Analysis of Volatility Spillover and Connectedness in the Business Performance of the Korean Online Investment-Linked Financial Business
This study empirically analyzes the structure and connectedness of volatility spillovers among firms in the online investment-linked financial business (P2P lending), applying the volatility spillover model developed by Diebold and Yilmaz (2012). Using monthly loan balance data, the study quantitatively estimates the magnitude and direction of volatility transmission and visualizes the inter-firm linkages through network-based analysis. The results show that the Total Spillover Index (TSI) exhibits an inverted U-shaped trend, peaking during the 2021-2022 period. This pattern is interpreted as reflecting changes in the industry's concentration on real estate-secured loans and the homogeneity of loan products during the observation period. The Net Spillover Index was found to be lower for large-scale firms with higher loan balances, suggesting that these firms adopted more stable strategies that absorb, rather than transmit, market volatility. Meanwhile, the network analysis revealed that PFCT, the largest firm in terms of loan volume, was categorized as a volatility receiver based on its net spillover index, yet occupied a central position in the overall network. This indicates that PFCT played a role as a final absorber of shocks through its extensive connections with other firms. These findings empirically confirm that flexible and adaptive strategies were more effective for market participants in managing volatility. Furthermore, to support the healthy development and sustainable growth of the P2P lending business, the study highlights the need for policy support aimed at mitigating overconcentration in real estate-secured loans and promoting greater diversification in loan product structures.
Authors
- Jeong-Woo Kwak (ORCID: https://orcid.org/0009-0005-8209-5617)
Publication Details
- Journal
- Konkuk Research Institute of Real Estate and Urban Studies
- Published
- 2025-08-31
- DOI
- https://doi.org/10.22423/kreus.2025.18.1.75
- Primary Topic
- Impact of AI and Big Data on Business and Society
- Type
- article
- Field-Weighted Citation Impact
- 0.00