The study examines the dynamic relationships between credit risk, profitability, and capital. Credit risk is a crucial metric for bank health, affecting micro- and macro-financial stability. It is impossible to overstate the importance of effective risk management procedures for maintaining the health and stability of banks and reducing the potential threat that non-performing loans may pose. Profitability and capital, being of paramount importance in the realm of credit risk, represent intricately linked factors. While existing studies address the static linkages between credit risk, profitability, and capital in depth, there is a significant gap in our knowledge of their dynamic interactions. There exists a conspicuous void in our understanding of their dynamic interactions. To bridge this knowledge gap, our study employs a novel technique. We employ a Dynamic Common Correlated Effects (DCCE) model, leveraging a panel dataset encompassing 85 Indonesian banks, encompassing data spanning from January 2012 to September 2021. The study found that non-performing loans (NPLs) have a negative dynamic (and significant) relationship with profitability in the long run and short run, whereas capital is statistically unimportant. The equilibrating process of this dynamic relationship takes between 2.11 and 3.73 months. Our result is robust when examined across bank types but slightly different between before and during the COVID-19 era. The study contributes to understanding the relationships among credit risk, profitability, and the capital equilibrating process, and it has important implications for business practices and regulations, particularly in terms of financial stability.

Purpose: To examine the relationship between the term structure of interest rates of sovereign bonds in emerging nations and their macroeconomic indicators, specifically emphasizing its persistence and interaction with inflation, foreign exchange and fiscal conditions.Design/methodology/approach: Adopting the Mean Group Instrumental Variables (MGIV) technique, as proposed by Cui et al. (2020) and Norkute et al. (2021), this study analyzes a monthly panel dataset from nine emerging economies spanning January 2010 to October 2021, totaling 1,278 observations.Findings: The findings reveal significant persistence in both slope and curvature, with a rising yield level linked to the term structure's flattening, while shifts in inflation and exchange rates correlate with its steepening.Originality/value: Our study is among the few which used an empirically constructed measure of the term structure of interest instead of a theoretical construct. To best our knowledge, we are the first to employ MGIV.

This paper investigates credit risk management as a dynamic system. Panel Vector Autoregression (PVAR) is employed to model interrelationships among four key components: Non-Performing Loans (NPLs), Loan Loss Provision (LLP), loan charge-off (LCO) and capital. The Cost-to-Income ratio (CIR) and Size and Net Profit-to-Equity ratio (ROE) are used as control variables. The panel dataset comprises 1461 conventional rural banks in Indonesia with a quarterly frequency from June 2010 to March 2024. There are several key findings of this study. First, credit risk management practices in rural banks predominantly follow an incurred loss approach, although the expected loss model appears to be more commonly adopted by larger institutions. Second, capital serves a critical function as a buffer against credit losses. Third, subsample investigation reveals a significant role of accounting discretionary. This study offers significant implications for both policy development and academic research in microfinance.

Financial accounting manipulation (FAM) remains a persistent concern in emerging Asian markets, yet existing studies typically assess firm resilience, market valuation, and institutional governance separately. This study addresses this gap by examining how the Resilience Factor (RF), Market Valuation (VAL), and Country Governance Index (CGI), along with their interaction effects, shape FAM. Using a panel dataset of 4303 non-financial firms across 17 Asian countries from 2012 to 2023 (51,636 observations), the analysis employs an Instrumental Variable–Two-Stage Least-Squares (IV-2SLS) approach to address endogeneity related to simultaneity and omitted variable bias. The results show that financially resilient firms are more prone to manipulation, market valuation reduces manipulation incentives, and stronger country governance constrains manipulation. Moreover, valuation moderates the governance–manipulation relationship, suggesting complementary monitoring roles between markets and institutions. Robustness checks across regions, industries, and the COVID-19 period confirm the findings. The study contributes to agency and institutional theory by highlighting how firm-level and country-level mechanisms jointly influence manipulation, offering policy implications for regulators and investors in Asian capital markets.