Joint estimation of leverage thresholds in state-dependent capital structure dynamics: An (S,s) target zone framework for African firms

  • Oluseun Paseda orcid

    Department of Banking and Finance, University of Ibadan, Ibadan 200005, Nigeria; Babcock Business School, Babcock University, Ilishan-Remo 121103, Nigeria

  • Peter Ashade orcid

    Department of Finance, Babcock Business School, Babcock University, Ilishan-Remo 121103, Nigeria

  • Charles Manasseh orcid

    Department of Banking & Finance, University of Nigeria, Nsukka 410001, Nigeria

  • Felicia Olokoyo orcid

    Department of Finance, Covenant University, Ota 112233, Nigeria

  • J. Abiodun Oladimeji orcid

    Department of Finance, University of Lagos, Lagos 101017, Nigeria

  • Fatimah Abdulazeez orcid

    Department of Banking and Finance, University of Ibadan, Ibadan 200005, Nigeria

  • Idowu Bosede Fasola orcid

    Department of Banking and Finance, University of Ibadan, Ibadan 200005, Nigeria

Article ID: 4670
Keywords: dynamic corporate financing; refinancing hazard; dynamic panel econometrics; leverage adjustment asymmetry; partial-adjustment benchmarks; African listed firms; state-dependent decisions; inaction bands

Abstract

This study develops a joint-estimation framework for analysing state-dependent capital structure dynamics in African listed firms within an (S,s) target-zone setting. Unlike conventional speed-of-adjustment models that assume continuous convergence towards an optimal leverage ratio, the proposed framework allows firms to tolerate leverage drift within an inaction band and undertake refinancing only when upper or lower leverage thresholds are breached. The model jointly estimates target leverage, refinancing thresholds, and inaction-band width using dynamic panel techniques, constrained nonlinear estimation, and a state-dependent refinancing hazard with bootstrap-robust inference. The African setting provides a particularly suitable environment for investigating such dynamics because firms face episodic access to capital markets, exchange-rate and inflation shocks, shallow debt markets, and heterogeneous institutional environments that elevate refinancing frictions and encourage discontinuous adjustment behaviour. Empirical analysis is conducted using an unbalanced panel of listed non-financial firms from fifteen African stock exchanges over 1999–2024. The results reveal pronounced (S,s) behaviour characterised by wide periods of leverage inertia punctuated by discrete refinancing interventions. Inaction bands widen with firm-level volatility, financing deficits, and market-timing opportunities, but narrow significantly with liquidity, creditor-rights protection, governance quality, and market depth. Additional evidence indicates that upper-brink adjustments occur substantially more frequently than lower-brink adjustments, suggesting that deleveraging pressures dominate leverage-increasing episodes in African markets. Extensive Monte Carlo experiments, including misspecification tests against linear adjustment processes, demonstrate superior bias, RMSE (Root Mean Square Error), coverage, and classification performance relative to conventional alternatives while avoiding spurious threshold detection. The findings highlight the importance of institutional quality and financial-market development in shaping corporate refinancing behaviour and provide new evidence that leverage adjustment in African firms is inherently nonlinear, state dependent, and threshold driven.

Published
2026-08-31
How to Cite
Paseda, O., Ashade, P., Manasseh, C., Olokoyo, F., Oladimeji, J. A., Abdulazeez, F., & Fasola, I. B. (2026). Joint estimation of leverage thresholds in state-dependent capital structure dynamics: An (S,s) target zone framework for African firms. Advances in Differential Equations and Control Processes, 33(3). https://doi.org/10.59400/adecp4670

References

[1]Korteweg A, Strebulaev IA. An empirical target zone model of dynamic capital structure. SSRN. 2012. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1787001

[2]Gwatidzo T, Ojah K. Firms’ debt choices in Africa: Are institutional infrastructure and non-traditional determinants important? International Review of Financial Analysis. 2014; 31: 152–166.

[3]Ayadi E, Ben Jedidia L, Ben Mbarek N. Inflation Shocks and Equity Vulnerability: Regime, Sign, and Cross-Country Asymmetries in the G7. Economies. 2026; 14(2): 55. doi: 10.3390/economies14020055

[4]Doojav GO, Juragat A. Nonlinearities and state-dependence in the monetary transmission mechanism: Evidence from a commodity-dependent economy. International Economics. 2025; 184: 100640. doi: 10.1016/j.inteco.2025.100640

[5]Kuehn LA, Schreindorfer D, Schulz F. Persistent crises and levered asset prices. The Review of Financial Studies. 2023; 36(6): 2571–2616. doi: 10.1093/rfs/hhac081

[6]Magubane K. The stability of the financial cycle: Insights from a Markov switching regression in South Africa. Journal of Risk and Financial Management. 2025; 18(2): 76. doi: 10.3390/jrfm18020076

[7]Khemiri W, Noubbigh H. Determinants of capital structure: Evidence from Sub-Saharan African firms. Quarterly Review of Economics and Finance. 2018; 70: 150–159.

