Binomial model for measuring expected credit losses from trade receivables in non-financial sector entities Cover Image

Binomial model for measuring expected credit losses from trade receivables in non-financial sector entities
Binomial model for measuring expected credit losses from trade receivables in non-financial sector entities

Author(s): Branka Remenarić, Ivan Čevizović, Ivana Kenfelja
Subject(s): Methodology and research technology, Financial Markets, Accounting - Business Administration
Published by: Sveučilište Josipa Jurja Strossmayera u Osijeku, Ekonomski fakultet u Osijeku
Keywords: Expected credit loss model; binomial model; IFRS 9; accounts receivable; financial instruments; incurred loss model;

Summary/Abstract: In July 2014, the International Accounting Standards Board (IASB) published International Financial Reporting Standard 9 Financial Instruments (IFRS 9). This standard introduces an expected credit loss (ECL) impairment model that applies to financial instruments, including trade and lease receivables. IFRS 9 applies to annual periods beginning on or after 1 January 2018 in the European Union member states. While the main reason for amending the current model was to require major banks to recognize losses in advance of a credit event occurring, this new model also applies to all receivables, including trade receivables, lease receivables, related party loan receivables in non-financial sector entities. The new impairment model is intended to result in earlier recognition of credit losses. The previous model described in International Accounting Standard 39 Financial instruments (IAS 39) was based on incurred losses. One of the major questions now is what models to use to predict expected credit losses in non-financial sector entities. The purpose of this paper is to research the application of the current impairment model, the extent to which the current impairment model can be modified to satisfy new impairment model requirements and the applicability of the binomial model for measuring expected credit losses from accounts receivable.

  • Issue Year: 31/2018
  • Issue No: 1
  • Page Range: 125-135
  • Page Count: 11
  • Language: English