Nexus between financial innovations, remittances and credit performance: Evidence from augmented ARDL and nonlinear ARDL

  • Received December 21, 2020;
    Accepted March 10, 2021;
    Published September 10, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.18(3).2021.25
  • Article Info
    Volume 18 2021, Issue #3, pp. 295-311
  • TO CITE АНОТАЦІЯ
  • Funding data
    Funder name: The Institute of Advanced Research (IAR), United International University
    Funder identifier: IAR, UIU
    Award numbers: Grant UIU/IAR/01/2021/BE/07
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This work is licensed under a Creative Commons Attribution 4.0 International License

The motivation for this study is to assess the impact of financial innovation and remittances on bank-based financial institutions’ credit performance in Bangladesh for the period 1981–2019. The study applies augmented ARDL (AARDL) and nonlinear ARDL (NARDL) to identify both long-run and short-run effects and directional causality by performing non-granger casualty tests. AARDL confirms the presence of a long-run association between financial innovation, remittance, trade openness, FDI, and credit performance, which is measured by non-performing loans. In the long run, financial innovation and FDI volatility expose a positive link with NPLs, but remittance inflows and trade openness establish a negative association. Asymmetry shocks in financial innovation reveal a positive relationship with credit performance. In contrast, the asymmetric shock of remittance and trade openness unveil a negative tie to credit performance, especially in the long run. Furthermore, directional causality provides evidence to support a feedback hypothesis explaining causality between financial innovation and credit performance, as well as remittance inflows and credit performance. These findings suggest that credit performance is guided by future development in remittances and financial innovation; thus, closer attention from policymakers and financial experts is persistent to capitalize or mitigate the impact of the financial system.

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    • Figure 1. Conceptual model for hypotheses testing
    • Table 1. Variable definition and sources
    • Table 2. Null hypotheses for all three tests
    • Table 3. Unit root test results
    • Table 4. Ng-Parron unit root test
    • Table 5. Augmented ARDL cointegration test
    • Table 6. Long-run and short-run model coefficients
    • Table 6 (cont.). Long-run and short-run model coefficients
    • Table 7. NARDL model estimation results
    • Table 7 (cont.). NARDL model estimation results
    • Table 8. Toda-Yamamoto causality test results (dmax=4)
    • Table 8 (cont.). Toda-Yamamoto causality test results (dmax=4)
    • Conceptualization
      Md. Qamruzzaman
    • Data curation
      Md. Qamruzzaman
    • Formal Analysis
      Md. Qamruzzaman
    • Funding acquisition
      Md. Qamruzzaman
    • Investigation
      Md. Qamruzzaman
    • Methodology
      Md. Qamruzzaman
    • Project administration
      Md. Qamruzzaman
    • Resources
      Md. Qamruzzaman
    • Supervision
      Md. Qamruzzaman
    • Software
      Md. Qamruzzaman
    • Validation
      Md. Qamruzzaman
    • Visualization
      Md. Qamruzzaman
    • Writing – original draft
      Md. Qamruzzaman
    • Writing – review & editing
      Md. Qamruzzaman