US 10-Year Real Yield (DFII10) Regime Signal Methodology

Abstract

This paper describes the methodology for deriving market regime signals from the US 10‑year real yield (DFII10), using a trend‑based framework. The approach computes the 6‑month moving average of the real yield and applies a categorical regime classification—Bullish, Bearish, or Neutral—based on the relationship between the yield and its trend. This framework aligns monetary conditions and real‑rate dynamics for macro interpretation.

1. Data

• DFII10: 10‑Year Treasury Inflation‑Indexed Security, constant maturity (Real Yield), monthly.
• Data sourced from FRED and begins in 2003‑01‑01.
• Additional contextual data (optional): market regimes or policy events for cross‑validation.

The dataset is sorted by Date and filtered to post‑2003 observations. All analysis is performed in percentage yield space.

2. Methodology

1. Compute a 6‑month moving average to capture medium‑term trend behaviour:
   Trend_t = Mean(Value_t−5:t)
2. Classify the signal based on yield position relative to trend:
   • Bullish: Real yield Value_t below its trend (Value_t < Trend_t), implying accommodative conditions.
   • Bearish: Real yield above its trend (Value_t > Trend_t), indicating tightening or restrictive conditions.
   • Neutral: Real yield within ±0.05 % of its trend.

This framework highlights medium‑term turning points in real interest‑rate regimes and serves as a diagnostic tool for assessing the stance of monetary conditions.

3. Implementation Notes

chart_df = merged_df.copy()
chart_df = chart_df.sort_values('Date')
chart_df = chart_df[chart_df['Date'] >= pd.Timestamp('2003-01-01')]

chart_df['DFII10_Trend'] = chart_df['Value_DFII10'].rolling(6, min_periods=1).mean()

def classify_signal(row):
    if pd.isna(row['Value_DFII10']) or pd.isna(row['DFII10_Trend']):
        return 'Neutral'
    diff = row['Value_DFII10'] - row['DFII10_Trend']
    if diff > 0.05:
        return 'Bearish'
    elif diff < -0.05:
        return 'Bullish'
    return 'Neutral'

chart_df['Signal'] = chart_df.apply(classify_signal, axis=1)

4. Sensitivity and Limitations

• Shorter windows (e.g., 3 m) capture tactical movements but add noise.
• Real yields are subject to breakeven inflation distortions; interpret the signal directionally.
• Neutral bounds (±0.05 %) can be tuned to reflect investor tolerance for noise.

5. Reproducibility

• Record source (DFII10 from FRED), retrieval date, and version control of scripts.
• Ensure consistent rolling‑window definitions and tolerance levels.
• Save both the chart image (US_Real_Yield_Signal_chart_mpl.png) and DataFrame output for auditability.

6. Applications

The real‑yield regime signal assists in assessing market expectations for real rates and inflation compensation. It provides macro context for equity‑bond correlation shifts and can be integrated into multi‑asset regime frameworks.