Evolution of post-crisis bank regulations and controlling tools: a systematic review from a historical aspect

This paper sheds light on the financial crisis of 2008–2010 in eleven emerging Eastern European economies (EE11): Armenia, Azerbaijan, Belarus, Bulgaria, Georgia, Kazakhstan, the Kyrgyz Republic, Moldova, Romania, Tajikistan and Ukraine. The aim is twofold. In the first place it seeks to find out if the financial instability hypothesis, as put forward by Minsky and Kindleberger, is a valid explanatory factor for the crisis. Secondly, it tries to map if general institutional frameworks of these countries were developed in order to stand against the factors leading into the financial crisis.
To answer these research problems the paper maps cycles of three parameters representing the real economy, i.e. gross domestic product, manufacturing output and unemployment and four parameters representing the financial markets, i.e. money supply, credit volumes, inflation and government debt. The cycle approach is carried out with the help of a structural time series analysis to isolate cycles in time series. The paper concludes that there were substantial positive financial cycles previous to the financial crisis mirrored by similar cycles in the real economy.
Similarly, the results show negative cycles in the same parameters during the years of crisis. It seems that an uncontrolled increase in money and credit caused the economy to overheat and thereafter contract into financial and real economy crises.
Also, the paper compiles twelve different indices of institutional development. These are standardized and presented in an institutional development matrix, showing that the general institutional framework for the eleven economies was weak previous to and under the meltdown of the economies.
The construction of an integrated institutional development index on the basis of the same twelve parameters confirms institutional shortcomings, which may have made the economies less able to guard themselves from a crisis initiated by both domestically and internationally financial instability.

The paper investigates the offshore crisis 2015–2017 and its impact on central international offshore oil and gas related maritime cluster, the Blue Maritime Cluster, located at the North-Western coast of Norway.
This complete maritime cluster, heavily involved in offshore petroleum operations, it experienced an almost devastating blow, as it lost almost one-third of its employees as its value added contracted by 39 percent.
When the crises is basically seen as a result of falling of oil prices and lower activity and squeezed profit margins, this paper investigates the crisis in the light of financial instability and reactions down the maritime supply chain.
By collecting data from the Blue Maritime Cluster and the Norwegian central company register one is able both to trace the fall in the activity due to the crisis and measures of financial strength. The study approaches the data by using a structural time series analysis in order to map cycles as deviations from polynomial trends.
The findings ascertain that financial instability was dominant within the Blue Maritime Cluster during its boom before the crisis. Debt ratios and thereby gearing (leverage) were high. Thus, the companies could not meet their obligations when the crisis hit.
The paper also finds that narrow focused supply chain management made the cluster fall deep into the abyss. Companies with a more diversified portfolio were able to meet the hard years better than others.

The article analyzes the regulatory framework in the insurance market in connection with the advent of Big Data, such as information collected from different sources that can be manipulated by new technologies. The use of Big Data offers significant opportunities to the insurance companies in terms of digitization of the distribution channels and greater knowledge of the customers, which is instrumental to a more effective identification of the individual’s risk profile, as well as improvement of the competitiveness. However, regulatory measures are needed for a proper use of Big Data in terms of respect of the individual privacy, potential discrimination and constraint on competition.