[8]Kamepalli SK. Essays in Macroeconomics of Microeconomic Frictions [PhD Thesis]. Columbia University; 2026.

[9]Harding M, Wouters R. Risk and State-Dependent Financial Frictions (No. 2022-37). Bank of Canada; 2022.

[10]Li S, Yang J, Zhao S. Robust leverage dynamics without commitment. Economic Theory. 2022; 74(2): 643–679.

[11]Frank MZ, Goyal VK. Empirical corporate capital structure. In: Handbook of Corporate Finance. Edward Elgar Publishing; 2024. pp. 27–125.

[12]Modigliani F, Miller MH. The cost of capital, corporation finance, and the theory of investment. American Economic Review. 1958; 48(3): 261–297.

[13]Modigliani F, Miller MH. Corporate income taxes and the cost of capital: A correction. American Economic Review. 1963; 53(3): 433–443.

[14]Miller MH. Debt and taxes. Journal of Finance. 1977; 32(2): 261–275. doi: 10.1111/j.1540-6261.1977.tb03267.x

[15]Myers S. The capital structure puzzle. Journal of Finance. 1984; 39(3): 575–592. doi: 10.2307/2327916

[16]Myers S, Majluf N. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics. 1984; 13(2): 187–221. doi: 10.1016/0304-405X(84)90023-0

[17]Baker M, Wurgler J. Market timing and capital structure. Journal of Finance. 2002; 57(1): 1–32. doi: 10.1111/1540-6261.00414

[18]DeAngelo H, Roll R. How stable are corporate capital structures? Journal of Finance. 2015; 70(1): 373–418. doi: 10.1111/jofi.12163

[19]DeAngelo H. Corporate financial policy: What really matters? Journal of Corporate Finance. 2021; 68: 101925. doi: 10.1016/j.jcorpfin.2021.101925

[20]DeAngelo H. The capital structure puzzle: What are we missing? Journal of Financial and Quantitative Analysis. 2022; 57(2): 413–454. doi: 10.1017/S002210902100079X

[21]Lambrecht BM, Myers SC. The dynamics of investment, payout, and debt. Review of Financial Studies. 2017; 30(11): 3759–3800. doi: 10.1093/rfs/hhx045

[22]Paseda OA, Ayadi OF. Capital structure instability: An empirical investigation among non-financial quoted firms in Nigeria. Nigerian Journal of Economic and Social Studies. 2023; 65(1): 72–128.

[23]Paseda O. The speed of adjustment of capital structure of Nigerian quoted firms. Journal of Developing Areas. 2025; 59(1): 207–231. doi: 10.1353/jda.2025.a952661

[24]Paseda O, Ashiru O, Balogun G. Financial innovation and bank financial performance: Further evidence from Nigerian deposit money banks. Journal of Developing Areas. 2026; 60(2): 109–144. doi: 10.1353/jda.2026.a992557

[25]Fontalvo HR, de la Puente M, Torres J, et al. Foreign Direct Investment Under Uncertainty: A Regime-switching, Threshold and Dynamic Correlation Analysis of Emerging Economies. Global Business Review. 2026. doi: 10.1177/09721509261418489

[26]Nyati MC, Muzindutsi PF, Tipoy C. Interactions between Business Cycles, Financial Cycles and Monetary Policy in South Africa. Forecasting. 2026; 8(3): 51. doi: 10.3390/forecast8030051

[27]Mudiangombe BM, Muteba Mwamba JW. Dynamic asymmetric effect of currency risk pricing of exchange rate on equity markets: a regime-switching based C-vine copulas method. International Journal of Financial Studies. 2022; 10(3): 72. doi: 10.3390/ijfs10030072

[28]Arrow KJ, Harris T, Marschak J. Optimal inventory policy. Econometrica. 1951; 19(3): 250–272. doi: 10.2307/1906813

[29]Miller MH, Orr D. A model of the demand for money by firms. Quarterly Journal of Economics. 1966; 80(3): 413–435. doi: 10.2307/1880728

[30]Caplin A, Leahy J. Economic theory and the world of practice: A celebration of the (S, s) model. Journal of Economic Perspectives. 2010; 24(1): 183–202. doi: 10.1257/jep.24.1.183

