Subtopic Deep Dive

Non-Performing Loans in Banking
Research Guide

What is Non-Performing Loans in Banking?

Non-Performing Loans (NPLs) in banking are loans where borrowers fail to make scheduled payments for 90 days or more, analyzed through macroeconomic and bank-specific determinants across mortgage, business, and consumer portfolios.

Louzis et al. (2011) identify GDP growth, unemployment, lending rates, and bank capital as key NPL drivers in Greece, with distinct effects by loan type (1083 citations). Post-financial crisis studies link NPLs to banking globalization shocks (Claessens and van Horen, 2015, 227 citations). Over 20 papers from the list examine European banking deregulation and competition impacts on NPL risks.

Curated Papers

Key Challenges

Why It Matters

Louzis et al. (2011) enable banks to forecast NPL ratios using panel regressions on Greek data, aiding credit provisioning. Regulators apply Claessens and van Horen (2015) insights to monitor globalization-induced vulnerabilities post-2008 crisis. Baltensperger and Dermine (1987) inform prudential rules to balance deregulation with stability, reducing systemic risks in retail markets (Heinemann and Jopp, 2002).

Key Research Challenges

Heterogeneous Loan Portfolio Effects

NPL drivers differ across mortgage, business, and consumer loans, complicating unified models (Louzis et al., 2011). Greek data shows unemployment impacts consumer loans more than mortgages. Separate regressions needed per portfolio.

Macroeconomic Forecasting Accuracy

Linking GDP and unemployment to future NPLs faces lagged effects and crisis shocks (Claessens and van Horen, 2015). Post-2008 volatility reduces model reliability. Dynamic panel methods required for robustness.

Bank-Specific vs. Systemic Drivers

Distinguishing bank capital efficiency from economy-wide factors challenges risk attribution (Staikouras, 2006). Deregulation amplifies competition-NPL links (Baltensperger and Dermine, 1987). Micro-macro integration demands advanced econometrics.

Essential Papers

Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios

Dimitrios P. Louzis, Angelos T. Vouldis, Vasilios L. Metaxas · 2011 · Journal of Banking & Finance · 1.1K citations

The Impact of the Global Financial Crisis on Banking Globalization

Stijn Claessens, Neeltje van Horen · 2015 · IMF Economic Review · 227 citations

Factors Influencing the Choice of Commercial Banks by University Students in South Africa

Cleopas Chigamba, Olawale Fatoki · 2011 · International Journal of Business and Management · 80 citations

University students represent an attractive segment of customers for retail banks in many countries includingSouth Africa. The objective of this study was to investigate the determinants of the cho...

Banking Deregulation in Europe

Ernst Baltensperger, Jean Dermine, Charles Goodhart et al. · 1987 · Economic Policy · 77 citations

Banking deregulation Ernst Baltensperger and Jean Dermine Deregulation of financial services is well under way in many European countries. This has led to fears that economies are now more vulnerab...

Business Opportunities and Market Realities in Financial Conglomerates

Sotiris K. Staikouras · 2006 · The Geneva Papers on Risk and Insurance Issues and Practice · 54 citations

The benefits of a working European Retail Market for financial services: Report to European Financial Services Round Table

Friedrich Heinemann, Mathias Jopp · 2002 · Econstor (Econstor) · 28 citations

Die Deutsche Bibliothek – CIP-Einheitsaufnahme Heinemann, Friedrich: The benefits of a working European retail market for financial services /

Prudential Regulation and Competition in Financial Markets

Rüdiger Ahrend, Jens Arnold, Fabrice Murtin · 2009 · OECD Economics Department working papers · 27 citations

This paper examines how a range of stability-oriented regulatory policies for banking and insurance are related to selected stability and competition outcomes in these sectors. Based on survey info...

Reading Guide

Foundational Papers

Start with Louzis et al. (2011) for core determinants across loan types (1083 citations), then Baltensperger and Dermine (1987) for deregulation context shaping NPL risks.

Recent Advances

Claessens and van Horen (2015) on crisis globalization effects (227 citations); Kubiszewska (2017) on Baltic banking concentration and NPLs; Stavárek and Řepková (2014) on Czech competition metrics.

Core Methods

Panel data regressions for macro-bank drivers (Louzis et al., 2011); Panzar-Rosse for competition (Stavárek and Řepková, 2014); survey-based prudential policy indices (Ahrend et al., 2009).

How PapersFlow Helps You Research Non-Performing Loans in Banking

Discover & Search

Research Agent uses searchPapers('non-performing loans Greece determinants') to find Louzis et al. (2011), then citationGraph reveals 1000+ citing works on European NPLs, and findSimilarPapers expands to Claessens and van Horen (2015) for crisis impacts.

Analyze & Verify

Analysis Agent applies readPaperContent on Louzis et al. (2011) to extract regression coefficients, verifyResponse with CoVe checks GDP elasticity claims against raw data, and runPythonAnalysis replicates panel models using pandas for NPL forecasting verification with GRADE scoring model fit.

Synthesize & Write

Synthesis Agent detects gaps in post-2011 NPL data for emerging markets, while Writing Agent uses latexEditText to draft equations, latexSyncCitations for Louzis references, and latexCompile to generate a risk model paper with exportMermaid for determinant flowcharts.

Use Cases

"Replicate Louzis 2011 NPL regression on new Greek bank data"

Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas OLS on GDP/unemployment) → GRADE verification → output: R-squared plot and coefficients CSV.

"Write LaTeX section on NPL portfolio differences citing Louzis"

Research Agent → citationGraph → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → output: Compiled PDF with tables and synced bibliography.

"Find code for NPL forecasting models from papers"

Research Agent → paperExtractUrls (Stavárek and Řepková 2014) → Code Discovery → paperFindGithubRepo → githubRepoInspect → output: Panzar-Rosse H-statistic Python scripts for competition-NPL analysis.

Automated Workflows

Deep Research workflow scans 50+ NPL papers via searchPapers, structures Louzis-style determinants report with citationGraph clusters. DeepScan applies 7-step CoVe to verify Claessens (2015) globalization claims against bank data. Theorizer generates hypotheses on deregulation-NPL links from Baltensperger (1987) and Staikouras (2006).

Try Doxa for Non-Performing Loans in Banking Research

Frequently Asked Questions

What defines non-performing loans?

NPLs are loans overdue by 90+ days; Louzis et al. (2011) analyze them separately for mortgage (low sensitivity to rates), business (high GDP link), and consumer (unemployment-driven) portfolios in Greece.

What are key methods for NPL analysis?

Panel regressions and dynamic GMM models identify macro drivers like GDP growth (Louzis et al., 2011); Panzar-Rosse H-statistic measures competition impacts (Stavárek and Řepková, 2014).

What are foundational papers?

Louzis et al. (2011, 1083 citations) on Greek NPL determinants; Baltensperger and Dermine (1987, 77 citations) on deregulation risks; Staikouras (2006, 54 citations) on conglomerate exposures.

What open problems exist?

Post-crisis NPL forecasting under low rates unaddressed; heterogeneous portfolio models lack global data (Claessens and van Horen, 2015); competition-NPL interactions need real-time metrics (Ahrend et al., 2009).