Measuring Financial Systemic Interconnectivity

Length: 656 words

Financial system risk regulators are charges with the responsibility of reducing the risk probability and outlining guideline policies to counter system failure shock. To achieve these, the regulators employ varying methodologies and tools designed to be detective of symptoms of system failure and to close the information gap. In order to fully comprehend impact of systemic risk, we first must analyze systemic linkages and the challenges they pose to the prevention of system failure, for the higher the financial linkages, the higher the risk and failure probability.

To asses the interconnectivity of the financial system, four main approaches outlined by the International Monetary Fund (IMF) can be employed. The approaches discussed in the IMF report titled, “Responding to the Financial Crisis and Measuring Systemic Risks (APRIL 2009)”, are comprehensible and form the foundation for measuring system risk in the financial framework. These approaches are; 1. Network approach: – not only is it necessary to analyze an individual financial firms system risk impact but also the chain of effects this firm’s relational inter linkages to other firms may have.

As Allen and Babus (2008) point out network analysis is a natural candidate to aid with this challenge, as it allows the regulator to see

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beyond the immediate “point of impact” by tracking several rounds of spillovers likely to arise from direct financial linkages (cited in the IMF report “Responding to the Financial Crisis and Measuring Systemic Risks”, 2009). The main challenge with this approach would be in the availability of a universal milieu across all the interlinked firms.

Although information may be available within the firms’ environment, disclosure of a firm’s internal facts may not be readily available, and if it was, there may not be a universal method of analyzing it. To adequately analyze one firm’s failure’s repo effect on other firms its directly inter linked to, regulators simulate a financial system risk shock on the firm that then triggers simulation of how this shock may affect the network of firms. The diagram below gives a basic outline of this process.

Figure 1: The effect of interconnectivity in financial system risk exposure 2. Core-risk model: – though critical analysis may be achieved using the network approach that analyzes interbank linkages, deeper analysis is needed on the broader financial networks direct and indirect to the financial firms. Information extracted in this manner is based on the core-risk model that seeks to analyze the effect of one firm’s system risk perception to other firms whether directly or indirectly linked to it.

As pointed out by Brunnermeier and others (2009, p. 5), “It may be that the best way to assess the implications of endogenous risk is via new endogenous co-risk measures that measure the increase in overall risk after conditioning on the fact that one bank is in trouble. ” (cited in the IMF report “Responding to the Financial Crisis and Measuring Systemic Risks”, 2009). When two or more companies or firms hold a similar asset, indirect linkages arise when one firm sells its assets creating a decline in value of the other firms’ similar assets.

3. Distress Dependency Matrix: – with the core-risk model defining risk through direct and indirect financial firm interconnections, the distress dependency matrix pairs two firms and analyzes their conditional probability of failure. These matrix studies groups of paired institutions and how one pair would affect the others’ system risk. As the IMF staff indicate, “… it is possible to estimate the probability a financial institution experiencing distress conditional on another institution being in distress.

” (IMF report “Responding to the Financial Crisis and Measuring Systemic Risks”, 2009). 4. Default Intensity Model: – this model is structured to give the probable effect of system risk based on the direct and indirect linkages of large groups or fractions of financial firms. It may play a role in analysis clusters of financial institutions grouped according to country or region or financial service offering to reveal the effect of each group of firms on the economy.

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