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This study aims to shed more light on the relationship between probability of default, investment horizons and rating classes to make decision-making processes more efficient.

Based on credit default swaps (CDS) spreads, a methodology is implemented to determine the implied default probability and the implied rating, and then to estimate the term structure of the market-implied default probability and the transition matrix of implied rating. The term structure estimation in discrete time is conducted with the Nelson and Siegel model and in continuous time with the Vasicek model. The assessment of the transition matrix is performed using the homogeneous Markov model.

The results show that the CDS-based implied ratings are lower than those based on Thomson Reuters approach, which can partially be explained by the fact that the real-world probabilities are smaller than those founded on a risk-neutral framework. Moreover, investment and sub-investment grade companies exhibit different risk profiles with respect of the investment horizons.

The originality of this study consists in determining the implied rating based on CDS spreads and to detect the difference between implied market rating and the Thomson Reuters StarMine rating. The results can be used to analyze credit risk assessments and examine issues related to the Thomson Reuters StarMine credit risk model.

This 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.

This 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.

This 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.

Many issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.

The 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.

No previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.

In fraudulent conveyance cases, plaintiffs allege that by entering into a complex leverage transaction, such as an LBO, a firm’s former owners ensured its subsequent collapse. Proving that the transaction rendered the firm insolvent may allow debtors (or their proxies) to claw back transfers made to former shareholders and others as part of the transaction.

Courts have recently questioned the robustness of the solvency evidence traditionally provided in such cases, claiming that traditional expert analyses (e.g., a discounted flow analysis) may suffer from hindsight (and other forms of) bias, and thus not reflect an accurate view of the firm’s insolvency prospects at the time of the challenged transfers. To address the issue, courts have recently suggested that experts should consider market evidence, such as the firm’s stock, bond, or credit default swap prices at the time of the challenged transaction. We review market-evidence-based approaches for determination of solvency in fraudulent conveyance cases.

We compare different methods of solvency determination that rely on market data. We discuss the pros and cons of these methods and illustrate the use of credit default swap spreads with a numerical example. Finally, we highlight the limitations of these methods.

If securities trade in efficient markets in which security prices quickly impound all available information, then such security prices provide an objective assessment of investors’ views of the firm’s future insolvency prospects at the time of challenged transfer, given contemporaneously available information. As we explain, using market data to analyze fraudulent conveyance claims or assess a firm’s solvency prospects is not as straightforward as some courts argue. To do so, an expert must first pick a particular credit risk model from a host of choices which links the market evidence (or security price) to the likelihood of future default. Then, to implement his chosen model, the expert must estimate various parameter input values at the time of the alleged fraudulent transfer. In this connection, it is important to note that each credit risk model rests on particular assumptions, and there are typically several ways in which a model’s key parameters may be empirically estimated. Such choices critically affect any conclusion about a firm’s future default prospects as of the date of an alleged fraudulent conveyance.

Simply using market evidence does not necessarily eliminate the question of bias in any analysis. The reliability of a plaintiff’s claims regarding fraudulent conveyance will depend on the reasonableness of the analysis used to tie the observed market evidence at the time of the alleged fraudulent transfer to default prospects of the firm.

There is a large body of literature in financial economics that examines the relationship between market data and the prospects of a firm’s future default. However, there is surprisingly little research tying that literature to the analysis of fraudulent conveyance claims. Our paper, in part, attempts to do so. We show that while market-based methods use the information contained in market prices, this information must be supplemented with assumptions and the conclusions of these methods critically depend on the assumption made.

The purpose of this paper is to quantify the monetary amount of relationship investment in an investment banking context, investigate the drivers behind these relationship investments and look for evidence indicating reciprocity from the clients who receive these relationship investments. Relationship marketing has been one of the dominant mantras in marketing strategy circles, yet there is a lack of empirical evidence to prove significant relationship investment and reciprocity between exchange partners.

Relationship investment as the monetary amount by which the fair value of a loan at issuance is below its par value is measured. Regression analysis is used to study the drivers of relationship investment, including relationship depth, relationship breadth and relationship potential. Finally, reciprocity is studied as the extent to which bank’s expectations are realized through future revenues.

Based on 164 loans issued by a multinational investment bank, it was found that the bank provides significant monetary benefit to its corporate clients. The amount of monetary benefit provided to each client depends on the breadth and potential of the bank-borrower relationship. The author also finds evidence suggesting that the clients reciprocated these relationship investments and the bank anticipated the reciprocity by clients.

This paper is the first to empirically show a significant monetary investment in a relationship-marketing context, with the intention of building stronger relationship with clients and earning future revenues through reciprocity.

In January 2011, during the World Economic Forum's annual meeting in Davos, Switzerland, Jason Sterling, a hedge fund manager, was conducting online research to see if he could trade on any newsworthy information emerging from the summit. Sterling's fund traded primarily in sovereign debt, and he needed to figure out if European leaders would be able to come up with a viable solution to the crisis or whether the debt crisis would lead to the default of several European nations. He knew that if a solution was not found in the coming weeks, the sovereign debt markets could be thrown into turmoil.