[31]Burdett K, Menzio G. The (Q,S,s) pricing rule. Review of Economic Studies. 2018; 85(2): 892–928. doi: 10.1093/restud/rdx048

[32]Lee J, Ying C. Leverage Dynamics and Liquidity Management without Commitment. SSRN. 2026. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6193739

[33]Li Y, Yang J, Zhou W. Robust Leverage Dynamics under Costly Equity Issuance. SSRN. 2026. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6305980

[34]Greenwald D. Firm debt covenants and the macroeconomy: The interest coverage channel. SSRN. 2019. Available online: https://ssrn.com/abstract=3535221

[35]Fama EF, French KR. Capital structure choices. Critical Finance Review. 2012; 1(1): 59–101. doi: 10.1561/104.00000002

[36]Paseda O. The impact of firm-specific characteristics on the capital structure of Nigerian quoted firms. SSRN. 2016. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2889419

[37]Paseda O. Firm size, asset tangibility, growth, volatility, dividends and the capital Structure of Nigerian quoted firms. Journal of Finance and Accounting. 2021; 9(2): 36–52. doi: 10.11648/j.jfa.20210902.13

[38]Jensen MC, Meckling WH. Theory of the firm: Managerial behavior, agency costs, and ownership structure. Journal of Financial Economics. 1976; 3(4): 305–360. doi: 10.1016/0304-405X(76)90026-X

[39]Jensen MC. Agency costs of free cash flow, corporate finance, and takeovers. American Economic Review. 1986; 76(2): 323–329.

[40]Arellano M, Bond S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies. 1991; 58(2): 277–297. doi: 10.2307/2297968

[41]Blundell R, Bond S. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics. 1998; 87(1): 115–143. doi: 10.1016/S0304-4076(98)00009-8

[42]Amemiya T. Advanced Econometrics. Harvard University Press; 1985.

[43]Gallant AR. Nonlinear Statistical Models. Wiley; 1987.

[44]White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980; 48(4): 817–838. doi: 10.2307/1912934

[45]Mammen E. Bootstrap and wild bootstrap for high dimensional linear models. Annals of Statistics. 1993; 21(1): 255–285. doi: 10.1214/aos/1176349025

[46]Cameron AC, Gelbach JB, Miller DL. Bootstrap-based improvements for inference with clustered errors. Review of Economics and Statistics. 2008; 90(3): 414–427. doi: 10.1162/rest.90.3.414

[47]Strebulaev IA, Whited TM. Dynamic models and structural estimation in corporate finance. Published in Foundations and Trends in Finance. 2012; 6: 1–163.

[48]Wooldridge JM. Econometric Analysis of Cross Section and Panel Data, 2nd ed. MIT Press; 2010.

[49]Ali H, Naz S. Interpretable deep learning for modeling policy uncertainty and firm-specific risk: Evidence from advanced and emerging markets. Data Science in Finance and Economics. 2026; 6(1): 147–182.

[50]Moroke ND. Cognitive Big Data Architecture for Daily Operational Jamming Transition Detection with Low-Latency Inference in Infrastructure-Constrained Financial Markets: The MERI Framework. Big Data and Cognitive Computing. 2026; 10(7): 240. doi: 10.3390/bdcc10070240

[51]Davidson R, MacKinnon JG. Bootstrap methods in econometrics. In: Mills TC, Patterson K (editors). Palgrave Handbook of Econometrics. Palgrave Macmillan; 2006. pp. 812–838.

[52]Acharya VV, Plantin G, Wang O. Indebted Supply and Monetary Policy: A Theory of Financial Dominance (No. w34798). National Bureau of Economic Research; 2026.

[53]Goodhart CA, Peiris MU, Tsomocos DP, et al. HKC05-Household Portfolios, Corporate Leverage, and the Supply Side of Monetary Policy. Oberlin College Kasper Economics and Business; 2026. Available online: https://digitalcommons.oberlin.edu/economics_wps/5

[54]Nguyen Khac Quoc B, Pham Duy T. Heterogeneous Impacts of Macroprudential Policy on GDP Tails: The Role of Credit Cycle, Financial Cycle and Financial Leverage in Vietnam. Emerging Markets Finance and Trade. 2025; 62(11): 4073–4093. doi: 10.1080/1540496X.2025.2604596

[55]Ahmed ZS, Ibrahim O. Credit Risk Dynamics and Macroeconomic Shocks in the European Union: A Stochastic Simulation Framework for Banking Stability and Policy Analysis (2000–2025). Research Square. 2026.

[56]Fusari N, Lamichhane S. Structural Equity Option Pricing: Implications for Credit Risk. SSRN. 2025; 5393252.