Reference Entity Research Articles

The demand for central clearing: To clear or not to clear, that is the question!

This paper empirically analyses whether post-global financial crisis regulatory reforms have created appropriate incentives to voluntarily centrally clear over-the-counter (OTC) derivative contracts. We use confidential European trade repository data on single-name sovereign credit default swap (CDS) transactions and show that both seller and buyer manage counterparty exposures and capital costs, strategically choosing to clear when the counterparty is riskier. The clearing incentives seem particularly responsive to seller credit risk, which is in line with the notion that counterparty credit risk (CCR) is asymmetric in CDS contracts. The riskiness of the underlying reference entity also impacts the decision to clear as it affects both CCR capital charges for OTC contracts and central counterparty clearing house (CCP) margins for cleared contracts. Lastly, we find evidence that when a transaction helps netting positions with the CCP and hence lower margins, the likelihood of clearing is higher.

Inflammatory Rhabdomyoblastic Tumor: Clinicopathologic and Molecular Analysis of 13 Cases

Inflammatory rhabdomyoblastic tumors (IRMTs) are newly recognized skeletal muscle tumors with uncertain malignant potential. We investigated 13 IRMTs using clinicopathologic, genetic, and epigenetic methods. The cohort included 7 men and 6 women, aged 23 to 80 years (median, 50 years), of whom 2 had neurofibromatosis type 1. Most tumors occurred in the deep soft tissues of the lower limbs, head/neck, trunk wall, and retroperitoneum/pelvis. Two tumors involved the hypopharyngeal submucosa as polypoid masses. Eight tumors showed conventional histology of predominantly spindled cells with nuclear atypia, low mitotic activity, and massive inflammatory infiltrates. Three tumors showed atypical histology, including uniform epithelioid or plump cells and mitotically active histiocytes. The remaining 2 tumors demonstrated malignant progression to rhabdomyosarcoma; one had additional IRMT histology and the other was a pure sarcoma. All 11 IRMTs without malignant progression exhibited indolent behavior at a median follow-up of 43 months. One of the 2 patients with IRMTs with malignant progression died of lung metastases. All IRMTs were positive for desmin and PAX7, whereas myogenin and MyoD1 were expressed in a subset of cases. Targeted next-generation sequencing identified pathogenic mutations in NF1 (5/8) and TP53 (4/8). All TP53 mutations co-occurred with NF1 mutations. TP53 variant allele frequency was much lower than that of NF1 in 2 cases. These tumors showed geographic (subclonal) strong p53 immunoreactivity, suggesting the secondary emergence of a TP53-mutant clone. DNA methylation–based copy number analysis conducted in 11 tumors revealed characteristic flat patterns with relative gains, including chromosomes 5, 18, 20, 21, and/or 22 in most cases. Widespread loss of heterozygosity with retained biparental copies of these chromosomes was confirmed in 4 tumors analyzed via allele-specific profiling. Based on unsupervised DNA methylation analysis, none of the 11 tumors tested clustered with existing reference entities but formed a coherent group, although its specificity warrants further study.

Counterparty Risk and Counterparty Choice in the Credit Default Swap Market

We investigate how market participants price and manage counterparty credit risk using confidential trade repository data on single-name credit default swap (CDS) transactions. We find that counterparty risk has a modest impact on the pricing of CDS contracts but a large impact on the choice of counterparties. For contracts ineligible for central clearing, we show that market participants are significantly less likely to trade with counterparties whose credit risk is highly correlated with the credit risk of the reference entities and with counterparties whose credit quality is low. For clearable contracts, we find that nondealers are more likely to clear during a crisis and less likely to clear when the reference entity is a large U.S. dealer or a sovereign. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2023.4870 .

