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Term structure estimation with liquidity-adjusted Affine Nelson Siegel model: A nonlinear state space approach applied to the Indian bond market

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  • Sudarshan Kumar
  • Vineet Virmani

Abstract

Efficient term structure estimation in emerging markets is difficult not only because of overall lack of liquidity, but also because of the concentration of liquidity in a few securities. Using the arbitrage-free Affine Nelson-Siegel model, we explicitly incorporate this phenomenon using a proxy for liquidity based on observable data in the bond pricing function and estimate the term structure for Indian Government bond markets in a nonlinear state space setting using the Unscented Kalman Filter. We find strong empirical evidence in support of the extended model with both i) a better in-sample fit to bond prices, and ii) the likelihood ratio test rejecting the restrictions assumed in the standard AFNS specification. In an alternative specification, we also model liquidity as a latent risk factor within the AFNS framework. The estimated latent liquidity factor is found to be strongly correlated with the standard market benchmarks of overall liquidity and the India VIX index.

Suggested Citation

  • Sudarshan Kumar & Vineet Virmani, 2022. "Term structure estimation with liquidity-adjusted Affine Nelson Siegel model: A nonlinear state space approach applied to the Indian bond market," Applied Economics, Taylor & Francis Journals, vol. 54(6), pages 648-669, February.
    • Handle: RePEc:taf:applec:v:54:y:2022:i:6:p:648-669
    DOI: 10.1080/00036846.2021.1967866
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    Cited by:

    1. Sudarshan Kumar & Sobhesh Kumar Agarwalla & Jayanth R. Varma & Vineet Virmani, 2023. "Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1615-1644, November.

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