As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not...

In this paper, we compare different methods for computing default probabilities using a sample of banks that experienced financial distress during the 2007–2009 global financial crisis. The traditional KMV-Merton model for firm valuation, credit ratings by rating agencies and a recently...

Changes in collateralization have been implicated in significant default (or near-default) events during the financial crisis, most notably with AIG. We have developed a framework for quantifying this effect based on moving between Merton-type and Black-Cox-type structural default models. Our...

We study financial networks and reveal a new kind of systemic risk arising from what we call default ambiguity, i.e., a situation where it is impossible to decide which banks are in default. Specifically, we study the clearing problem: given a network of banks interconnected by financial...

We examine all available 146 Proof-of-Work based cryptocurrencies that started trading prior to the end of 2014 and track their performance until December 2018. We find that about 60% of those cryptocurrencies were eventually in default. The substantial sums of money involved mean those...

Recent regulatory reforms like the mandatory clearing of standardized swap contracts and mandatory trading on centralized execution platforms have significantly changed the derivatives landscape. These reforms have, in certain cases, led the market to increasingly trade on multilateral...

The authors price corporate debt from a structural model of firm default. They assume that the capital market brings about efficient firm default when the continuation value of the firm falls below the value it would have after bankruptcy restructuring. This characterization of default makes the...

This article presents a new model for valuing a credit default swap (CDS) contract that is affected by multiple credit risks of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most...

This paper suggests using a multilayer artificial neural network (ANN) method, known as deep learning ANN, to predict the probability of default (PD) within the survival analysis framework. Deep learning ANN structures consider hidden interconnections among the covariates determining the PD...

While empirical literature has documented a negative relation between default risk and stock returns, the theory suggests that default risk should be positively priced. We provide an explanation for this "default anomaly", by calculating monthly probabilities of default (PDs) for a large sample...