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 (T5YIFR, T10YIE).

Abstract

We construct a monthly signal using FRED’s T5YIFR (5‑Year, 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)

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.

What these series represent:

Data source: St. Louis Fed FRED, which republishes the underlying Treasury constant‑maturity estimates and derived breakeven/forward series.

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).
  • Breakeven distortions: breakevens embed (i) expected inflation, (ii) an inflation risk premium, and (iii) a TIPS liquidity premium; QE and “safe‑asset” demand can move nominal and real yields unevenly, shifting breakevens without a one‑for‑one change in expectations.
  • Liquidity overlays (recommended): record a liquidity_regime and optionally down‑weight level components when liquidity is stressed. Practical proxies include TIPS trading liquidity (bid‑ask/volume), nominal–TIPS fitting errors, or publicly available liquidity indices/ETFs when direct microstructure data are unavailable.

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

10. Interpretation Guide (How to Read the Signal)

Key identity: breakeven inflation is best treated as inflation compensation:

Breakeven ≈ Expected Inflation + Inflation Risk Premium − TIPS Liquidity Premium

This means the signal can move because markets update inflation expectations, but also because risk premia or liquidity conditions change.

  • RISING regime: inflation compensation is increasing relative to its long‑run distribution (z(MIIE) ≥ 1.0). Typical macro associations: higher inflation pricing, higher probability of restrictive policy, and (often) upward pressure on real yields—unless the move is dominated by liquidity/premia effects.
  • FALLING regime: inflation compensation is declining (z(MIIE) ≤ −0.5). Typical associations: disinflation pricing and/or flight‑to‑quality dynamics that compress inflation compensation.
  • STABLE regime: mid‑range z‑scores suggest expectations are not moving decisively relative to history; focus shifts to cross‑signal confirmation (CPI/PPI, growth, liquidity, credit).

10.1 Practical checks (avoid common misreads)

  • Liquidity stress: during risk‑off episodes, TIPS liquidity can deteriorate and mechanically depress breakevens. If a liquidity proxy is flagged “stressed”, interpret a FALLING reading with caution (it may be liquidity‑driven rather than a clean shift in expectations).
  • QE / supply effects: large‑scale asset purchases and shifts in the supply of safe duration can change term premia and relative pricing across nominal vs real curves; treat level moves as less informative when policy interventions are dominant.
  • Use z‑scores as “distance from normal”: robust long‑window z‑scores are intended to reduce sensitivity to transient dislocations. Extremes (e.g., |z| ≥ 2) are better viewed as “unusual pricing” than as point forecasts of inflation.
  • Cross‑validation: compare MIIE to realised inflation signals (Fed Inflation; CPI vs PPI) and to real‑yield/term‑premium diagnostics. Persistent divergence is often more informative than single‑month flips.

10.2 Literature anchor (selected)

  • Fed Board and academic work on extracting inflation compensation from TIPS yield curves and the role of risk premia (Gürkaynak, Sack & Wright).
  • Evidence that ignoring TIPS liquidity can mislead inference about expectations and inflation risk premia (D’Amico, Kim & Wei; related central‑bank summaries).
  • Documentation of persistent TIPS–Treasury mispricing consistent with liquidity/supply effects (Fleckenstein, Longstaff & Lustig).
  • Evidence that QE and safe‑asset demand shift term premia and relative pricing across rates markets (Krishnamurthy & Vissing‑Jorgensen).

12. Governance & Change Control