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

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.

KEYWORDS:

• Term structure
• Indian Government bond
• Liquidity
• Non-linear state-space
• Unscented Kalman filter

JEL CLASSIFICATION:

• E43
• G12
• G17
• C55
• C58

Acknowledgments

We thank Arnab K. Laha, Anindya Chakrabarti, Brain Lucey, Jayanth R. Varma, Katherine Bennett Ensor, Rituparna Sen, Sourish Das, and participants at the 26th EBES conference(2018), 2nd INFINITI ASIA-PACIFIC Conference (2018), India Finance conference (2018), Statistical Methods in Finance conference (2018) for the valuable comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ See https://www.cboe.com/tradable_products/vix/ for methodology of computing VIX and https://www1.nseindia.com/content/indices/India_VIX_Fact_Sheet.pdf for more about India VIX