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This work presents the complexity that emerges in a Bertrand duopoly between two companies in the Greek oil market, one of which is semi-public and the other is private. The game uses linear demand functions for differentiated products from the existing literature and asymmetric cost functions that arose after approaches using the published financial reports of the two oil companies (Hellenic Petroleum and Motor Oil). The game is based on the assumption of homogeneous players who are characterized by bounded rationality and follow an adjustment mechanism. The players’ decisions for each time period are expressed by two difference equations. A dynamical analysis of the game’s discrete dynamical system is made by finding the equilibrium positions and studying their stability. Numerical simulations include bifurcation diagrams and strange attractors. Lyapunov numbers’ graphs and sensitivity analysis in initial conditions prove the algebraic results and reveal the complexity and chaotic behavior of the system focusing on the two parameters k1 and k2 (speed of adjustment for each player). The d-Backtest method is applied through which an attempt is made to control the chaos that appears outside the stability space in order to return to the locally asymptotically stable Nash equilibrium for the system.

Public debt has been troubling and troubles the governments of the countries, particularly those that in the recent years had fragile economies, some of which needed support programs such as Greece, Portugal, Cyprus and Ireland or Spain and Italy. The concern of governments and European authorities was to calculate the debt, in terms of GDP, its serviceability and sustainability, and its repayment in such a way that would mitigate the risk of future default. This study aims at assessing the debt of a country by taking into account the probability of future default. It uses actuarial techniques to estimate a burning cost similar to the one of life insurance protection schemes. The approach used in this study indirectly prices the debt through the calculation of the reserve needed to avoid the default of a country.

The inactivity of banks may be the result of a number of events, such as merger & acquisition (M&A), liquidation, default-bankruptcy, etc. All these phenomena of inactivity contribute to the same result, the reform of the European banking sector and they may have the same causes. The paper will address the issue of inactivity and will try to detect its causes using econometric models. Six groups of indicators are examined: performance, size, ownership, corporate governance, capital adequacy or capital structure and loan growth. Three econometric methods (Probit, Logit, OLS) have been used to create a system that predicts inactivity. The results of the econometric models show that from the six groups of indicators, four have been found to be statistically important (performance, size, ownership, corporate governance). Two have a negative impact (ownership, corporate governance) on the probability of inactivity and two positive (performance, size). The paper’s value and innovation is that it has given a systemic approach to find indicators of inactivity and it has excluded two groups of indicators as non-statistically important (capital adequacy or capital structure and growth).

The chapter explores the relation among sovereign debt ratings, individual bank ratings and bank financial status. Credit Rating Agencies (CRA) are crucial to the function of financial markets. Two main blocks of models have been designed to investigate the above relations. The first block uses a constructed binary variable for Fitch’s ranking of European banks. The second block tries to identify the relation between financial performance and ratings. The data used for the empirical analysis, covering the period from 2004 to 2011, focus on the commercial banks of the 27 European Union countries. The statistical results show the close relation among sovereign debt, bank ratings and performance. The statistical importance of this relation is an indicator that external—exogenous factors (CRA) can play a significant role in formulating the economic environment, especially in times of crisis.

The crises of 2002 and 2008 have raised the issue of banking sector’s ability to absorb the effects of the crises. The inactivity of banks may be the result of a number of events, such as merger & acquisition (M&A), liquidation, default-bankruptcy, etc. Bankruptcy has been found to be a phenomenon that doesn’t happen often. On the contrary, M&As and liquidation are the main inactivity phenomena.The paper will address the issue of inactivity and will try to detect its causes using econometric models. Six groups of indicators are examined: performance, size, ownership, corporate governance, capital adequacy or capital structure and loan growth. Three econometric methods (Probit, Logit, OLS) have been used to create a system that predicts inactivity.The results of the econometric models show that from the six groups of indicators, four have been found to be statistically important (performance, size, ownership, corporate governance). Two have a negative impact (ownership, corporate governance) on the probability of inactivity and two positive (performance, size). This finding is consistent with the theory. The paper’s value and innovation is that it has given a systemic approach to find indicators of inactivity and it has excluded two groups of indicators as non-statistically important (capital adequacy or capital structure and growth).

The crises of 2002 and 2008 have raised the issue of banking sector’s ranking system efficiency. The credit rating agencies (CRAs) are crucial for the modern financial market. The impact that the ratings have is indisputable. This paper is seeking to find the factors that affect the ratings and to create a prediction mechanism. The originality of the paper is based on the fact that it utilizes a number of different variables (ownership status, corporate governance, etc.), and the hypothesis is innovative because it addresses the issue of divergence–convergence of the European banking sector and its respective ratings. In order to do that, all ranking evaluations have been matched with a numerical value (i.e., AAA has value 24, A- has value 18, and B- has value 9). A good grade is considered to be above A- (value 18) and a bad grade below that number. Seven groups of indicators are examined: performance, size, ownership, corporate governance, capital adequacy or capital structure, sovereign–country ranking, and loan growth. Three econometric methods (probit, logit, and OLS) have been used to create a system that predicts the rating grade. The independent variable is binary (good grade = 1/bad grade = 0). Inactive banks have been excluded from the sample. This paper provides a model for predicting the factors that affect the grade of the bank rating. Many of the factors, which other studies have pointed out, are found to be statistically important in our models, too. This paper contributes to the literature by establishing a unique framework for the European banks and identifying the factors that affect the rating grade by giving an answer to the issue of an isomorphic banking sector in Europe. It shows that there are significant differences among the various geographical regions [variables cgsys, ns, European countries with major economic problems or those under stress (Portugal, Ireland, Spain, and Greece)].