This study examines the connectedness and risk spillovers between Chinese FinTech and traditional financial institutions by using quantile-based vector autoregression (QVAR) networks. Specifically, by using daily data from January 2014 to June 2022, we focus on system-, sector-, and institution-level quantile connectedness characteristics, with the following findings. At the system level, the QVAR networks linking FinTech and traditional financial institutions are more connected at the extreme quantiles than at the median quantile. At the sector level, banks, real estate firms, and FinTech sectors act as net risk receivers, whereas securities and insurers act as net risk emitters. At the institutional level, risk transmission and reception of institutions significantly increase when market conditions rapidly change. We also investigate the determinants of quantile connectedness by using an exponential random graph model and find that (i) across different quantiles, the book-to-market and return on equity of institutions have a positive impact on their risk spillovers; (ii) at the extreme quantiles, the book-to-market is more pronounced than the return on equity; and (iii) at the median quantile, banks and FinTech institutions are more connected than insurers, real estate firms, securities, and other financials.