TransAM: Transformer appending matcher for few-shot knowledge graph completion

Few-shot knowledge graph completion (FSKGC) refers to predicting new facts for a new relation with only few-shot observed entity pairs (triples) as support set. Existing solutions to FSKGC mainly conduct the matching process over entity pair representations. Although effective, a major concern of these models is that the entity interactions are not fully explored, based on the observation that they usually generate the pair representation before the matching stage. Such a design inherently overlooks the fine-grained information from entity interactions, leading to performance decrements in one or three shot, which require matching models to capture more sufficient semantic meanings for prediction. To remedy this issue, in this paper, we explore the entity interactions within and between different instances, i.e., the co-occurrence of two entities, for FSKGC and propose our model named TransAM, Transformer Appending Matcher. TransAM solves the FSKGC problem by computing the probability of entity sequence with a well-designed transformer matching network. Specifically, TransAM appends query entity pair to serialized reference entity sequence and utilizes transformer to calculate the probability by capturing intra- and inter- triple entity interactions. To bridge the gap between transformer and the triple structure, TransAM introduces rotary operation to preserve the head and tail roles of entity within the triple and distinguishes different triples by a separated triple position encoding. Empirical studies on two public benchmark datasets NELL-One and Wiki-One show that TransAM outperforms existing metric-learning solutions in MRR and Hits@1 with both one- and three- shot settings, and achieves comparable results on five-shot setting. Datasets and code will be public available at https://github.com/gawainx/TransAM.

Derivatives and ESG

Financial markets are increasingly developing innovative, ESG‐related derivatives and relying upon these instruments to hedge ESG‐related risks. The global derivatives markets are among the largest, most consequential financial markets in the world. Derivatives are financial contracts that derive their value from an underlying reference entity which can be almost anything, including interest rates, credit, equities, foreign exchange, the weather, or the price of carbon. They provide for hedging, investment (speculation), and arbitrage, and trade on regulated exchanges and in over‐the‐counter markets. Derivatives can also facilitate access to the tremendous amounts of capital necessary for the transition to a cleaner energy future and to the objective of net zero emissions by 2050 of governments around the world. Through an exploration of recent innovations and developments in the exchange‐traded and over‐the‐counter derivative markets, this article explores the role of the derivatives ecosystem—the instruments themselves, trading exchanges, and clearinghouses—in promoting ESG objectives.

Credit default swaps (CDSs): an effective tool to manage credit risk of Indian banks

PurposeCredit default swaps (CDSs) are among the most widely used credit derivatives since their innovation and designed to hedge the credit risk of reference entities. They were exposed after the global financial crisis of 2007–08, and were blamed for its occurrence. This paper aims to describe the fundamental mechanism of CDSs, demonstrating how a CDSs contract works. Further, this study explores the growth of the global and Indian CDS market by taking a holistic perspective.Design/methodology/approachAn objective-driven descriptive research design is adopted to achieve a rigorous and accurate analysis of the study. Therefore, research papers from high-impact journals have been carefully reviewed to achieve the aim of the study.FindingsThe study shows that CDSs are still in their infancy in India. Banks are the primary market makers and users in the Indian CDSs market; therefore, regulatory authorities must assist them to boost the market. For banks to become more confident, they should gain experience and knowledge from other active CDSs markets around the world.Originality/valueThis study attempts to provide insights into the current state of the global as well as the Indian CDS market. Further, this study suggests approaches for the Indian banking sector to play an active role in the Indian CDSs market.

The Effect of Government Reference Bonds on Corporate Borrowing Costs: Evidence from a Natural Experiment

Government bonds might provide reference entities that reduce corporate bond yields. We study China’s 2017 issuance of two U.S. dollar (USD)-denominated sovereign bonds when there were (effectively) no outstanding USD sovereigns. We find that Chinese corporate USD bonds experienced a four- to nine-basis-point decline in yield spreads, whereas corporate renminbi (RMB) bonds did not. The effect was stronger for corporate bonds with maturities similar to those of the USD sovereigns. Further consistent with the reference effect, USD-denominated corporate bonds experienced declines in bid-ask spreads and volatility; corporate bond yield spread changes became more sensitive to sovereign yield innovations; and the less-informed foreign mutual funds significantly increased their holdings of offshore USD Chinese corporate bonds. Limited evidence indicates that new corporate bond maturities shifted toward the sovereign bonds’ maturity after their issuance.This paper was accepted by Lukas Schmid, finance.Funding: B. Wang received financial support from Hong Kong Institute for Monetary Research. C. Y. Hong received financial support from the National Natural Science Foundation of China [Grant 72003125].Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2022.4499 .

Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)

Few-shot relation learning refers to infer facts for relations with a few observed triples. Existing metric-learning methods mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meaning and propose our model TransAM. Specifically, we serialize reference entities and query entities into sequence and apply transformer structure with local-global attention to capture intra- and inter-triple entity interactions. Experiments on two public datasets with 1-shot setting prove the effectiveness of TransAM.

Changesin the midst ofa construction network: a diachronic construction grammar approach to complex prepositions denoting internal location

AbstractLinguists have debated whether complex prepositions deserve a constituent status, but none have proposed a dynamic model that can both predict what construal a given pattern imposes and account for the emergence of non-spatial readings. This paper reframes the debate on constituency as a justification of the constructional status of complex prepositional patterns from a historical perspective. It focuses on thePrep NPILof NPlmconstruction, which denotes a relation of internal location between a located entity (a trajector) and a reference entity (a landmark). Four subschemas of the Internal Location construction are examined:middlecxn(in the middle of),centercxn(in/at the center of),heartcxn(in/at the heart of), andmidstcxn(in the midst of). All occurrences are extracted from the COHA, along with their co-occurring landmark NPs. Using vocabulary growth curves, all patterns are shown to be productive over the whole period covered by the corpus, although at different levels. Usingword2vec, a semantic vector space with the landmark collocates of each pattern is made. Curves indexed on association scores are plotted to see how densely semantic areas have been populated across four consecutive periods: 1810s–1860s, 1870s–1910s, 1920s–1970s, and 1980s–2000s. Two divisions of labor have emerged.midstcxnandheartcxnare in complementary distribution and operate mostly at the level of abstract locations whereasmiddlecxnandcentercxnare in parallel distribution and operate at the level of concrete locations.

Credit derivatives and corporate default prediction

There have been 128 defaults among U.S. CDS reference entities between 2001 and 2020. Within this sample, the five-year CDS spread is a significant predictor of corporate default in models with equity market covariates and firm attributes. This finding holds for forecast horizons up to 12 months, among financial and non-financial firms, within and without the great financial crisis, and is robust to the inclusion of corporate bond and equity options market information. A decomposition of the CDS spread into liquidity, physical default, and risk premium components shows that most of its predictive power for corporate default comes from the physical default component, both in- and out-of-sample. These results confirm the relevance of information contained in single-name CDS pricing to corporate default prediction.

Outline of a Linguistic Model for the Study of Emerging Naming

The article presents the outline of a linguistic model that is part of a methodology for identifying and analyzing emerging or referentially unstable namings, such as cultural appropriation, street harassment, climate refugee or ecocide. The model and the method are intended to guide the interpretation – manual or semi-automatic – of the referential expressions, according to the semantic-cognitive type of the designated entity (human entity, social process, event, etc.), but also taking into account interdiscursive negotiations that affect the choice of terms and their uses. The proposed approach is original and is based on several guiding ideas: (1) take into account the complexity of the naming and the entanglement of his different facets which are categorization, meaning, performativity and valuation (desirability, preferences, social norms), (2) target the development phase of the naming (observe how speakers deal with the unstable): for this purpose, we will use the notion of identification between weak or identified entities and strong or reference entities, (3) report in an integrated way the referential elaboration of knowledge, the lexical and semantic elaboration of expressions, and the expression of intersubjective attitudes. The scientific framework combines three main disciplinary areas: automatic language processing (construction and representation of knowledge, reference), semantics (elaboration of meanings) and discourse analysis (interdiscursive elaboration of concepts and terms).