In 2018, the International Financial Reporting Standard 9-Financial Instruments became mandatory, effectively changing the underlying accounting principles of financial instruments. This paper systematically reviews the academic literature on the implementation effects of IFRS 9, providing a coherent picture of the state of the empirical literature on IFRS 9.

The study thrives on a systematic review approach by analyzing existing academic studies along the following three broad categories: adoption and implementation, impact on financial reporting, and risk management and provisioning. The study concludes by providing research prospects to fill the identified gaps.

We document data-related issues, forecasting uncertainties and the interaction of IFRS 9 with other regulatory standards as implementation challenges encountered. Also, we observe cross-country heterogeneity in reporting quality. Furthermore, contrary to pre-implementation expectations, we find improvement in risk management. This suggests that despite the complexities of the new regulatory standard on financial instruments, it appears to be more successful in achieving the intended objective of enhancing better market discipline and transparency rather than being a regulatory overreach.

As the literature on IFRS 9 is burgeoning, we provide state-of-the-art guidance and direction for researchers with a keen interest in the economic significance and implications of IFRS 9 adoption. The study identifies gaps in the literature that require further research, specifically, IFRS 9 adoption and firm’s hedging activities, IFRS 9 implications on non-financial firms. Lastly, existing studies are mostly focused on Europe and underscore the need for more research in under-researched jurisdictions, particularly in Asia and Africa. Also, to standard setters, policymakers and practitioners, we provide some insight to aid the formulation and application of standards.

The purpose of this paper is to aid understanding of the changes in Basel Committee on Banking Supervision (BCBS) regulatory strategies after the global financial crisis.

The author uses the credit valuation adjustment (CVA) charge reform as a test case for inquiring whether BCBS has departed from its pre-crisis facilitative regulatory strategy path. The regulatory strategy of the CVA charge is discussed.

The charge exhibits a new regulatory strategy that BCBS has adopted. It seeks to manipulate market structures by imposing risk-insensitive capital charge methodologies.

The paper offers a new heuristic to analyse regulatory initiatives and their significance. The CVA charge has not been subject to a regulatory theory-based analysis in prior literature.

A credit rating, as a single indicator on one consistent scale, is designed as an objective and comparable measure within a credit rating agency (CRA). While research focuses mainly on the comparability of ratings between agencies, this paper additionally questions empirically how CRAs meet their promise of providing a consistent assessment of credit risk for issuers within and between market segments of the same agency.

Exhaustive and robust regression analyses are run to assess the impact of market sectors and rating agencies on credit ratings. The examinations consider the rating level, as well as rating downgrades as a further measure of empirical credit risk. Data stems from a large global sample of Bloomberg ratings from 11 market sectors for the period 2010-2018.

The analyses show differing effects of sectors and agencies on issuer ratings and downgrade probabilities. Empirical results on credit ratings and rating downgrades can then be attributed to investment grade and non-investment grade ratings.

The paper contributes to current finance research and practice by examining the credit rating differences between sectors and agencies and providing assistance to investors and other stakeholders, as well as researchers, how issuers’ sector and rating agency affiliations act as relative metrics.

The implementation of credit risk models has largely relied either on the use of historical default dependence, as proxied by the correlation of equity returns, or on risk neutral equicorrelation, as extracted from CDOs. Contrary to both approaches, the purpose of this paper is to infer risk neutral dependence from CDS data, taking counterparty risk into consideration and avoiding equicorrelation. The impact of risk neutral correlation on the fees of some higher dimensional credit derivatives is also explored.

Copula functions are used in order to capture dependency. An application to market data is provided.

Both in the FtD and CDO cases, using (the correct) risk neutral measure instead of equity dependency has the same effect as the adoption of a copula with tail dependency instead of a Gaussian one. This should be important for those who resort to copulas in credit derivative pricing.

As far as is known, several attempts have been made in order to compare the behavior of different copulas in derivative pricing; however, no attempt has been made in order to extract risk neutral dependence without using the equicorrelation assumption. Therefore no attempt has been made to understand which copula features could proxy for risk neutrality, whenever risk neutral dependency cannot be inferred (for instance because CDS involving that name are not actively traded)

To implement the model described in the companion paper, “Pricing credit risk through equity options calibration, part 1 – theory,” and show how to calculate the price of a set of coupon bonds issued by a US telecommunications and media company, AOL Time Warner, based on the information retrieved by the AOL equity derivatives market.

The risk‐neutral density function of AOL Time Warner's stock is inferred from options volatilities; from there, the AOL assets risk neutral density function is calculated together with the default probabilities at different dates in the future. Finally, a set of AOL coupon bonds are priced accordingly and compared to market prices.

The AOL model‐theoretical prices are close to market prices, meaning that it is possible to perform relative‐value analysis in the risky bonds market based on the equity markets information.

The paper shows how easily the model can be used as a tool for performing relative‐value analysis between the equity options and the credit markets by using real market data.