Estimation of Counterparty Credit Risk Impact under IFRS RequirementsA modelling proposal under a quantitative market information-based approach

Resumen

Counterparty credit risk is one of the main financial risk to be monitored by financial and non-financial institutions, worldwide. It entails a huge impact in areas as diverse as Business, Finance, Risk management, Funding & Liquidity management, Treasury, Trading, Solvency control, Accounting, Reporting, etc. Concerning valuation and accounting matters, counterparty credit risk is present throughout IFRS rules, with emphasis on a particular way under IFRS 9, IFRS 13 and IFRS 16. Under the IFRS 9, entities must estimate the PD (Probability of Default) for all financial assets (and other elements) not measured at Fair Value through Profit & Loss in turn to compute the Expected Credit Loss for those assets. Also, regarding the potential impact that a modification in a debt instrument terms (i.e., debt restructuring) may have under IFRS 9, the original debt could have to be derecognized and replaced with the present value of the modified debt, which should be computed by discounting its cash-flows with a robust, liquid yield curve according to the company´s credit quality and instrument seniority. Likewise, under IFRS 13 framework, the expected counterparty credit risk should be incorporated to the value of a derivative which is measured at Fair Value. In this case, the derivative credit risk will be determined for both counterparty (CVA – Credit Value Adjustment) and own credit risk (DVA – Debt Value Adjustment). Therefore, the counterparty credit quality (and subsequent PD) and the own PD for the entire life of the instrument should be estimated. A common problem in this regard is that there is no quoted credit instruments nor credit rating information of a company. For such cases, I propose a regression model that provides a theoretical credit rating for a counterparty as a first, necessary step when estimating the PD or the discounting curve. The model is new in a certain extent in comparison with other recent models in several aspects, such as the size and composition of the database used to calibrate the model variables (financial ratios percentiles within a sector distribution) and the fact that is intended to provide a “forward-looking” risk approach. The initial assumption is that financial ratios are a reliable source of information to estimate a rating letter when those are efficiently combined, with no necessity of qualitative nor additional company´s management-related information. I demonstrate that, with a granular sectorial database and by applying optimization in variables via Stepwise AIC process, the model output is reliable and robust to estimate the credit rating for a given company. On the other hand, under IFRS 16, entities must discount future lease payments to value the leased asset or liability. The discount rate is generally understood as the lessee’s IBR (Incremental Borrowing Rate). IFRS 16 states the IBR must consider that the hypothetical loan is collateralized by the leased asset. In this regard, there is a lack of accounting and finance literature focused on analysing how the IBR should be calculated taking into consideration both the counterparty credit risk of the lessee and the quality of the collateral. The starting hypothesis is that this quality is mainly determined by the underlying asset’s expected LGD (Loss-Given Default) so that the relationship between the IBR and the LGD could be modelled. In this thesis I propose two quantitative models based on CDS (Credit Default Swap) spreads and liquid bond prices to estimate the IBR given the lessee credit rating and collateral-linked LGD. The results are statistically robust and demonstrates that the relationship between CDS spreads or bonds yield-to-maturity and the LGD implied in their market prices can be translated as a sensitivity measure to estimate the IBR for a lease contract by pivoting from a standard market yield curve.