Market‑Implied Inflation Expectations

A reproducible signal that summarises forward inflation expectations inferred from TIPS breakevens. Built to complement the Fed Inflation signal by focusing on market pricing rather than realised CPI.

Why: CPI shows history; TIPS breakevens reveal forward expectations. Sources: FRED (T5YIE, T10YIE).

Abstract

We construct a monthly signal using FRED’s T5YIE (5‑Year Forward Inflation Expectation Rate) and T10YIE (10‑Year Breakeven Inflation Rate). Each is transformed to YoY and 3‑month annualised changes, standardised via robust rolling z‑scores, and combined into a Market‑Implied Inflation Expectations (MIIE) composite. Regimes map to RISING, STABLE, or FALLING expectations. A comparison hook aligns MIIE with the Fed Inflation and CPI vs PPI signals.

1. Data (FRED Identifiers)

T5YIE — 5‑Year Forward Inflation Expectation Rate (breakeven implied, % p.a.)
T10YIE — 10‑Year Breakeven Inflation Rate (UST10 − TIPS10, % p.a.)

Both series are resampled to month‑end with bounded forward‑fill (≤2 months) to accommodate publication lags. Titles and last‑observation dates are captured for documentation.

2. Data Quality, Cleaning & Validation

Recent availability check: log last observation date; flag stale (>62 days) inputs.
Bounded forward‑fill: up to 2 months max; else mark as missing_critical.
Minimum history: require ≥120 months coverage for robust transforms; otherwise adapt window (≥60 months) and emit a warning.
Outlier guard: winsorise 0.5–99.5th percentiles before z‑scoring (configurable).

A data_quality_flag field records staleness, ffill counts, and window adaptations.

3. Component Transforms

Given monthly series x_t in pct points:

YoY(x)_t = x_t - x_{t-12}

Ann\;3m(x)_t = 4 \times (x_t - x_{t-3})

Level(x)_t = x_t

Constructed components: t5yie_level, t5yie_yoy, t5yie_ann3m, t10yie_level, t10yie_yoy, t10yie_ann3m.

4. Standardisation (Rolling z‑scores)

Each component is scaled using a rolling window W=120 months. Robust z‑scores use median/MAD; otherwise mean/std.

z_t = \dfrac{x_t - \text{median}_{W}(x)}{1.4826\,\text{MAD}_{W}(x)} \quad \text{(robust)}

If insufficient history, use adaptive window (≥60m) and emit a warning.

5. Weighting & Composite Construction

Let z^k_t be the z‑score for component k. Default weights emphasise levels (forward expectation) with supporting momentum (YoY, 3m ann.). Missing inputs are re‑normalised out.

{
  "t5yie_level_z": 0.35, "t5yie_yoy_z": 0.15, "t5yie_ann3m_z": 0.10,
  "t10yie_level_z": 0.30, "t10yie_yoy_z": 0.07, "t10yie_ann3m_z": 0.03
}

MIIE_t = \sum_k w_k\, z^k_t,\quad \sum_k w_k = 1

6. Scaling & Regime Mapping

We provide a bounded score (0–100) and categorical regimes for communication:

Score_t = 100\times\dfrac{MIIE_t - \min_W MIIE}{\max_W MIIE - \min_W MIIE}

RISING: z(MIIE)_t ≥ 1.0 (expectations moving higher)
STABLE: −0.5 < z(MIIE)_t < 1.0
FALLING: z(MIIE)_t ≤ −0.5 (expectations moving lower)

7. Comparison Hooks

Fed Inflation Signal: Compare MIIE regime to composite inflation pressure; divergence suggests markets pricing a turn ahead of CPI.
CPI vs PPI: If PPI < CPI while MIIE is FALLING, it strengthens a disinflation narrative; the converse signals pressure.

These hooks require only the other signals’ latest regime/score; no structural coupling is assumed.

8. Implementation Notes (Python)

# Fetch (fredapi) → monthly end
t5 = fred.get_series("T5YIE")
t10 = fred.get_series("T10YIE")

# Resample to month-end & bounded ffill (<=2)
t5m = t5.resample("M").last().ffill(limit=2)
t10m = t10.resample("M").last().ffill(limit=2)

# Transforms (levels, YoY, 3m ann. in pct points)
def ann_3m(s): return 4*(s - s.shift(3))
components = {
  "t5yie_level": t5m, "t5yie_yoy": t5m - t5m.shift(12), "t5yie_ann3m": ann_3m(t5m),
  "t10yie_level": t10m, "t10yie_yoy": t10m - t10m.shift(12), "t10yie_ann3m": ann_3m(t10m),
}

# Robust rolling z-scores (W=120, fallback >=60)
# Combine with weights → MIIE, then scale to 0–100 and map regime.

9. Reproducibility, Audit & Monitoring

Persist FRED IDs, retrieval timestamps, resampling policy, and z‑score window.
Log cleaning (ffill count), staleness flags, and window adaptations.
Store panels: raw, transforms, z‑scores, contributions, composite, regimes.

10. Interpretation & Applications

MIIE reflects how markets price medium‑ to long‑term inflation. RISING heightens the risk of tighter policy and higher real yields; FALLING supports a disinflationary bias. Use in synthesis with liquidity, credit, and growth signals.

11. Governance & Change Control

Quarterly review of components, thresholds, weights, and backtests.
Document rationale for changes; maintain semantic versioning.