The Effect of company's Interest Coverage Ratio on the Structural and Reduced-Form Models, in Predicting Credit Derivatives Price

Derivatives pricing models use either fixed or variable interest rates at the corporate level to compensate for the devaluation, which results in an estimated accounting profit caused by the cash inflation at the maturity date. These models also fail to take into account the lost opportunity costs, which are considered a deficiency. Accordingly, the present study strives to remove this problem by adding the company's Interest Coverage Ratio (ICR) to pricing models; which is the novelty of this study. The research data were extracted from the Bloomberg Terminal for an eight-year period from 2008 to 2015. The statistical population of the research includes the North American and European companies recognized as the reference entities for Credit Default Swaps (CDS) in the given period, and the statistical sample consists of 125 companies. The data were analysed using four Artificial Neural Network (ANN) algorithms, viz., ANFIS, NNARX, AdaBoost, and SVM. . The results of the research indicated the increased predictive accuracy of the pricing models under scrutiny after adding the interest coverage ratio. The findings also shed light on the superiority of the intensity model over the structural model in prognosticating the price of CDS contracts.

COVID-19 pandemic and global carbon dioxide emissions: A first assessment

Anthropogenic carbon dioxide emissions are the main cause of global climate change. The COVID-19 pandemic has been one of the worst of its kind in the last century with regard to global deaths and, in the absence of any effective treatment, it led to governments worldwide mandating lock-down measures, as well as citizens voluntarily reducing non-essential trips and activities. In this study, the influence of decreased activity on CO2 emissions and on the economy was assessed. The US, EU-28, China and India, representing almost 60% of anthropogenic carbon emissions, were considered as reference entities and the trends were extrapolated to estimate the global impact. This study aimed to deduce initial estimates of anthropogenic CO2 emissions based on the available economic and industrial outputs and activity data, as they could not be directly measured. Sector-wise variations in emissions were modeled by assuming proportionality of the outputs/activities and the resulting emissions. A decline in road traffic was seen up to March 2020 and then a steady growth was observed, with the exception of China where road traffic started to recover by the end of January. The vast majority of passenger flights were grounded and, therefore, global air traffic plummeted by 43.7% from January to May 2020. A considerable drop in coal power production and the annual industrial growth rate was also observed. The overall economic decline led to a drop of 4.9% in annual global gross domestic product (GDP) for Q2 2020. The total global CO2 emissions reduction for January through April 2020 compared to the year before was estimated to be 1749 Mt. CO2 (14.3%) with a maximum contribution from the transportation sector (58.3% among total emissions by sector). Like other previous crises, if the economy rebounds as expected the reductions will be temporary. Long-term impacts can be minimized considering the business as well as lifestyle changes for travel, utilizing virtual structures created during this crisis, and switching to sustainable transportation.

FACTOR COPULA MODEL FOR PORTFOLIO CREDIT RISK

A critical aspect in the valuation and risk management of multi-name credit derivatives is the modeling of the dependence among sources of credit risk. The dependence modeling poses difficulties in the pricing of a multi-name credit derivatives, in the estimation of the value-at-risk of a portfolio, or in the pricing of some other basket credit derivative as the description not only on the default arrival in an individual reference entity but on the default dependence among entities in the portfolio should be considered. Although the elliptical models have been widely used due to their mathematical tractability, the dependence modeling using the multi-dimensional Lévy process has shown growing interest among researchers despite its complexity. In this paper, we introduce one factor copula model for portfolio credit risk based on Normal Tempered Stable (NTS) distribution and calibrate the model through 5-year synthetic Collateralized Debt Obligation (CDO) tranche spreads under a large homogeneous portfolio approximation. The calibration results show that the one factor copula model based on NTS distribution is more flexible and provides a dependence structure fitting market CDO tranche spreads. As one of the major applications of the dependence modeling in credit risk, this model shares the advantage of the Gaussian one factor model, and all extensions and implementation methods used for it can be utilized.

Counterparty risk valuation on credit-linked notes under a Markov Chain framework

A credit-linked note (CLN) is a note paying an enhanced coupon to investors for bearing the credit risk of a reference entity. In this paper, we study the counterparty risk on CLNs under a Markov chain framework, and introduce a Markov copula model to describe joint defaults between the reference entity underlying the CLN and CLN issuer. Assuming that the respective default intensities are directly and inversely proportional to the interest rate, which follows a CIR process, we obtain the explicit formulae for CLN values through a PDE approach. Finally, credit valuation adjustment (CVA) formula is derived to price counterparty credit risk.

