High frequency online inflation and term structure of interest rates: Evidence from China

Tao Zhang, Ke Tang, Taoxiong Liu and Tingfeng Jiang

Journal of Empirical Finance, 2025, vol. 83, issue C

Abstract: In the digital era, the information value of online prices, characterized by weak price stickiness and high sensitivity to economic shocks, deserves more attention. This paper integrates the high-frequency online inflation rate into the dynamic Nelson-Siegel (DNS) model to explore its relationship with the term structure of interest rates. The empirical results show that the weekly online inflation significantly predicts the yield curve, especially the slope factor, whereas the monthly official inflation cannot predict the yield curve and is instead predicted by the yield curve factors. The mechanism analysis reveals that, due to low price stickiness, online inflation is more sensitive to short-term economic fluctuations and better reflects money market liquidity, thereby having significant predictive power for short-term interest rates and the slope factor. Specifically, online inflation for non-durable goods and on weekdays shows stronger predictive power for the slope factor. The heterogeneity in price stickiness across these categories explains the varying impacts on the yield curve.

Keywords: High-frequency online inflation; Term structure of interest rates; Dynamic Nelson-Siegel model; Price stickiness (search for similar items in EconPapers)
JEL-codes: C53 E43 E44 G17 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:83:y:2025:i:c:s0927539825000489

DOI: 10.1016/j.jempfin.2025.101626

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