What Pair News Sensitivity means (and what it does not mean)
Pair News Sensitivity describes how strongly and in what way a currency pair tends to react when relevant news is released. In practice, you assess it by measuring changes in price and/or other market behavior around an event window and comparing them to a baseline window.
This concept is descriptive: it summarizes a relationship in data. It is not a standalone trade signal, and it does not imply that the same reaction will occur again. Your assessment should separate stable mechanics (how you measure and align data) from variable conditions (market regime, liquidity, costs, and execution).
What data you need: inputs, provenance, and alignment
To assess Pair News Sensitivity, collect four groups of inputs.
- Event definitions (the “news” side) You need a list of news events that you will treat as relevant. For each event, record:
- Event type (e.g., macro release categories)
- Release date and scheduled time in a stated time zone
- Actual release time (if it differs from scheduled time)
- Release horizon or importance label (if your dataset provides it)
Stable measurement depends on consistent event definitions and consistent time handling.
- Price and volatility measurements (the “pair response” side) You need time-aligned measurements for the currency pair, such as:
- Mid-price or last traded price series at a defined frequency
- Returns over chosen intervals (for example, changes within a window)
- A volatility proxy (for example, realized volatility over a window)
State your method clearly: which price type, which sampling frequency, and how returns are computed.
- Market microstructure and cost proxies (to avoid mixing effects) News sensitivity can be distorted by transaction costs and liquidity changes. Include at least one of the following where available:
- Bid/ask spread series or an estimate
- Slippage or execution cost proxy (if using backtest-like analysis)
- Liquidity measures (such as depth or order-book indicators, if your data source has them)
Without cost-related inputs, you may incorrectly attribute a move to “news impact” when it is actually driven by widening spreads or thin trading.
- Context and controls (to reduce confounding) A reliable assessment documents conditions that may alter reactions:
- Market regime indicators you derive from your data (trend, volatility regime, or average spread state)
- Broader risk-off/risk-on context variables if you have consistent datasets
- Overlapping events within your lookback window (multiple releases can interact)
How the assessment works: a practical evaluation approach
A typical method is event-study style:
- Define a baseline window (before the release) and one or more event windows (during and after release).
- Compute response metrics such as average returns and average volatility change.
- Measure sensitivity by comparing event-window metrics to baseline metrics.
To make assumptions explicit, document:
- Your event window lengths (e.g., “from 5 minutes before to 30 minutes after”)
- Your return interval (e.g., 1-minute returns aggregated into a window)
- Whether you treat scheduled and actual times differently
Material limitation: the results depend heavily on window selection and sampling frequency. A pair may show a response only at very short horizons; at longer horizons the effect can wash out or blend with subsequent market dynamics.
Quality checks: proof of document and red flags
Even without live market data, you can validate that your pipeline is coherent. Use afvinkpunten such as:
- Evidence of document: confirm the dataset provides event timestamps and time zone metadata.
- Rode vlaggen: missing timestamps, inconsistent time zones, duplicate events, or events with implausible “actual release” times.
- Klaarcriterium: you can reproduce the same response metric from the same inputs using your documented formula.
Additional data-quality checks:
- Completeness: ensure each event has sufficient price observations for your full windows.
- Outlier handling: identify extreme prints or bad ticks that can inflate sensitivity.
- Survivorship bias: if you select only “currently common” pairs or instruments, your history may be incomplete.
Limitations and risks (what can fail)
At least one material failure mode is timing mismatch. If event times are incorrect, your measured sensitivity may reflect random noise instead of news impact.
Other key limitations:
- Historical relationships do not establish future results; sensitivity can change when liquidity, regulation, or market participation changes.