Anastasios Tsamis

Going back to go forward: on studying the determinants of corporate governance disclosure

Purpose The purpose of this study is to: i) review the prior empirical studies that investigate the Corporate Governance (CG) determinants and provide a synopsis and ii) explore the main factors that drive the level of CG disclosure in Greek context. Design/methodology/approach We perform an extensive review of the relevant literature and identify twenty-four papers that use various potential factors. Afterwards, we construct two different GC indices to investigate these potentials and we conduct multiple regression analysis to identify and explain these determinants. Findings The empirical analysis shows that large Greek listed firms are more likely to disclose more CG information in the CG statement (CGS). In addition, the analysis shows statistically significant association with performance related variables (such as Tobin’s q and liquidity) and CG related variables (such as independent members, board meetings and women on board). Research limitations/implications The results of the study support theor...

The impact of the Combined Code in Greek soft law

Purpose – The purpose of this paper is to examine the extent of Combined code (2010) impact in the Greek soft law (SEV code, 2011) and the adoption of an overlapping set (between the two codes) of best practice provisions in Greece. Design/methodology/approach – Content analysis was conducted to examine the similarities between the UK’s Combined code (2010) and the Greek SEV code (2011). Moreover, a sample of 219 Greek listed companies’ annual reports was analyzed, and their compliance with a specific number of provisions was evaluated. Findings – Through analyzing the content of both codes, it was found that from the total 64 provisions of the SEV code (2011), 45 were matched to at least one of the Combined codes (2010). From these 45 provisions, 26 were characterized as “in spirit” influence and 19 as “in letter”. Based on this evidence, 22 overlapping practices were selected to investigate the compliance and a quite low rate was revealed, an average percentage of 30.46 per cent. These findings indicate...

Concentration risk model for Greek bank's credit portfolio

Purpose - – The IRB capital requirements of Basel II define the minimum level of capital that the bank has to retain to cover the current risks of its portfolio. The major risk that many banks are facing is credit risk and Basel II provides an approach to calculate its capital requirement. It is well known that Pillar I Basel II approach for credit risk capital requirements does not include concentration risk. The paper aims to propose a model modifying Basel II methodology (IRB) to include name concentration risk. Design/methodology/approach - – The model is developed on data based on a portfolio of Greek companies that are financed by Greek commercial banks. Based on the initial portfolio, new portfolios were simulated having a range of different credit risk parameters. Subsequently, the credit VaR of various portfolios was regressed against the credit risk indicators such as Basel II capital requirements, modified Herfindahl Index and a non-linear model was developed. This model modifies the Pillar I IRB capital requirements model of Basel II to include name concentration risk. Findings - – As the Pillar I IRB capital requirements model of Basel II does not include concentration risk, the credit VaR calculations performed in the present work appeared to have gaps with the Basel II capital requirements. These gaps were more apparent when there was high concentration risk in the credit portfolios. The new model bridges this gap providing with a correction coefficient. Practical implications - – The credit VaR of a loan portfolio could be calculated from the bank easily, without the use of additional complicated algorithms and systems. Originality/value - – The model is constructed in such a way as to provide an approximation of credit VaR satisfactory for business loan portfolios whose risk parameters lie within the range of those in a realistic bank credit portfolio and without the application of Monte Carlo simulations.