Systemic risk models address the issue of interdependence between financial institutions and, consider how bank default risks are transmitted among banks
Big data analysis for financial risk management.
J. Big Data, no. 1 (2016): 18
A very important area of financial risk management is systemic risk modelling, which concerns the estimation of the interrelationships between financial institutions, with the aim of establishing which of them are more central and, therefore, more contagious/subject to contagion. The aim of this paper is to develop a novel systemic risk m...更多
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- An understanding of the factors related to bank failure enables regulatory authorities to supervise banks more efficiently.
- The literature on predictive models for single bank failures is relatively recent: until the 1990s most authors emphasize the absence of default risk of a bank, in the presence of a generalised expectation of state interventions.
- As a consequence of all of these aspects, the very recent years are seeing a growing body of literature on bank failures, and on systemic risks originated from such them.
- Due to its practical limitations, Merton’s model has been evolved into a reduced form, leading to a widespread diffusion of the resulting model, and the related implementation in regulatory models
- Systemic risk models address the issue of interdependence between financial institutions and, consider how bank default risks are transmitted among banks
- Background and literature review The literature on predictive models for single bank failures is relatively recent: until the 1990s most authors emphasize the absence of default risk of a bank, in the presence of a generalised expectation of state interventions
- Most research papers on bank failures are based on financial market models, that originate from the seminal paper of , in which the market value of bank assets, typically modelled as a diffusion process, is matched against bank liabilities
- We propose a framework that can estimate systemic risks with models based on two different sources: financial markets and financial tweets, and suggest a way to combine them, using a Bayesian approach
- We present the first systemic risk model based on big data, and show that such a model can shed further light on the interrelationships between financial institutions
- The authors introduce the proposal. First the authors describe a methodology able to select in advance tweets, based on the H-index proposed by Hirsch , employed to measure research impact, for which a stochastic version has been proposed by Cerchiello .
The h-index is employed in the bibliometric literature as a merely descriptive measure, that can be used to rank scientists or institutions where scientists work.
- First the authors describe a methodology able to select in advance tweets, based on the H-index proposed by Hirsch , employed to measure research impact, for which a stochastic version has been proposed by Cerchiello .
- Xn be n random variables representing the number of retweets of the Np tweets of a given twitterer.
- In the context of research impact measurement, the n random variables are the citations of the n papers of a given scientist.
- Beirlant  and Pratelli , among other contributions, assume that F is continuous, at least asymptotically, even if retweet counts have support on the integer set
- The authors believe that the proposal can be very useful to estimate systemic risk and, to individuate the most contagious/subject to contagion financial institutions.
- This because it can compare and integrate two different, albeit complementary, sources of information: market prices and twitter information.
- Another important value of the model is its capability of including in systemic risk networks institutions that are not publicly listed, using the tweet component alone: a relevant advantage for banking systems as the Eurozone one, where only 45 out of 131 of the largest banks, subject to the European Central Bank assessment of 2014, are listed
- Table1: List of considered listed Italian banks
- Table2: Taxonomy proposed and descriptive sentiment analysis
- Table3: List of selected twitterers ordered according to their T-index
- Table4: Correlation between financial and sentiment returns
- The authors acknowledge financial support from the PRIN project MISURA: multivariate models for risk assessment
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