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The Covid-19 pandemic has rekindled interest in sovereign debt crises amidst calls for debt relief for developing and emerging countries. But has debt relief lessened the debt burdens of emerging and developing economies? The purpose of this paper is to empirically address this question. In particular, the focus is on the implications of debt relief and institutional qualities for sovereign debt in emerging and developing economies.

The model extends the framework on the probability of default by incorporating the receipt of debt relief by a debtor country. Doing so allows to better explain movements of sovereign defaults relating to debt relief. The model is estimated via the regular probit regression.

The analysis shows that the debt relief provided, thus, far, failed to ease the debt overhang problems of developing and emerging countries and reduced investment. The current debt relief schemes may underscore the prospects of self-enforcing and self-fulfilling sovereign debt crises rather than eliminating the dilemma completely. Regarding the forms of debt relief, the analysis shows that debt forgiveness offers favourable prospects in terms of debt sustainability and economic outcomes than debt rescheduling. Perhaps, the sovereign debt crises, particularly in low-income countries, hinge on insolvency problems rather than transitory illiquidity issues.

Any debt relief mechanism should consider seriously the potential incentive effect that reinforces expectations of future debt-relief initiatives. Importantly, solving the sovereign debt problem requires a programme for sustained investment and economic growth, while not discounting the critical role of prudent debt management policies and institutions.

This study contributes a different angle to the debate on sovereign debt distress. Aside from the structural and economic factors, this study investigates the role of debt management policy in the debtor nation and the implications of debt relief benefits for sovereign risk. The framework also focuses on whether the different forms of debt relief exert distinctive impacts.

This paper aims to provide an overview, a classification of existing research groups for correlated default models using a reduced-form method and an identification of future research opportunities in the field.

A systematic literature review is used for the identification, selection, evaluation and synthesis of relevant literature using keywords regarding the reduced-form default models in the Web of Science database. The authors also add articles from cross-referencing and expert recommendations to the literature. HistCite^™ program is used to generate a citation map of the literature.

The results show that reduced-form correlated default risk models are developing towards modelling credit risk with both observable and unobservable variables. The frailty correlated default model at the firm level is still a potential research field.

This is the first paper systematically reviewing the research on reduced-form models of default timing.

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.

Prior research provides evidence that firms make accounting choices to avoid violation of debt covenant provisions and the resulting costs of technical default. We extend this research by asking why some firms refrain from making accounting policy changes when faced with costs of technical default. We considered two possible explanations. First, we hypothesise that these defaulting firms may lack the flexibility to make accounting changes. Second, we hypothesise that these defaulting firms may lack incentive to change accounting methods. Results confirm prior research and indicate that defaulting firms make more accounting changes than non‐defaulting firms. The decision by defaulting firms to change or not change accounting methods during the three years ending in the year of a technical default of debt covenants can be explained in part by the ability of the firm and by the incentives of the firm to make a change.

The chapter studies strategic default using an experimental approach.

The experiment considers a stochastic asset process and a loan with no down-payment. The treatments are two asset volatilities (high and low) and the absence and presence of social interactions via a direct effect on the subject's payoff.

I demonstrate that (i) people appear to follow the prediction of the strategic default model quite closely in the high asset volatility treatment, and that (ii) incorporating social interactions delays the strategic default beyond what is considered optimal.

The study tests adequately the strategic default using a novel experimental design and analyzes the neighbor's effect on that decision.

We set out, in this paper, to extend the Das and Sundaram (2000) model as a means of simultaneously considering correlated default risk structure and counter-party risk. The multinomial model established by Kamrad and Ritchken (1991) is subsequently modified in order to facilitate the development of a computational algorithm for valuing two types of active credit derivatives, credit-spread options and default baskets. From our numerical examples, we find that along with the correlated default risk, the existence of counter-party risk results in a substantially lower valuation of credit derivatives. In addition, we find that different settings of the term structure of interest rate volatility also have a significant impact on the value of credit derivatives.

Portfolio credit derivatives, such as basket credit default swaps (basket CDS), require for their pricing an estimation of the dependence structure of defaults, which is known to exhibit tail dependence as reflected in observed default contagion. A popular model with this property is the (Student's) t-copula; unfortunately there is no fast method to calibrate the degree of freedom parameter.

In this paper, within the framework of Schönbucher's copula-based trigger-variable model for basket CDS pricing, we propose instead to calibrate the full multivariate t distribution. We describe a version of the expectation-maximization algorithm that provides very fast calibration speeds compared to the current copula-based alternatives.

The algorithm generalizes easily to the more flexible skewed t distributions. To our knowledge, we are the first to use the skewed t distribution in this context.

This paper studies the pricing of collateralized debt obligation (CDO) using Monte Carlo and analytic methods. Both methods are developed within the framework of the reduced form model. One-factor Gaussian Copula is used for treating default correlations amongst the collateral portfolio. Based on the two methods, the portfolio loss, the expected loss in each CDO tranche, tranche spread, and the default delta sensitivity are analyzed with respect to different parameters such as maturity, default correlation, default intensity or hazard rate, and recovery rate. We provide a careful study of the effects of different parametric impact. Our results show that Monte Carlo method is slow and not robust in the calculation of default delta sensitivity. The analytic approach has comparative advantages for pricing CDO. We also employ empirical data to investigate the implied default correlation and base correlation of the CDO. The implication of extending the analytical approach to incorporating Levy processes is also discussed.

This chapter analyses the ability of some structural models to predict corporate bankruptcy. The study extends the existing empirical work on default risk in two ways. First, it estimates the expected default probabilities (EDPs) for a sample of bankrupt companies in the USA as a function of volatility, debt ratio, and other company variables. Second, it computes default correlations using a copula function and extracts common or latent factors that drive companies’ default correlations using a factor-analytical technique. Idiosyncratic risk is observed to change significantly prior to bankruptcy and its impact on EDPs is found to be more important than that of total volatility. Information-related tests corroborate the results of prediction-orientated tests reported by other studies in the literature; however, only a weak explanatory power is found in the widely used market-to-book assets and book-to-market equity ratio. The results indicate that common factors, which capture the overall state of the economy, explain default correlations quite well.