The Time-Varying Puzzle of Technology Finance and Systemic Risk Propagation: Nonlinear Transmission via Investor Sentiment and Asset Bubbles

The rapid expansion of technology finance poses complex challenges to systemic risk management, yet the existing literature lacks a game-theoretic framework to explain the dynamic risk transmission mechanisms involving investor sentiment and asset bubbles. This study examines the time-varying, nonlinear effects of technology finance on systemic risk through behavioral transmission channels by integrating a sequential game approach with a time-varying parameter stochastic volatility vector autoregression model. Using Chinese quarterly data from 2006Q1 to 2024Q4, this study captures endogenous market interactions and reveals a nonlinear, stage-dependent trajectory. Specifically, while technology finance amplifies systemic risk in the short term, it enhances system resilience over the long term. Furthermore, this paper identifies a cascading risk transmission mechanism: technology finance initially stimulates optimistic investor sentiment, driving short-term asset bubble formation and escalating systemic risk, whereas long-term patterns reflect market corrections driven by rational arbitrageurs and adaptive regulation. Exhibiting both stage and event dependence, these findings reconcile previously conflicting empirical evidence. Ultimately, this work develops a unified theoretical–empirical framework to elucidate how technology finance generates and transmits systemic risk, offering practical policy implications for fostering innovation while mitigating systemic risk.

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Publication Details

Journal
Systems
Published
2026-10-30
DOI
https://doi.org/10.3390/systems14111376
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.00

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article

The Time-Varying Puzzle of Technology Finance and Systemic Risk Propagation: Nonlinear Transmission via Investor Sentiment and Asset Bubbles

Jiaji An, He Di, Xinran Zhao
Systems
FinTech, Crowdfunding, Digital Finance
article

The Time-Varying Puzzle of Technology Finance and Systemic Risk Propagation: Nonlinear Transmission via Investor Sentiment and Asset Bubbles

Jiaji An, He Di, Xinran Zhao
article en

Abstract

The rapid expansion of technology finance poses complex challenges to systemic risk management, yet the existing literature lacks a game-theoretic framework to explain the dynamic risk transmission mechanisms involving investor sentiment and asset bubbles. This study examines the time-varying, nonlinear effects of technology finance on systemic risk through behavioral transmission channels by integrating a sequential game approach with a time-varying parameter stochastic volatility vector autoregression model. Using Chinese quarterly data from 2006Q1 to 2024Q4, this study captures endogenous market interactions and reveals a nonlinear, stage-dependent trajectory. Specifically, while technology finance amplifies systemic risk in the short term, it enhances system resilience over the long term. Furthermore, this paper identifies a cascading risk transmission mechanism: technology finance initially stimulates optimistic investor sentiment, driving short-term asset bubble formation and escalating systemic risk, whereas long-term patterns reflect market corrections driven by rational arbitrageurs and adaptive regulation. Exhibiting both stage and event dependence, these findings reconcile previously conflicting empirical evidence. Ultimately, this work develops a unified theoretical–empirical framework to elucidate how technology finance generates and transmits systemic risk, offering practical policy implications for fostering innovation while mitigating systemic risk.

SystemsVol. 14(11)
Jilin University of Finance and Economics (CN), Jilin University (CN)
National Social Science Fund of China
Openalex Percentile: Top 6%
FinTech, Crowdfunding, Digital Finance
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The Time-Varying Puzzle of Technology Finance and Systemic Risk Propagation: Nonlinear Transmission via Investor Sentiment and Asset Bubbles — Jiaji An, He Di, et al. · Systems (2026) | TGRS Research Map | TGRS