How to estimate expected credit losses – ECL – for provisioning under IFRS 9

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mariya Gubareva
Keyword(s):  
Risk Premium ◽  
Default Risk ◽  
Financial Regulation ◽  
Credit Default Swap ◽  
High Yield ◽  
Expected Loss ◽  
Content Type ◽  
Term Structures ◽  
Ifrs 9 ◽  
Investment Grade

PurposeThis paper provides an objective approach based on available market information capable of reducing subjectivity, inherently present in the process of expected loss provisioning under the IFRS 9.Design/methodology/approachThis paper develops the two-step methodology. Calibrating the Credit Default Swap (CDS)-implied default probabilities to the through-the-cycle default frequencies provides average weights of default component in the spread for each forward term. Then, the impairment provisions are calculated for a sample of investment grade and high yield obligors by distilling their pure default-risk term-structures from the respective term-structures of spreads. This research demonstrates how to estimate credit impairment allowances compliant with IFRS 9 framework.FindingsThis study finds that for both investment grade and high yield exposures, the weights of default component in the credit spreads always remain inferior to 33%. The research's outcomes contrast with several previous results stating that the default risk premium accounts at least for 40% of CDS spreads. The proposed methodology is applied to calculate IFRS 9 compliant provisions for a sample of investment grade and high yield obligors.Research limitations/implicationsMany issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.Practical implicationsThe proposed approach offers a clue for a better alignment of accounting practices, financial regulation and credit risk management, using expected loss metrics across diverse silos inside organizations. It encourages adopting the proposed methodology, illustrating its application to a set of bond exposures.Originality/valueNo previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.

US energy high-yield bonds spur default risk

2015 ◽  
Keyword(s):  
Interest Rates ◽  
Default Risk ◽  
Oil Price ◽  
High Yield ◽  
Content Type ◽  
The Past ◽  
The Us ◽  
Low Interest Rates ◽  
Investment Grade ◽  
Energy Companies

Subject Impact of the oil price drop on energy high-yield bonds. Significance The over 50% oil price drop since June 2014 is hitting bonds issued by energy companies, particularly those issued by sub-investment grade corporates. The US high-yield bond market has been growing rapidly over the past five years. The shale boom has generated considerable investment, mainly funded through the issuance of these bonds which benefit from historically low interest rates. As the oil price has plunged, the spread over Treasury yields paid by the average issuer in the energy subsector has more than doubled between July and the December 2014 peak. Impacts Yields currently offered by the energy subsector are not far from pricing in a default scenario. Persistently low oil prices will further darken the outlook for the energy subsector and the high-yield market generally. A possible default cycle in the energy sector could accelerate outflows, overstretching the sector further.

High Yield Bonds

2019 ◽  
pp. 599-620
Author(s):  
Byron C. Barnes ◽  
Tony Calenda ◽  
Elvis Rodriguez
Keyword(s):  
Interest Rates ◽  
Economic Activity ◽  
Default Risk ◽  
Credit Default Swap ◽  
High Yield ◽  
Credit Default ◽  
Key Drivers ◽  
Default Swap ◽  
Investment Grade ◽  
Credit Event

High yield bonds (HYBs) have become an integral part of the funding and investment landscape. HYBs are bonds rated below investment grade, indicating a potentially greater default risk and concomitant return. Although often associated with leveraged buyouts (LBOs), corporations also use HYBs to finance general corporate needs. The key drivers of HYB issuance include general economic activity, the number and size of transactions requiring financing, interest rates, and the availability of substitute financial products such as leveraged loans. Leveraged loans are another source of financing for issuers with a similar profile as HYB issuers. A key difference between HYBs and leveraged loans is that the covenants associated with a leveraged loan are typically more lender friendly. Similar to investment grade bonds, investors can purchase insurance to hedge a long HYB position against a credit event by using a credit default swap.

