The equity risk posed by the too-big-to-fail banks: a Foster–Hart estimation
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The measurement of financial risk relies on two factors: determination of riskiness by use of an appropriate risk measure; and the distribution according to which returns are governed. Wrong estimates of either, severely compromise the accuracy of computed risk. We identify the too-big-to-fail banks with the set of “Global Systemically Important Banks” (G-SIBs) and analyze the equity risk of its equally weighted portfolio by means of the “Foster–Hart risk measure”—a bankruptcy-proof, reserve based measure of risk, extremely sensitive to tail events. We model banks’ stock returns as an ARMA–GARCH process with multivariate “Normal Tempered Stable” innovations, to capture the skewed and leptokurtotic nature of stock returns. Our union of the Foster–Hart risk modeling with fat-tailed statistical modeling bears fruit, as we are able to measure the equity risk posed by the G-SIBs more accurately than is possible with current techniques.
KeywordsFinancial risk Normal Tempered Stable distribution Foster–Hart risk Value-at-Risk (VaR) Average Value-at-Risk (AVaR)
The opinions, findings, conclusions or recommendations expressed in this paper are our own and do not necessarily reflect the views of the Bank of Japan. Abhinav Anand gratefully acknowledges financial support by the Science Foundation Ireland Under Grant Number 08/SRC/FMC1389. Tiantian Li thanks financial support by the National Natural Science Foundation of China Under Grant Number 71471119. We would like to express our gratitude to Svetlozar Rachev for methodological guidance and constant encouragement without which this research would have been impossible. We also thank Sandro Brusco, Yair Tauman and Hugo Benitez-Silva; and to Xin Tang for their critical commentary on an earlier version of this work. Special thanks are in order to Bruno Badia, for suggestions for improvements on a previous draft of this working paper. All remaining flaws of course, are entirely our own.
- Admati, A., & Hellwig, M. (2013). The Bankers’ new clothes: What’s wrong with banking and what to do about it. Princeton, NJ: Princeton University Press.Google Scholar
- Alexander, S. S. (1961). Price movements in speculative markets: Trends or random walks. Industrial Management Review, 2, 7–25.Google Scholar
- Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1998). Coherent measures of risk. Mathematical Finance, 6(3), 203–228.Google Scholar
- Barndorff-Nielsen, O. E., & Shephard, N. (2001). Normal modified stable processes. Economics Series Working Papers from University of Oxford, Department of Economics 72.Google Scholar
- FinAnalytica Inc. (2014). Cognity 4.0. http://optimizer.cognity.net/.
- Haas, M., & Pigorsch, C. (2009). Financial economics, fat-tailed distributions. In R. A. Meyers (Ed.), Complex systems in finance and econometrics (pp. 308–339). New York: Springer.Google Scholar
- Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. Journal of Derivatives, 6, 6–24.Google Scholar
- Kurosaki, T., & Kim, Y. S. (2013). Sytematic risk measurement in the global banking stock market with time series analysis and CoVaR. Investment Management and Financial Innovations, 10(1), 184–196.Google Scholar
- Rachev, S. T., Schwartz, E., & Khindanova, I. (2003). Stable modeling of credit risk. In S. T. Rachev (Ed.), Handbook of heavy tailed distributions in finance. Amsterdam: North Holland.Google Scholar
- Rachev, S. T., Stoyanov, S., Biglova, A., & Fabozzi, F. (2005). An empirical examination of daily stock return distributions for US stocks. In D. Baier, R. Decker & L. Schmidt-Thieme (Eds.), Data analysis and decision support. Berlin: Springer.Google Scholar
- Rachev, S. T., & Mittnik, S. (2000). Stable Paretian models in finance. Hoboken, NJ: Wiley.Google Scholar