Twitter data models for bank risk contagion
| Autor: | Paolo Giudici, Giancarlo Nicola, Paola Cerchiello |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
050208 finance
Financial contagion Actuarial science Computer science business.industry Cognitive Neuroscience 05 social sciences Financial market Bayesian probability Big data 02 engineering and technology Computer Science Applications Data modeling Artificial Intelligence 0502 economics and business 0202 electrical engineering electronic engineering information engineering Systemic risk Financial modeling 020201 artificial intelligence & image processing business Network model |
| Zdroj: | Neurocomputing. 264:50-56 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2016.10.101 |
| Popis: | A very important and timely area of research in finance is systemic risk modelling, which concerns the estimation of the relationships between different financial institutions, with the aim of establishing which of them are more contagious/subject to contagion. The aim of this paper is to develop a systemic risk model which, differently from existing ones, employs not only the information contained in financial market prices, but also big data coming from financial tweets. From a methodological viewpoint, we propose a new framework, based on graphical Gaussian models, that can estimate systemic risks with stochastic network models based on two different sources: financial markets and financial tweets, and suggest a way to combine them, using a Bayesian approach. From an applied viewpoint, we present the first systemic risk model based on big data, and show that such a model can help predicting the default probability of a bank, conditionally on the others. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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