EU banks rating assignments: Is there heterogeneity between new and old member countries?

We model EU countries’ bank ratings using financial variables and allowing for intercept and slope heterogeneity. We find that country-specific factors (in the form of heterogeneous intercepts) are a crucial determinant of ratings. Whilst “new” EU countries typically have lower ratings than “old” EU countries, after ontrolling for financial variables, all countries are found to have significantly different intercepts, which confirms our hypothesis. This intercept heterogeneity may reflect differences in country risk and the legal and regulatory framework that banks face (such as foreclosure laws). In addition, ratings may respond differently to the liquidity and operating expenses to operating income variables across countries: typically ratings are more responsive to the former and less sensitive to the latter for “new” EU countries compared with “old” EU countries

Corporate payments networks and credit risk rating

Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risks of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two properties are given, less is known about how the risk is distributed in the network and its relations with the topology. We study this problem by investigating a large proprietary dataset of payments among 2.4M Italian firms, whose credit risk rating is known. We document significant correlations between local topological properties of a node (firm) and its risk. Moreover we show the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves. This effect is observed when considering both pairs of firms and communities or hierarchies identified in the network. We leverage this knowledge to show the predictability of the missing rating of a firm using only the network properties of the associated node

EU Banks Rating Assignments: Is there Heterogeneity between New and Old Member Countries?

We model EU countries' bank ratings using financial variables and allowing for intercept and slope heterogeneity. Our aim is to assess whether "old" and "new" EU countries are rated differently and to determine whether "new" ones are assigned lower ratings, ceteris paribus, than "old" ones. We find that country-specific factors (in the form of heterogeneous intercepts) are a crucial determinant of ratings. Whilst "new" EU countries typically have lower ratings than "old" ones, after controlling for financial variables we also discover that all countries have significantly different intercepts, confirming our prior belief. This intercept heterogeneity suggests that each country's rating is assigned uniquely, after controlling for differences in financial factors, which may reflect differences in country risk and the legal and regulatory framework that banks face (such as foreclosure laws). In addition, we find that ratings may respond differently to the liquidity and operating expenses to operating income variables across countries. Typically ratings are more responsive to the former and less sensitive to the latter for "new" EU countries compared with "old" EU countries.EU countries, banks, ratings, ordered probit models, index of indicator variable

EU Banks Rating Assignments: Is there Heterogeneity between New and Old Member Countries?

We model EU countries’ bank ratings using financial variables and allowing for intercept and slope heterogeneity. Our aim is to assess whether “old” and “new” EU countries are rated differently and to determine whether “new” ones are assigned lower ratings, ceteris paribus, than “old” ones. We find that country-specific factors (in the form of heterogeneous intercepts) are a crucial determinant of ratings. Whilst “new” EU countries typically have lower ratings than “old” ones, after controlling for financial variables we also discover that all countries have significantly different intercepts, confirming our prior belief. This intercept heterogeneity suggests that each country’s rating is assigned uniquely, after controlling for differences in financial factors, which may reflect differences in country risk and the legal and regulatory framework that banks face (such as foreclosure laws). In addition, we find that ratings may respond differently to the liquidity and operating expenses to operating income variables across countries. Typically ratings are more responsive to the former and less sensitive to the latter for “new” EU countries compared with “old” EU countries.EU countries, banks, ratings, ordered probit models, index of indicator variables

Modelling the role of credit rating agencies : Do they spark off a virtuous circle?

In this paper, we propose a model of credit rating agencies using the global games framework to incorporate information and coordination problems. We introduce a refined utility function of a credit rating agency that, additional to reputation maximization, also embeds aspects of competition and feedback effects of the rating on the rated firms. Apart from hinting at explanations for several hypotheses with regard to agencies' optimal rating assessments, our model suggests that the existence of rating agencies may decrease the incidence of multiple equilibria. If investors have discretionary power over the precision of their private information, we can prove that public rating announcements and private information collection are complements rather than substitutes in order to secure uniqueness of equilibrium. In this respect, rating agencies may spark off a virtuous circle that increases the efficiency of the market outcome

A Framework for LGD Validation of Retail Portfolios

Modeling and estimating the loss given default (LGD) is necessary for banks which apply for the Internal-Ratings Based Approach for retail portfolios. To validate LGD estimations there are only very few approaches discussed in the literature. In this paper, two models for validating relative LGDs and absolute losses are developed. The validation of relative LGDs is important for risk-adjusted credit pricing and interest rate calculations. The validation of absolute losses is important to meet the capital requirements of Basel II. Both models are tested with real data of a bank. Estimations are tested for robustness with in-sample and out-of-sample tests.Loss Given Default, Validation, Retail Portfolio

Euro corporate bonds risk factors

This paper investigates the determinants of credit spread changes in Euro-denominated bonds. Because credit spread changes can be easily viewed as an excess return on corporate bonds over treasury bonds, we adopt a factor model framework, inspired by the credit risk structural approach. We try to assess the relative importance of market and idiosyncratic factors in explaining the movements in credit spreads. We adopt a heterogeneous panel with a multifactor error model and propose a two-step estimation procedure which yields consistent estimates of unobserved factors. The analysis is carried out with a panel of monthly redemption yields on a set of corporate bonds for a time span of three years. Our results suggest that the Euro corporate market is driven by observable and unobservable factors. Where the latter are identified through a consistent estimation of individual and common observable effects. We observe that the factors predicted by the structural model are not as relevant as in the case of the US market. The empirical results also suggest that an unobserved common factor has a significant role in explaining the systematic changes in credit spreads. However, contrary to the American evidence, it cannot be identified as a market factor.Euro Corporate Bonds; Cross Section Dependence; Common Correlated Effects; Yield Curve

On Sovereign Credit Migration: A Study of Alternative Estimators and Rating Dynamics

This paper investigates the finite-sample behaviour of sovereign credit migration estimators and analyzes the properties of the rating process. Through bootstrap simulations, we compare a discrete multinomial estimator and two continuous hazard rate methods which differ in that one neglects time-heterogeneity in the rating process whereas the other accounts for it. The study is based on Moody's ratings 1981-2004 for 72 industrialized and emerging economies. Hazard rate estimators yield more accurate default probabilities. The time homogeneity assumption leads to underestimating the default probability and greater migration risk is inferred upon relaxing it. There is evidence of duration dependence and downgrade momentum effects in the rating process. These findings have important implications for economic and regulatory capital allocation and for the pricing of credit sensitive instruments.Sovereign credit risk; Rating transitions, Markov chain, Time heterogeneity, Rating momentum, Duration dependence.

Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines