Credit Risk in an Economy with New Firms Arrivals

Macroeconomic determinants of credit risk: Evidence from the Eurozone

“…The use of dynamic models to measure the credit quality of firms is also proposed by Tardelli (2018), who alternatively handles a filtering approach to achieve information about the probabilistic prediction of the population of firms and about the conditional distribution of firms' distance to default. The dynamics of credit default are also modeled by Centanni, Oliva, and Tardelli (2017) by considering the effect from new firms joining the population, given that such firms enter the market and will interact with firms already in place. This last approach also emphasizes the influence of the fluctuation of macroeconomic variables on the existence of correlated changes in firms credit quality.…”

Portfolio optimization of credit risky bonds: a semi-Markov process approach

“…In 2017, Dharmaraja et al (2017) proposed a Markov chain model with catastrophe to determine the mean time of default of a risky asset. Centanni et al (2017) modelled the dynamics of defaults for a dynamic set of firms in a given time period under the Markovian assumption. They assumed that there are some observable variables such as the total number of defaults, the total number of firms operating in the market at time t and some unobservable variables such as the number of firms alive or defaulted in each class at time t. Then, they using particle filtering techniques they obtained an approximation of the distribution of unobserved variables.…”

“…Extensive numerical illustrations are given to show the applicability of the proposed model and is compared with Markov chain model proposed by Singh and Dharmaraja (2017). This article uses semi-Markov process to obtain the transition probabilities of the rating migration, how-ever, one can consider the models by Centanni et al (2017); Tardelli (2018) to obtain the dynamics of credit quality of firms and generate the path of bond returns.…”