are evaluating. Because markets and providers can define and compute impact differently, you should also document assumptions and failure modes before making any comparison or calculation.
Mechanism and definition
Impact Levels generally refer to a structured way of expressing how strongly a specific scheduled or identifiable piece of economic or market-relevant information could affect price behavior. The mechanics are usually driven by measurable inputs, such as:
- Event identity and scope: what the event is (e.g., an economic release), which region or economy it belongs to, and which instruments or reference rates it is intended to relate to.
- Event timing: the official release date/time and the time zone. A common mistake is mixing local time, platform time, or provider time.
- Event attributes: the data points of the event itself (for example, reported value, previous value, and any consensus/forecast figure if your model uses expectations).
- Market context at the same time window: the baseline you compare against (for example, pre-release expectations, prior volatility regime, or typical movement ranges).
Stable mechanics versus variable conditions matters. The stable part is the data structure and the logic of how you map inputs to an “impact” concept. The variable part is everything that changes with conditions, such as market liquidity, transaction costs, and how execution affects realized outcomes.
Evidence and example
A practical checklist for the data you would gather, record, and independently verify can look like this:
- Definition document: write down the exact rules you are using to interpret Impact Levels (what scale, what inputs, what computation or mapping). Without this, two “Impact Levels” may not be comparable.
- Event source and provenance: record where each event input came from (official publisher, statistics office, central bank calendar, or another documented provider). Keep the version or release identifier when available.
- Timeliness checks: confirm that the event timestamp, your data timestamp, and the market data timestamp use consistent time zones. If you use a window (e.g., “minutes around release”), state the window boundaries explicitly.
- Quality checks on event values: verify that reported and reference numbers (such as prior or benchmark figures) are not missing, duplicated, or revised without notice.
- Instrument mapping: document which currency pair(s), rates, or related benchmarks correspond to the event in your framework, and ensure the mapping rules are consistent.
For an example without assuming real-time prices: if you are evaluating an event’s potential impact on a currency’s exchange rate movement, your inputs would include the event’s official release timestamp, the event’s released numbers, and a clearly defined baseline window for “before” and “after.” The calculation (even if it is a simple comparison metric) should state the exact assumptions about the comparison window and how you treat outliers.
Limitations and risks
Several limitations are material when assessing Impact Levels:
- Definitions may differ: one provider’s “impact” may combine different assumptions (forecasts vs. only reported values). Comparing levels across systems can fail if definitions don’t match.
- Timeliness errors: mismatched time zones, delayed updates, or revised event figures can distort results.
- Missing or inconsistent data: if forecasts, priors, or baseline windows are absent, the assessment may silently degrade.
- Failure mode from overfitting: historical relationships between event surprises and subsequent price behavior do not guarantee future behavior. Market structure, liquidity, and participant expectations change.
- Condition dependence: outcomes vary with market conditions and costs (spreads, slippage) and with legal or operational constraints that affect trading and reporting.
These risks mean you should treat Impact Levels as an analytical input, not as a certainty. Even well-defined computations can fail when assumptions do not hold.
Verification and next question
To verify that your Impact Levels assessment is accurate and self-contained, you should be able to answer:
- Can you point to the definition rules you used (scale, mapping, computation)? - Can you reproduce the inputs from the original event documentation and confirm timestamps?