There is ongoing pressure from regulators, investors and rating agencies on financial institutions to build appropriate models that allow measurement of the different risks faced. For the financial institutions the implementation of these models is often a first step towards developing what is now often called an enterprise wide risk framework, which can support and reward management on an enterprise-wide basis by integrating all risk components. As far as credit risk is concerned it follows from Sklar’s theorem that one is able to assess the risk of the entire loan portfolio provided that the dependency structure or copula is known (as well as the marginal distributions of the individual credit losses). However, whilst the assessment of the marginal risks is now relatively well under control, fitting a copula in a credit context is a difficult exercise due to the relative scarcity of observed defaults. Therefore, most institutions limit themselves to using default correlations when assessing the dependencies and use a variance-covariance framework to asses the credit portfolio risk. It must be noted that in the literature on default correlations it is now standard to use the concept of asset correlation to discuss and to compare the different findings. Indeed, an assumption on joint asset movements (typically using a Gaussian copula) allows one to back out the implied asset correlations from the default correlations; we refer to Crouhy et al (2000) for more details.
Asset correlations are also an important component of the Basel II Accord for regulatory capital requirements of credit risk portfolios. In the Basel Committee on Banking Supervision (BCBS) document of January 2001 (BCBS (2001a), asset correlations were assumed to take a value of 20% for all obligors. The modification later that year (BCBS (2001b)) assumed that asset correlation declined with PD: for the lowest PD the asset correlation was 20% and for the highest PD the asset correlation was 10%. Then finally in the Consultative Document of 2006 asset correlations for sovereigns, banks and corporates were principally assumed to be between12% and 24%, once again depending on the probability of default. We note that for small and medium sized corporates an extra downward firm-size adjustment up to 4% is made and this brings the effective range of corporate asset correlations between 8% and 24%.
The Peace Corps did not manage its government purchase card program effectively and in accordance with the Office of Management and Budget Circular A-123 Appendix B. This occurred because Peace Corps did not ensure adequate internal controls over its Government Purchase Card Program. Specifically, Peace Corps:
As a result, Peace Corps employees purchased $3,033 of unnecessary items, did not always use the most efficient means of payment, and did not fully comply with federal purchase card and contracting regulations.
Do managers learn from the stock market? This question has recently attracted much attention among finance researchers. Indeed, understanding whether and how information flows from the stock market to companies turns out to be of paramount importance to properly appraise the impact of financial markets on the real economy. The mechanism underlying such learning from managers roots in the long-standing idea that prices aggregate diverse pieces of information via the trading activity of a myriad of different investors. As a result, market prices may embed some specific information that managers do not have yet. This new information, in turn, can guide them towards a more efficient allocation of corporate resources and hence may contribute to increase firm value.
