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

Abstract

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.

References

Baudchon, H. (2017). Overview of French Economy. Eco Flash - BNP Paribas. Retrieved from

economic-research.bnpparibas.com

Bianchetti, M. (2011). The Zeeman effect in astrophysics. Physica, 33(1), 102–121. https://doi.org/10.1016/0031-8914(67)90263-7

Brunnermeier, M. K. (2008). Deciphering the liquidity and credit crunch 2007-08. SSRN, 23(1), 77–100. https://doi.org/10.2139/ssrn.1317454

De Grauwe, P. (2010). The Financial Crisis and the Future of the Eurozone. Bruges European Economic Policy (BEEP) Briefing 21/2010, 21. Retrieved from http://aei.pitt.edu/58454/

Delatte, A.-L., Gex, M., & López-Villavicencio, A. (2012). Has the CDS market influenced the borrowing cost of European countries during the sovereign crisis? Journal of International Money and Finance, 31(3), 481–497. https://doi.org/10.1016/J.JIMONFIN.2011.10.008

Dickinson, E. (2008). Credit default swaps: so dear to us, so dangerous. SSRN, 1–28. https://doi.org/10.2139/ssrn.1315535

Scheicher, M., Peltonen, T., D'Errico, M. & Battison, S. (2017). How does risk flow in the credit default swap market? European Central Bank, Working Paper-2041.

Etienne, D. (2009). The current financial crisis and its effects on the French economy, 45. Retrieved from http://miun.diva-portal.org/ smash/get/ diva 2:302360/FULLTEXT01.pdf

Fernández-Villaverde, J., Garicano, L., & Santos, T. (2013). Political Credit Cycles: The Case of the Eurozone. Journal of Economic Perspectives, 27(3), 145–166. https://doi.org/10.1257/jep.27.3.145

Fontana, A., & Scheicher, M. (2010). An analysis of euro area sovereign CDS and their relation with government bonds by Alessandro Fontana working paper series no 1271/december 2010/an analysis of euro area sovereign cds and their re. ECB Working Paper, (12).

Ghosh, B. (2017). FRA-CDS-VDAX based credit crash model: A German conundrum. International Journal of Economic Research, 14(8), 221–228. https:// doi.org/10.1364/JOSAA.18.002491

Gourévitch, B., Le Bouquin-Jeannès, R., & Faucon, G. (2006). Linear and nonlinear causality between signals: methods, examples and neurophysiological applications. Biological cybernetics, 95(4), 349-369.

Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3). https://doi.org/ http:// dx.doi.org/10.1787/5js33lfw0xxn-en.

Howarth, D. (2013). France and the international financial crisis: The Legacy of State-led Finance David. Governance, 1–44.

Kalbaska, A., & Gątkowski, M. (2012). Eurozone sovereign contagion: Evidence from the CDS market (2005–2010). Journal of Economic Behavior & Organization, 83(3), 657–673. https://doi.org/ 10.1016/ J.JEBO.2012.05.010

Kolstad, M. (2013). An analysis of Eurozone sovereign credit default swap.pdf, (August 15,2013), 112.

Lane, P. R. (2012). The European sovereign debt crisis. Journal of Economic Perspectives, 26(3), 49–68. https://doi.org/10.1257/jep.26.3.49

Mehta, N. (2016). CDS bond-basis tightens as sentiment improves. Markit Commentary.

Pereda, E., Quiroga, R. Q., & Bhattacharya, J. (2005). Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology, 77(1–2), 1–37. https://doi.org/ 10.1016/j.pneurobio. 2005.10.003

Pu, X., & Zhang, J. (2012). Sovereign CDS Spreads, Volatility, and Liquidity: Evidence from 2010 German Short Sale Ban. Financial Review, 47(1), 171–197. https://doi.org/10.1111/j.1540-6288.2011 00325.x

Stulz, R. M. (2009). Credit Default Swaps and the credit crisis. SSRN, 24(1), 73–92. https://doi.org/10.2139/ssrn.1475323

Taylor, J. B. (2009). The financial crisis and the policy responses. NBER Working Paper, 23(January), 1–19. https://doi.org/ 10.1108/ 13581980 911004352

Ters, K., & Urban, J. (2016). The transmission of Euro area sovereign risk Contagion: evidence from intraday CDS and Bond markets. SSRN. https://doi.org/10.2139/ssrn.2865894

Wyplosz, C. (2010). The Eurozone in the Current Crisis. SSRN, (207). https:// doi.org/10.2139/ssrn.1589990

CONT, R. (2010). Credit default swaps and financial stability. Financial Stability Review -Banque de France.

Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/https:// doi.org/ 10.1016/ S0169-2070(97)00044-7

Published
2019-07-01