SEC permits abbreviated tender and exchange offers for non-convertible high-yield and investment-grade debt securities

2015 ◽  
Vol 16 (3) ◽  
pp. 28-29
Author(s):  
Ian B. Blumenstein ◽  
J. Eric Maki ◽  
John T. Owen
Keyword(s):  
Design Methodology ◽  
High Yield ◽  
Content Type ◽  
Efficient Mechanism ◽  
Practical Guidance ◽  
Traditional Structures ◽  
Investment Grade ◽  
Practical Implications

Purpose – To advise companies of a recent SEC no-action letter relating to tender and exchange offers for certain debt securities. Design/methodology/approach – Reviews various conditions allowing an issuer to use a shortened timeframe in which certain debt tender/exchange offers need be kept open for as few as five business days. Findings – The abbreviated debt tender/exchange offer structure contemplated by the no-action letter provides a more efficient mechanism for conducting debt tender/exchange offers in certain circumstances. Practical implications – Issuers conducting a debt tender/exchange offer should consider whether the new abbreviated structure is more effective in achieving their objectives than the more traditional structures. Originality/value – Practical guidance from experienced securities regulatory lawyers that gives an overview of important developments in debt tender/exchange offer practice.

scholarly journals Spread determinants in corporate bond pricing: The effect of market and liquidity risks

2020 ◽  
pp. 2-2
Author(s):  
Menevşe Özdemir-Dilidüzgün ◽  
Ayşe Altıok-Yılmaz ◽  
Elif Akben-Selçuk
Keyword(s):  
Risk Premium ◽  
Emerging Market ◽  
Corporate Bonds ◽  
Liquidity Risk ◽  
Market Liquidity ◽  
Corporate Bond ◽  
Bond Pricing ◽  
High Yield ◽  
Bond Liquidity ◽  
Investment Grade

This paper investigates the effect of market and liquidity risks on corporate bond pricing in Turkey, an emerging market, and in Europe. Results show that corporate bond returns have exposure to liquidity factors and not to market factors in both settings. Corporate bonds issued in Turkey have significant exposure to fluctuations in benchmark treasury bond liquidity and corporate bond market liquidity; while corporate bonds issued in Eurozone have exposure to equity market liquidity and are sensitive to fluctuations in a 10-year generic government bond liquidity. The total estimated liquidity risk premium is 0.7% per annum for Turkish ?A? and above graded corporate bonds, and 1.08% for the last investment grade level (BBB-) long term bonds. For Eurozone, the total liquidity risk premium is 0.27% for investment grade 5-10 year term bonds, 1.05% for high-yield 1-5 year term bonds and 1.02% for high-yield 5-10 year term category.

Calculating lifetime expected loss for IFRS 9: which formula is measuring what?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bernd Engelmann
Keyword(s):  
Design Methodology ◽  
Cash Flows ◽  
Management Systems ◽  
Expected Loss ◽  
Loan Loss ◽  
Fixed Rate ◽  
Content Type ◽  
Mathematical Proofs ◽  
Ifrs 9 ◽  
Practical Implications

PurposeThe purpose of this article is to derive formulas for lifetime expected credit loss of loans that are required for the calculation of loan loss reserves under IFRS 9. This is done both for fixed-rate and floating rate loans under different assumptions on LGD modeling, prepayment, and discount rates.Design/methodology/approachThis study provides exact formulas for lifetime expected credit loss derived analytically together with the mathematical proofs of each expression.FindingsThis articles shows that the formula most commonly applied in the literature for calculating lifetime expected credit loss is inconsistent with measuring expected loss based on expected discounted cash flows. Formulas based on discounted cash flows always lead to more conservative numbers.Practical implicationsFor banks reporting under IFRS 9, the implication of this research is a better understanding of the different approaches used for computing lifetime expected loss, how they are connected, and what assumptions are underlying each approach. This may lead to corrections in existing frameworks to make applications of risk management systems more consistent.Originality/valueWhile there is a lot of literature explaining IFRS 9 and evaluating its impact, none of the existing research has systematically analyzed the calculation of lifetime expected credit loss for this purpose and how the formula changes under different modeling assumptions. This gap is filled by this study.

scholarly journals Development of an Impairment Point in Time Probability of Default Model for Revolving Retail Credit Products: South African Case Study

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 208
Author(s):  
Douw Gerbrand Breed ◽  
Niel van Jaarsveld ◽  
Carsten Gerken ◽  
Tanja Verster ◽  
Helgard Raubenheimer
Keyword(s):  
South African ◽  
Term Structure ◽  
Default Risk ◽  
Lorenz Curve ◽  
Term Structures ◽  
Risk Characteristics ◽  
Retail Credit ◽  
Credit Scores ◽  
Ifrs 9 ◽  
The Lorenz Curve

