Artificial Intelligence and the Indian Sovereign Yield Curve: Empirical Evidence in Times of Macroeconomic Turmoil

Lekha Chakraborty () and Prasanth C. ()
Additional contact information
Lekha Chakraborty: National Institute of Public Finance and Policy
Prasanth C.: Christ (Deemed to be University), Bengaluru

Working Papers from National Institute of Public Finance and Policy

Abstract: This paper investigates whether AI adoption has induced structural changes in the determinants of Indian sovereign bond yields across the maturity spectrum. The rapid global adoption of artificial intelligence (AI) in times of macroeconomic turmoil, particularly generative AI technologies since late 2022, has prompted intense debate about its potential macroeconomic consequences, including the measurement issues. Using monthly data from 2000 to 2025 and autoregressive distributed lag (ARDL) models augmented with an AI dummy variable and slope interactions on expected inflation and broad money (M3) growth, we identify significant regime shifts. Results indicate that in the post-AI period, longer-maturity yields exhibit markedly reduced sensitivity to expected inflation and money supply growth. This dampening is statistically significant, with interaction terms largely offsetting baseline positive elasticities. By contrast, short-term yields (91-day Treasury bills) show heightened inflation sensitivity in the AI era, while intermediate yields display mixed patterns. These findings are consistent with theoretical predictions that AI-driven productivity gains could lower equilibrium real interest rates and weaken traditional monetary transmission channels at the long end of the yield curve. For an emerging market like India, where inflation expectations have historically influenced borrowing costs, such changes may enhance monetary policy independence but complicate fiscal-monetary coordination. The paper contributes to the sparse empirical literature on AI’s financial market implications in emerging economies. Policy implications include the need for the Reserve Bank of India (RBI) to recalibrate forward guidance and liquidity operations in light of evolving yield dynamics.

Keywords: Sovereign bond yields; artificial intelligence; ARDL bounds testing; structural change; monetary transmission; India (search for similar items in EconPapers)
JEL-codes: C22 E43 E44 G12 O33 (search for similar items in EconPapers)
Date: 2026-06
Note: Working Paper 450, 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nipfp.org.in/media/documents/WP_450_2026.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:npf:wpaper:26/450

Access Statistics for this paper

More papers in Working Papers from National Institute of Public Finance and Policy
Bibliographic data for series maintained by S.Siva Chidambaram ( this e-mail address is bad, please contact ).