Is the Tone of Risk Disclosures in MD&As Relevant to Debt Markets? Evidence from the Pricing of Credit Default Swaps*

ABSTRACTThis paper examines whether the tone of corporate textual disclosures related to risk and uncertainty conveys relevant information to the credit default swap (CDS) market. Prior studies largely focus on the amount of risk disclosures and provide inconclusive evidence on the usefulness of risk disclosures for investors in assessing firm risk. Using a large sample of textual risk disclosures in the Management's Discussion and Analysis (MD&A) section of 10‐K and 10‐Q filings, I predict and find that the change in CDS spreads over the three‐day window surrounding the 10‐K/Q filing date is positively associated with the pessimism of the language used in the risk disclosures. I conduct several analyses to show that the effect of the tone of risk disclosures is distinguishable from that of the amount of such disclosures. Cross‐sectional analyses reveal that the CDS market reaction to the tone of MD&A risk disclosures is more pronounced for reference entities closer to default, consistent with creditors' particular concern about downside risk. Further, the CDS market reacts more significantly to the tone of MD&A risk disclosures for reference entities with a weaker information environment. Overall, these results support the view that the tone of textual risk disclosures in MD&As has information content for investors in the CDS market in particular and debt markets in general. My findings improve the understanding of textual risk disclosures by showing that the tone and the amount of such disclosures have different implications for debt market investors' risk perceptions.

Mutual Fund Holdings of Credit Default Swaps: Liquidity, Yield, and Risk

ABSTRACTThis study analyzes the motivations for and consequences of funds' credit default swap (CDS) investments using mutual funds' quarterly holdings from pre‐ to postfinancial crisis. Funds invest in CDS when facing unpredictable liquidity needs. Funds sell more in reference entities when the CDS is liquid relative to the underlying bonds and buy more when the CDS‐bond basis is more negative. To enhance yield, funds engage in negative basis trading and sell CDS with the highest spreads within rating categories, and with spreads higher than those of their bond portfolios. Funds with superior portfolio returns also demonstrate more skill in CDS trading.

Basket Credit Derivative Pricing in a Markov Chain Model with Interacting Intensities

In this paper, we propose a Markov chain model to price basket credit default swap (BCDS) and basket credit-linked note (BCLN) with counterparty and contagion risks. Suppose that the default intensity processes of reference entities and the counterparty are driven by a common external shock as well as defaults of other names in the contracts. The stochastic intensity of the external shock is a Cox process with jumps. We derive recursive formulas for the joint distribution of default times and obtain closed-form premium rates for BCDS and BCLN. Numerical experiments are performed to show how the correlated default risks may affect the premium rates.

The cross-country transmission of credit risk between sovereigns and firms in Asia

This paper uses Credit Default Swap (CDS) data for Asian reference entities to examine cross-country credit risk spillover effects between sovereigns and firms. Data for three East Asian countries (China, Japan and South Korea) over the period 2009–2018 are analysed. We analyse changes in the CDS spreads of a sovereign debtor and those of a foreign firm via a bivariate GARCH-full-BEKK model; thus, spillovers in mean spread changes as well as in volatility are considered. The main findings indicate that strong credit risk interdependence exists between the East Asian countries given that credit shocks from a common creditor such as Japan appear to spill over to the other two Asian nations. Compared to their non-financial counterparts, financial institutions are more sensitive than non-financial firms to changes in the credit risk of a foreign sovereign debtor; financial institutions such as banks may hold debt of foreign sovereigns which makes their CDSs sensitive to this source of credit risk.

Credit Derivatives and Corporate Default Prediction

There have been 128 defaults among U.S. CDS reference entities between 2001 and 2020. Within this sample, the five-year CDS spread is a significant predictor of corporate default in models with equity market covariates and firm attributes. This finding holds for forecast horizons up to 12 months, among financial and non-financial firms, within and without the great financial crisis, and is robust to the inclusion of corporate bond and equity options market information. A decomposition of the CDS spread into liquidity, physical default, and risk premium components shows that most of its predictive power for corporate default comes from the physical default component, both in- and out-of-sample. These results confirm the relevance of information contained in single-name CDS pricing to corporate default prediction.