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Measuring and Explaining the CDS-Bond Basis Term-Structure Shape and Dynamics

Author

Listed:
  • Yonas Khanna

    (ING Bank)

  • André Lucas

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

  • Norman Seeger

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

Abstract

The CDS-bond basis quantifies the difference in risk premia between credit default swap (CDS) and bond markets. It is hard to measure at the individual firm level given substantial missing-value problems (30%-100%) in either or both markets, even for highly liquid blue-chip financial firms. We propose a novel imputation approach to obtain full historical firm-level basis term-structures across all maturities. Our approach can accommodate different term-structure interpolation methods, including Nearest-Neighbor, spline, and Nelson-Siegel interpolation. Using the new methodology, we construct the full history of the 2011-2021 JP Morgan (JPM) basis term-structure and use it to analyze its empirical determinants. We find that factors like market liquidity, funding liquidity, counterparty risk, and the default premium all impact the basis term-structure, though not all at the same moment in time. All factors are statistically significant during the Covid-19 pandemic. The various empirical limits-to-arbitrage proxies correlate differently with different parts of the basis term-structure, stressing the need to model the full basis term-structure rather than assuming it to be flat. The results are robust for other blue-chip financials, each time requiring the full basis term-structure imputation approach as proposed in this paper.

Suggested Citation

  • Yonas Khanna & André Lucas & Norman Seeger, 2025. "Measuring and Explaining the CDS-Bond Basis Term-Structure Shape and Dynamics," Tinbergen Institute Discussion Papers 25-037/III, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20250037
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    More about this item

    Keywords

    CDS-bond basis; missing value imputation; high-dimensional panel data; multi-curve modeling; time-varying spline interpolation; dynamic Nelson-Siegel; Kalman filter;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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