Macroprudential Policy to Manage Systemic Risk Deriving from Financial Institutions in Mongolia

Abstract

AbstractThis study presents a measurement model of systemic risk in the frontier market that fully employs financial statement data. The Financial Times Stock Exchange survey notes that frontier markets exist in more than 150 countries. The sum of systemic risk in the frontier markets can hinder economic stability in terms of a herd of risk. Frontier markets are likely to imply unpredicted systemic risk if we think based on previous lessons learned from being unable to foresee the early signals of the Lehman Brothers shock. The reasons for the rarely studied systemic risk in these markets might be that the markets are systemically unimportant, and there is a lack of publicly available data access. Therefore, I gave it my best shot to capture systemic risk in the frontier market entirely using financial statement data. It is sometimes said that the frontier market is systemically not important because of its small size; however, the research argued that the interconnection between financial institutions is highly likely to raise systemic risk even if it is small. The fact is the majority of financial institutions in developed, emerging, and frontier markets are deeply interconnected with each other via a network. For instance, the financial market in Mongolia is representative of frontier markets. As a financial regulatory aspect, financial conglomerates are increasing and deepening their interconnection in bank-dominated and underdeveloped capital markets. Hence, the intuition is to capture the systemic uncertainty behind increasing conspiracies in financial conglomerates to negatively impact financial stability. To accomplish this, the methodology measures financial institutions’ contribution to systemic risk. Financial institutions’ contribution to systemic risk can be computed by the systemic expected shortfall. The systemic expected shortfall would be dependent on the marginal expected shortfall and can be explained by financial leverage and liabilities as the predicting power increases. Moreover, financial statements are situational mirrors of financial institutions. Based on these assumptions, we empirically measured the time and cross-dimensions of systemic risk using financial statement data. Potential variables from financial statements were tested to identify variables that could forecast systemic risk. Stock returns and capital market data have been frequently experimented with in previous literature, and financial statement data of frontier markets are new for systemic risk measurement. As a result of the analysis, the systemic expected shortfall could explain cross-dimension systemic risk, which is financial institutions’ contribution to systemic risk. Subsequently, the time series of the marginal expected shortfall can forecast the amount of systemic risk in the next two periods. Eventually, macroprudential policy, a policy tool for systemic risk, can be easily developed after forecasting financial institutions’ contribution to systemic risk.KeywordsSystemic riskMacroprudential policySystemic expected shortfallMarginal expected shortfallFinancial leverageValue at riskOptimal tax