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The purpose of this paper is to investigate how the 401(k) auto-enrollment rate influences the size of elected contribution rates in defined contribution plans for new, young enrollees.

The authors survey 324 undergraduate students at a mid-sized Midwestern university, and compare the elected contribution rates for two groups who were randomly given two default rates: 3 and 15 percent.

The results indicate widespread evidence of the anchoring and adjusting heuristic in regards to the provided auto-enrollment rate, as the 3 percent default rate group selects a contribution rate of approximately 2 percent less than the group that was provided with the 15 percent default rate. The results also provide support to the benefits of financial education: those who were taking or had already taken a college-level finance course provide higher contribution rates by about 1.7 percent overall. Additionally, individuals with the lowest critical thinking skills elect approximately 2 percent less in annual contributions overall than those who demonstrate higher critical thinking skills.

Interestingly, all groups seem to be susceptible to the anchoring and adjustment heuristic, as the default rate plays a significant role in the elected contribution rate, regardless of an individual’s financial sophistication or critical thinking skill level. The authors hope that these findings prompt benefit plan administrators and policy-makers to reconsider default rates in their retirement plans that would allow for maximum savings and participation rates. The findings also speak in favor of developing programs that would assist enrollees with financial education and critical thinking skills that would yield better retirement savings decisions when asked to make their employee benefit selections.

The purpose of this paper is to investigate linkages between households’ expectations and credit markets in the housing crisis.

In the Markov-switching framework, the sample period is classified into high- and low-impact regimes based on impacts of expectations on default rates, and the good-time-to-buy (GTTB) index is chosen to proxy for expectations toward the housing-market dynamics.

The results suggest that in high-impact regimes, optimistic expectations are substantially associated with lower defaults for all default rates analyzed, and second mortgage defaults are more sensitive to households’ expectations than first mortgage defaults. In low-impact regimes, the GTTB index significantly influences composite and first-mortgage default rates, but its impact is insignificant for second mortgage and bankcard default rates.

The results provide compelling evidence that households’ expectations play more important roles in credit markets in turmoil periods.

Liquidity risk, i.e., the likelihood that a swap can be “sold” (i.e., assigned) may affect swap prices. This article addresses the importance of liquidity risk as a factor in the valuation of swaps, which are subject to default risk. The author presents a model for pricing these swaps by incorporating a proxy for liquidity risk. Using the model, the author finds that the effects of liquidity risk may partially offset the effects of default risk.

This article surveys available research on the contingent‐claims approach to risky debt valuation. The author describes both the structural and reduced form versions of contingent claims models and summarizes both the theoretical and empirical research in this area. Relative to the progress made in the theory of risky debt valuation, empirical validation of these models lags far behind. This survey highlights the increasing gap between the theoretical valuation and the empirical understanding of risky debt.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

Estimation of default and asset correlation is crucial for banks to manage and measure portfolio credit risk. The purpose of this paper is to find empirical relationship between the default and asset correlation with default probability, to understand the effect of systematic risk.

The authors have estimated single default and implicit asset correlations for banks and corporates in India and compare it with global scenario. This paper deduces a simple methodology to estimate the default correlations from the variance of temporal default rates. Next, the asset correlations have been estimated analytically by decomposition of variance equation in Merton's one factor risk model following approaches of Gordy and of Bluhm and Overbeck.

The authors empirically find a negative relationship between asset correlation and the probability of default using Moody's global corporate data that support Basel II internal ratings‐based (IRB) correlation prescription. However, they do not find any smooth relationship between the probability of default (PD) and asset correlation for Indian corporate. The magnitude of correlation estimates based on a large bank's internal rating‐wise default rates are much lower than what is prescribed by the Basel committee. Thus, the standardized correlation figures as assumed by the Basel Committee on Banking Supervision need to be properly calibrated by the local regulators before prescribing their banks to calculate IRB risk weighted assets.

These correlation estimates will help the regulators, insurance firms and banks to understand the linkage between counterparty default risks with the systematic factors. The findings of this paper could be used further in estimating portfolio economic capital for large corporate exposures of banks and insurance companies.

Credit derivatives continue to grow in popularity as well as complexity. While single‐name credit default swaps are still the most popular instruments, second‐generation products have become more commonplace. Second generation products are those whose payoffs are contingent on the viability of a number of firms and include instruments such as default baskets and synthetic collateralized debt obligations. The purpose of this paper is to provide a transparent and detailed account of default basket valuation along with thorough and intuitive explanations of comparative statics and the relationship between basket values and default correlation.

The paper delineates the standard approach to valuing default baskets and with its implementation examines results for two copula functions and the input assumptions which are critical to the valuation process.

It is found that the assumptions are critical to the valuation and that the copula chosen also has an impact on pricing and comparative statics.

This paper is very practical in its orientation and takes a pedagogical approach in its explanation of default baskets, the standard model, and key assumptions.

This paper fills a gap in the literature as prior works are more focused on certain enhancements or nuances of modeling basket credit derivatives while this work centers on the standard model and provides a thorough analysis and explanation of the comparative statics as well as a discussion of model limitations. This paper is ideal reading for those that seek an understanding of the modeling and risks associated with multi‐name credit derivatives.

The aim of this paper is to study the impact of equity returns volatility of reference entities on credit‐default swap rates using a new dataset from the Japanese market.

Using a copula approach, the paper models the different relationships that can exist in different ranges of behavior. It studies the bivariate distributions of credit‐default swap rates and equity return volatility estimated with GARCH (1,1) and focus on one parameter Archimedean copula.

First, the paper emphasizes the finding that pairs with higher rating present a weaker dependence coefficient and then, the impact of equity returns volatility on credit‐default swap rates is higher for the lowest rating class. Second, the dependence structure is positive and asymmetric indicating that protection sellers ask for higher credit‐default swap returns to compensate the higher credit risk incurred by low rating class.

The paper has several practical implications that are of value for financial hedgers and engineers, loan market participants, financial regulators, government regulators, central banks, and risk managers.

The paper also illustrates the potential benefits of equity returns volatility of reference entities as a proxy of default risk. These simplifications could be lifted in future research on this theme.

This paper aims to introduce mathematical models to capture the spreading of epidemics to explain the expansion of mortgage default events in the USA.

The authors use the state of infectiousness and death to represent the subsequent steps of payment elinquency and default, respectively. As the local economic structure influences regional unemployment, which is a strong driver of mortgage default, the authors model interdependencies of regional mortgage default rates through employment conditions and vicinity.

Based on a large sample between 2000 and 2014 of loan-level data, the estimation of key parameters of the model is proposed. The model’s forecast accuracy shows an above-average performance compared to well-known approaches such as linear regression or logit models.

The key findings may be useful in understanding the dynamics of mortgage defaults and its spatial spreading.