Non-performing Loan and its Effects on Banking Stability: Evidence from National and International Licensed Banks in Nigeria

Abstract

This study examines Non-Performing Loan (NPL) and its effects on the stability of Nigerian banks with national and international operational licenses from 2014:Q2 to 2017:Q2. A "restricted" dynamic GMM is employed to estimate the macroeconomic and bank specific drivers of NPL for each licensed category. Z-Score is constructed to proxy banking stability, and its response to shocks NPLs is examined in a panel vector autoregressive framework. The results reveal that drivers of NPLs vary across the two categories of banks, but, weighted average lending rate is a vital macroeconomic driver of NPLs for both. The results also confirm the moral hazard hypothesis and riskreturn tradeoff of efficient market theory. Furthermore, international banks withstand NPLs shocks in the long run, despite temporary flux in the short horizon, while the stability of national banks is susceptible to NPLs shocks in the long run. The study recommends that weighted average lending rate, anchored on monetary policy rate should be the focus of banks' regulators when addressing issues of NPLs. Again, strategies for mitigating short run impacts of NPLs on the stability of international licensed banks should be incorporated in the off-site regulatory framework to ensure banking stability.

The nexus between credit risk and liquidity risk and their impact on banks financial performance: Evidence from Pakistan

“…The Z -score measured is a dependent variable which is banks stability, distance to insolvency. The approach was used by (Atoi, 2018;Beck, Demirgüç-Kunt, & Merrouche, 2013;Ghenimi et al, 2017;Rizvi, Narayan, Sakti, & Syarifuddin, 2019). They found the inverse correlation between bank performance and probability of default (PD).…”

Determinants of nonperforming loans after recapitalization in the Nigerian banking industry: Does efficiency matter?

“…A serious concern between banking scholars and policymakers particularly after the global financial crisis (GFC) of 2007/2008 is the nonperforming loans in the banking industry. This concern is evident in recent studies such as Bolarinwa et al (2021), Kuzucu and Kuzucu (2019), Kumar and Kishore (2019), Rosenkranz and Lee (2019), Mazreku et al (2018), Atoi (2019), Us (2017), Yang (2017), Radivojevic and Jovovic (2017), Dimitrios et al (2016), Adeola and Ikpesu (2017), Memdani (2017). Scholars such as Rosenkranz and Lee (2019), Amuakwa‐Mensah et al (2017), Dimitrios et al (2016), and Demirgüç‐Kunt and Detragiache (1998) show that escalating nonperforming loans is a strong indicator of systemic risk and financial crisis.…”

Is there a nonlinear relationship between nonperforming loans and bank profitability? Evidence from dynamic panel threshold

“…This paper deviated from extant works by documenting the threshold level of nonperforming loans in the nexus. In particular, the philosophical stance of this paper differs from the long list of papers in the literature (Alandejani & Asutay, 2017; Amuakwa‐Mensah, Marbuah, & Ani‐Asamoah, 2017; Atoi, 2019; Gabriel et al, 2019; John, 2018; Kjosevski et al, 2019; Kure, Adigun & Okedigba, 2017; Kuzucu & Kuzucu, 2019; Laila, 2017; Rosenkranz & Lee, 2019; Us, 2018). To address this issue, the dynamic threshold model of Seo et al (2019) built on the GMM method is adopted.…”

“…Scholars have documented evidence from developing and developed economies. These authors have adopted panel of countries (Athanasoglou, Brissimis, & Delis, 2008; Bourke, 1988; Căpraru & Ihnatov, 2015; Dietrich & Wanzenried, 2011, 2014; Flamini, McDonald, & Schumacher, 2009; Petria, Capraru, & Ihnatov, 2015; Short, 1979) and the individual banking industry (Abid, Ouertani, & Zouari‐Ghorbel, 2014; Akinlo & Emmanuel, 2014; Amuakwa‐Mensah et al, 2017; EL‐Maude, Abdul‐Rahman, & Ibrahim, 2017; Alandejani & Asutay, 2017; Ali, 2018; Bolarinwa, Obembe, & Olaniyi, 2019; Gabriel, Victor, & Innocent, 2019; Atoi, 2019). Moreover, several studies examined the determinants using firm‐level, industry‐level, and the macroeconomic determinants separately whereas other studies have simultaneously adopted these variables (Bolarinwa et al, 2019).…”