Credit Spread Risk in the Banking Book and Treasury Stability: Evidence from Indonesian Banks
DOI:
https://doi.org/10.23917/reaksi.v11i2.17166Keywords:
CSRBB, Treasury Stability, Fixed Effect Model, Audit, Banking SectorAbstract
This study examines the effect of Credit Spread Risk in the Banking Book (CSRBB) on treasury stability and investigates the moderating role of audit within the Indonesian banking sector. Using panel data from major banks over the period 2021–2025, this research applies pooled ordinary least squares (OLS), fixed effect, and moderated regression analysis to provide comprehensive empirical evidence. The results show that CSRBB has a negative and statistically significant effect on treasury stability in the pooled OLS model, indicating that higher credit spread risk reduces the stability of banking treasury performance. However, this effect becomes insignificant when bank specific heterogeneity is controlled using the fixed effect model, suggesting that the impact of CSRBB is largely driven by cross sectional differences across banks rather than within bank variations over time. Furthermore, the findings reveal that bank specific characteristics play a dominant role in explaining treasury stability, with certain banks demonstrating significantly higher performance than others. The moderated regression analysis indicates that audit does not significantly moderate the relationship between CSRBB and treasury stability, implying that governance mechanisms, as proxied by audit, are not sufficiently effective in influencing risk performance dynamics. Overall, this study highlights the importance of internal bank characteristics and risk management practices in determining treasury stability, while the role of CSRBB and audit appears to be conditional and context dependent. The findings provide important implications for banking institutions and regulators in enhancing risk management frameworks and governance effectiveness.
References
Alsulmi, F., Mahmood, R., & Sapar, R. (2024). The Effects of Credit , Liquidity , and Operational Risks on GCC Bank Financial Stability : Moderating Role of Board Size. Advances in Social Sciences Research Journal, 11(11), 191–207. https://doi.org/https://doi.org/10.14738/assrj.1111.17840
Andini, N., & Malini, H. (2026). The influence of non-performing loans ( NPL ), loan to deposit ratio ( LDR ), return on assets ( ROA ), and capital adequacy ratio ( CAR ) on credit growth in commercial banks in Indonesia. Journal Economic and Business, 5(1). https://doi.org/10.56495/ejeb.v5i1.1374
Blöchlinger, A. (2021). Interest rate risk in the banking book : A closed-form solution for non-maturity deposits R. Journal of Banking and Finance Journal Homepage: Www.Elsevier.Com/Locate/Jbf Interest, 125. https://doi.org/10.1016/j.jbankfin.2021.106080
Bohn, A., & Marinez, J. (2025). Confronting new risk management guidelines for credit spread risk. McKinsey & Company, February, 1–9. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/confronting-new-risk-management-guidelines-for-credit-spread-risk-in-banking
Clark, E., & Baccar, S. (2018). Modelling credit spreads with time volatility , skewness , and kurtosis. Annals of Operations Research, 262(2), 431–461. https://doi.org/10.1007/s10479-015-1975-5
Erzurumlu, Y., Kirik, A., & Oygur, T. (2025). Dynamic Modelling of Shocks to Credit Spread Risk in Banking Book: Evidence from European Sovereign Markets. North American Journal of Economics and Finance, 86(1), 32. https://doi.org/10.1016/j.najef.2026.102688
Hariyanto, M. D., & Maryono. (2025). Analysis of the effect of financial ratios on return on assets (roa) in banking companies in 2020-2024. Journal of Economic, Business and Accounting, 8(4), 1794–1806. https://doi.org/10.31539/2jagyn29
Hughen, L. (2026). The Influence of Derivatives on Audit and Financial Reporting Risks. Accounting and Auditing, 2(1), 1–17. https://doi.org/10.3390/accountaudit2010001
Ie, R., Linggadjaya, T., Dorkas, A., & Atahau, R. (2025). The Moderating Role of Capital Adequacy on Bank Specific Characteristics to Sustainable Growth : Evidence From Commercial Banks in Indonesia. SAGE Open, September, 1–21. https://doi.org/10.1177/21582440251353042
Jainuri, M. R., & Kunaifi, A. (2026). Credit Risk Dynamics and Credit Quality in the Banking Sector and FMCG Industry : Bibliometric Study and Content Analysis. Journal of Social Research, 5(5), 1784–1793. https://doi.org/10.55324/josr.v5i5.3125
Kara, A. (2016). Securitisation and banking risk : what do we know so far ? Review of Behavioral Finance, 8(1). https://doi.org/10.1108/RBF-07-2014-0039
Maulana, A., Murdhaningsih, & Luthfi, F. (2025). Financial risk management , capital adequacy , and stability of Islamic banks : The moderating effect of efficiency in the Indonesian and Malaysian context. Banks and Bank Systems. https://doi.org/10.21511/bbs.20(3).2025.18
Munthe, S. A. P., Pane, S. G., & Nasution, L. N. (2025). Dynamic Analysis of Non-Performing Loans in Indonesian Banking. International Journal of Economics and Management Sciences, 0965. https://doi.org/10.61132/ijems.v2i4.976
Murtiningrum, W., & Wahyuningsih, E. (2024). Analysis of the Effect of Financial Ratios on ROA at Commercial Banks on the IDX. Asean International Journal of Business, 3(1), 12–19. https://doi.org/10.54099/aijb.v3i1.470
Pradigdo, A. C., Albart, N., & Huda, N. (2025). Systematic Literature Review: CAR, LDR, NIM and NPL on Banking Profitability in Indonesia. Jurnal Bisnis, Manajemen Dan Perbankan, 11(02), 372–386. https://doi.org/10.21070/jbmp.v11i2.2112
Prashanth, B. S., Kumar, M., Hoque, A., Al, N., Azaad, I., Christian, U., & Rao, A. (2026). Prediction of bank transaction fraud using TabNet — an adaptive deep learning architecture. International Review of Economics and Finance, 106(January), 1–27. https://doi.org/10.1016/j.iref.2026.104916
Rizkison., Susilawati, N., Munawarah, I., & Septria, F. (2025). Analisis Mendalam Non-Performing Loan (NPL) Bank Umum Konvensional Dilihat dari CAR, LDR, ROA, dan BOPO yang Terdaftar di Bursa Efek Indonesia (BEI). Jurnal Keuangan Dan Bisnis, 17(1), 92–99. https://doi.org/10.58890/jkb.v17i1.390
Segal, M. (2025). Modelling CSRBB under regulatory guidelines. Finance Research Letters, 82(February), 107501. https://doi.org/10.1016/j.frl.2025.107501
Wosko, Z. (2025). Is Credit Spread Risk Modellable ? Understanding CSRBB Modelling in Banks. Financial Sciences, 30(2). https://doi.org/10.15611/fins.2025.2.04
Wu, Z., Yang, B., & Su, Y. (2022). Liquidity , Credit Risk , and Their Interaction on the Spreads in China ’ s Corporate Bond Market. Discrete Dynamics in Nature and Society The, 2022. https://doi.org/10.1155/2022/2996704
Yi, J. (2014). Treasury Bills and Central Bank Bills for Monetary Policy. Procedia - Social and Behavioral Sciences, 109, 1256–1260. https://doi.org/10.1016/j.sbspro.2013.12.622
Zhang, Q., & Wu, M. (2011). Credit Risk Mitigation Based on Jarrow-Turnbull Model. Systems Engineering Procedia, 2(106), 49–59. https://doi.org/10.1016/j.sepro.2011.10.007
Downloads
Submitted
Accepted
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Riset Akuntansi dan Keuangan Indonesia

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.













