Using Granger Causality to Determine Interconnectedness in Unlisted Banking Markets

Abstract

risk was the main reason for last global financial crisis that exposed regulators, bankers and politicians towards the inability to manage and oversee the systemic risk. According to a study done in 2013 the approximate losses from average banking crisis amount up to 23% of GDP, with 2011 crisis in Latvia amounting up to 100% of potential GDP (Peydro, Laeven and Freixas, 2015; p.29, 117). The crisis forced regulators and policy makers to take unprecedented measures like capital purchase program, setting additional capital buffers and introducing new rules for derivative business. The macroprudential policy emerged as a new supervisory field. Ability to detect and evaluate interconnectedness in the banking system has been one of the key issues when evaluating systemic risk. Other aspects attributable to the systemic risk are the largeness of the bank (too big to fail), its complexity (Banulescu and Dumitrescu, 2015) or uniqueness in performing a crucial function for the market or state. With ever increasing globalization, free capital movement and unified pan-European regulation, banking markets in Central and Eastern Europe have experienced rapid changes that particularly influence interconnectedness side of the systemic risk. Banking markets in the Baltics still remain privately owned, as market share of listed commercial banks in the region remain low-from 0% in Latvia, to 7% in Estonia and 8% in Lithuania (as per author's calculation based on national stock exchange's database on listed banks, bank financial reports and European Central Bank National account statistics on bank balance sheets (assets and capital & reserves)). Therefore, the common tools to assess interconnectivity and systemic risk are un-applicable as for input data they use either changes of the price of the share (and similar traded assets) or loans between the