A new methodology to derive IFRS 9 PiT PDs is proposed. The methodology first derives a PiT term structure with accompanying segmented term structures. Secondly, the calibration of credit scores using the Lorenz curve approach is used to create account-specific PD term structures. The PiT term structures are derived by using empirical information based on the most recent default information and account risk characteristics prior to default. Different PiT PD term structures are developed to capture the structurally different default risk patterns for different pools of accounts using segmentation. To quantify what a materially different term structure constitutes, three tests are proposed. Account specific PiT PDs are derived through the Lorenz curve calibration using the latest default experience and credit scores. The proposed methodology is illustrated on an actual dataset, using a revolving retail credit portfolio from a South African bank. The main advantages of the proposed methodology include the use of well-understood methods (e.g., Lorenz curve calibration, scorecards, term structure modelling) in the banking industry. Further, the inclusion of re-default events in the proposed IFRS 9 PD methodology will simplify the development of the accompanying IFRS 9 LGD model due to the reduced complexity for the modelling of cure cases. Moreover, attrition effects are naturally included in the PD term structures and no longer require a separate model. Lastly, the PD term structure is based on months since observation, and therefore the arrears cycle could be investigated as a possible segmentation.

The Cross-Section of Expected Returns on Defaultable Assets

2017 ◽  
Vol 25 (4) ◽  
pp. 479-508
Author(s):  
Jungmu Kim ◽  
Yuen Jung Park
Keyword(s):  
Risk Factors ◽  
Term Structure ◽  
Risk Premium ◽  
Default Risk ◽  
Stock Price ◽  
Estimation Method ◽  
Credit Default Swap ◽  
Expected Returns ◽  
Accounting Data ◽  
Distress Risk

This study examines the model regarding the relation between the expected returns of defaultable asset and default risk factors utilizing CDS (credit default swap). While the previous study estimates the expected returns of CDS using stock price and accounting data, this study introduces new estimation method using CDS term structure. Two factors incorporating market-wide distress risk and recovery risk are considered as the default risk factors. The results of analyzing the Korean corporate CDS market over the sample period from September 2009 to December 2016 are as follows. First, for the expected returns of CDS, there exists the negative risk premium related to market-wide distress risk covariance. Second, the finding of this negative risk premium is robust even after the market risk factors and liquidity factor are controlled for. Third, the negative premium related to recovery risk is observed but it is not statistically significant. These results imply that the investors consider the market-wide distress risk covariance as the main risk factor when pricing the Korean corporate CDSs and they require the reward taking risk as they invest CDS with the lower distress risk covariance. Therefore, our empirical results support the model that the covariance part between individual firm's distress risk and market-wide default risk changes is not diversifiable and thus the investors require the premium for this non-diversifiable risk.

Investors drawn to high-yield emerging market bonds

2017 ◽  
Keyword(s):  
Global Financial Crisis ◽  
Emerging Market ◽  
Corporate Bond ◽  
High Yield ◽  
Content Type ◽  
Volatility Index ◽  
Investment Grade ◽  
The Global Financial Crisis ◽  
Political Turmoil ◽  
Bond Spreads

Subject Emerging market corporate bonds enter bubble territory. Significance Strong appetite for higher-yielding emerging market (EM) assets this year has compressed corporate bond spreads the most since the global financial crisis, fuelling concerns of a bubble. The sharpest compression has occurred in Asia where spreads on the Asian component of JPMorgan’s benchmark EM corporate bond index have fallen below their mid-2014 post-crisis low. Low volatility and the enduring ‘search for yield’ are underpinning demand but the scope for a correction is increasing as valuations are increasingly stretched -- particularly in Asian high-yield, and in non-investment grade bonds -- while concerns are high about China’s crackdown on financial leverage. Impacts The dollar has erased its post-election gain; it may fall more in coming weeks. The oil price has risen 10% since May 9 on rising confidence that OPEC will extend output cuts but further increases will be limited. The ‘Vix’ equities volatility index, Wall Street’s ‘fear gauge’, is close to a historic low despite the political turmoil in Washington.

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