Affine Modeling of Credit Risk, Pricing of Credit Events, and Contagion

We propose a discrete-time affine pricing model that simultaneously allows for (i) the presence of systemic entities by departing from the no-jump condition on the factors’ conditional distribution, (ii) contagion effects, and (iii) the pricing of credit events. Our affine framework delivers explicit pricing formulas for default-sensitive securities such as bonds and credit default swaps (CDSs). We estimate a euro-area multicountry version of the model and address economic questions related to the pricing of sovereign credit risk. We find that both frailty (common factors) and contagion phenomena are important to account for the joint dynamics of credit spreads. Our results also provide evidence of credit-event pricing, which is at the source of substantial credit risk premiums, even for short maturities. Finally, we extract measures of depreciation-at-default from CDSs denominated in different currencies. This paper was accepted by Kay Giesecke, finance.

High Yield Bonds

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.

Random thinning model with a truncated credit quality vulnerability factor: Application to top-down-type credit risk assessment

In the top-down approach of intensity-based credit risk modeling, a procedure called “random thinning” is needed to obtain credit event intensities for sub-portfolios. This paper presents a random thinning model incorporating a risk factor called the credit quality vulnerability factor (CQVF) to capture time-series variation in credit event occurrence in a target sub-portfolio. In particular, we propose a type of CQVF that follows truncated normal distributions specified by macroeconomic variables. Using credit event samples of Japanese firms, our empirical analysis aims to clarify the applicability and effectiveness of the proposed model to practical credit risk management. Since macroeconomic variables are included in our model, it is applicable to the macro-stress testing of portfolio credit risk management within a top-down-type framework.

Developing an Risk Signal Detection System Based on Opinion Mining for Financial Decision Support

Companies have long sought to detect financial risks and prevent crises in their business activities. Investors also have a great need to identify risks and utilize them for investment. Thus, several studies have attempted to detect financial risk. However, these studies had limitations in that various data were not exploited and diverse perspectives of the firm were not reflected. This can lead to wrong choices for investment. Thus, the purpose of this study was to propose risk signal prediction models based on firm data and opinion mining, reflecting both the perspectives of firms and investors. Furthermore, we developed a process to obtain real time firm related data and convenience visualization. To develop this process, a credit event was defined as an event that led to a critical risk of the firm. In the next step, the firm risk score was calculated for a firm having a possible credit event. This score was calculated by combining the firm activity score and opinion mining score. The firm activity score was calculated based on a financial statement and disclosure data indicator, while the opinion mining score was calculated based on a sentiment analysis of news and social data. As a result, the total firm risk grade was derived, and the risk level was proposed. These processes were developed into a system and illustrated by real firm data. The results of this study demonstrate that it is possible to derive risk signals through integrated monitoring indicators and provide useful information to users. This study can help users make decisions. It also provides users an opportunity to identify new investment momentums.

A Simulation Model for Pricing the Spread in a Credit Default Swap: Application and Analysis#

This paper explores pricing the contract of a Credit Default Swap (CDS) using a simulation model. It attempts to determine the spread value which is a periodic payment to be made by the protection buyer. It also helps in identifying the factors that should be taken into account to determine the true value of the payment which would hedge the risk in case of a credit event by the issuer of the underlying asset. The paper uses the Hull and White pricing model for creating the simulation model. This model is then applied to analyse CDSs of countries having different credit ratings. The paper using the model analyses the actual and estimated spread of the different countries and discusses the possible reasons for the same.<ins cite="mailto:Windows%20User" datetime="2018-01-07T00:55"> </ins>

Book Reviews

Caroline Weber of the University of Oregon reviews “Loan Sharks: The Birth of Predatory Lending,” by Charles R. Geisst. The Econlit abstract of this book begins: “Examines the early history of high-interest lending in the United States and demonstrates the connection between interest rates and social issues. Discusses a populist issue—resistance to “loan sharking” from the Civil War to the 1890s; a venerable practice—loan sharking and usury during the Progressive Era; the states attack—loan sharks, banks, and state interventions during the 1920s; the Crash as a credit event; and the Great Depression. Geisst is Ambassador Charles A. Gargano Professor of Finance at Manhattan College.”

The Information in Credit Default Swap Volume

This article examines the informational content of credit default swap (CDS) net notional for future stock and CDS prices. Using the information on CDS contracts registered in DTCC, a clearinghouse, I construct CDS-to-debt ratios from net notional, that is, the sum of net positive positions of all market participants, and total outstanding debt issued by the reference entity. Unlike the ratio using the sum of all outstanding CDS contracts, this ratio directly indicates how much of debt is insured with CDS and therefore, is a natural measure of investors’ concern on a credit event of the reference entity. Empirically, I find crosssectional evidence that the current increase in CDS-to-debt ratios can predict a decrease in stock prices and an increase in CDS premia of the reference firms in the next week. Greater predictability for firms with investment grade credit ratings or low CDS-to-debt ratios suggests that investors pay more attention to firms in good credit conditions than those regarded as junk or already insured considerably with CDS.

The impacts of financial crisis on sovereign credit risk analysis in Asia and Europe

In this paper, we investigate the nature of sovereign credit risk for selected Asian and European countries based on a set of sovereign CDS data over an eight-year period that includes the episode of the 2007–2008 global financial crisis. Our results indicate that there exists strong commonality in sovereign credit risk among the countries studied in this paper following the crisis. In addition, our results also show that commonality is importantly associated with both local and global financial and economic variables. However, there are markedly different impacts of the sovereign of credit risk in Asian and European countries. Specifically, we find that foreign reserve, global stock market, and volatility risk premium, affect Asian and European sovereign credit risks in the opposite direction. Lastly, we model the arrival rates of credit events as a square-root diffusion process from which a pricing model is constructed and estimated over pre- and post-crisis periods. Then the resulting model is used to decompose credit spreads into risk premium and credit-event components. For most countries in our study, credit-event components appear to weight more than risk-premiums.