Credit Pit Detection in Subordinate Securities: A French Perspective

  • Sfoorti Jain Envision Financial Systems Pvt. Ltd., Bangalore, India
Keywords: Artificial Neural Network, Granger causality, Credit Pit, Financial Crisis, Derivatives, Predictive Modeling


The purpose of this research is to prepare a predictive model for identifying credit crisis using an artificial neural network. The paper also aims to find out the driver and driven relationship between various financial instruments like CDS, FRA, IRS, and the Volatility index (VCAC) and government securities for France. The model, thus, is directed towards finding a threshold for credit pit events and linking various events corresponding to that dates where the threshold is breached to validate the accuracy and usefulness of the model. From the research, it is found that for France, the CDS-FRA-VCAC model derives the threshold for VCAC to indicate the probability of credit crisis or financial market crash. It is also found that sovereign bonds have a huge impact on France economy including various derivatives. This is probably why the Eurozone debt crisis impacted France much more than the 2008 financial crash.


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