A new financial stress index model based on support vector regression and control chart

References

• H. Abdi and L.J. Williams, Principal component analysis, Wiley Interdisciplinary Reviews: Comput. Statist. 2 (2010), pp. 433–459.
   Google Scholar
• B.S. Ahn, S.S. Cho, and C.Y. Kim, The integrated methodology of rough set theory and artificial neural network for business failure prediction, Expert Syst. Appl. 18 (2000), pp. 65–74. doi: 10.1016/S0957-4174(99)00053-6
   Web of Science ®Google Scholar
• L.C. Alwan and H.V. Roberts, Time series modeling for statistical process control, J. Bus. Econ. Stat. 6 (1988), pp. 87–95.
   Web of Science ®Google Scholar
• L.H. Chen and H.D. Hsiao, Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study, Expert Syst. Appl. 35 (2008), pp. 1145–1155. doi: 10.1016/j.eswa.2007.08.010
   Web of Science ®Google Scholar
• L.M. Dekkers, J.H.J. Einmahl, and L. De Haan, A moment estimator for the index of an extreme-value distribution, Ann. Statist. 17 (1989), pp. 1833–1855. doi: 10.1214/aos/1176347397
   Web of Science ®Google Scholar
• A. Dridi, M. El Ghourabi, and M. Limam, On monitoring the financial stress index with extreme value theory, Quant. Financ. 12(3) (2012), pp. 329–339. doi: 10.1080/14697681003792229
   Web of Science ®Google Scholar
• B. Eichengreen, A.K. Rose, and C. Wyplosz, Exchange market mayhem: The antecedents and aftermath of speculative attacks, Econ. Policy: A European Forum 21 (1995), pp. 249–312.
   Google Scholar
• B. Eichengreen, A.K. Rose, and C. Wyplosz, Contagious currency crises: First tests, Scand. J. Econ. 98(4) (1996), pp. 463–484. doi: 10.2307/3440879
   Web of Science ®Google Scholar
• P. Embrechts, C. Klüppelberg, and T. Mikosch, Modeling Extremal Events for Insurance and Finance, Springer, Berlin, 1997.
   Google Scholar
• Federal Reserve Bank of St. Louis, 2011.
   Google Scholar
• Federal Reserve System Reports. Available at http://www.federalreserve.govhttp://www.federalreserve.gov.
   Google Scholar
• M.E. Ghourabi and M. Limam, Residual responses to change patterns of autocorrelated processes, J. Appl. Stat. 34(7) (2007), pp. 785–798. doi: 10.1080/02664760701240063
   Web of Science ®Google Scholar
• G.R.J. Kalb, P. Kofman, and T.C.F. Vorst, Mixtures of tails in clustered automobile collision claims, Insur. Math. Econ. 18 (1996), pp. 89–107. doi: 10.1016/0167-6687(95)00029-1
   Web of Science ®Google Scholar
• G. Kaminsky and C. Reinhart, The twin crisis: The causes of banking and balance of payment problems, Am. Econ. Rev. 89(3) (1999), pp. 473–500. doi: 10.1257/aer.89.3.473
   Web of Science ®Google Scholar
• P. Kumar and V. Ravi, Bankruptcy prediction in banks and firm via statistical and intelligent techniques, Eur. J. Oper. Res. 180 (2007), pp. 1–28. doi: 10.1016/j.ejor.2006.08.043
   Web of Science ®Google Scholar
• M. Loretan and P.C.B. Phillips, Testing the covariance stationarity of heavy-tailed time series, J. Empir. Financ. 1 (1994), pp. 211–248. doi: 10.1016/0927-5398(94)90004-3
   Google Scholar
• T.E. McKee and T. Lensberg, Genetic programming and rough sets: A hybrid approach to bankruptcy classification, Eur. J. Oper. Res. 138 (2002), pp. 436–451. doi: 10.1016/S0377-2217(01)00130-8
   Web of Science ®Google Scholar
• J.S. Mi, W.Z. Wu, and W.X. Zhang, Approaches to knowledge reduction based on variable precision rough set model, Inf. Sci. 159 (2004), pp. 255–272. doi: 10.1016/j.ins.2003.07.004
   Web of Science ®Google Scholar
• Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
   Google Scholar
• Z. Pawlak, Rough sets and intelligent data analysis, Inf. Syst. 147 (2002), pp. 1–12.
   Google Scholar
• F. Perez-Cruz and A. Artes-Rodriguez, An IRWLS procedure for nu-SVR, Proc. ICASSP'01, Salt Lake City, UT, 2001.
   Google Scholar
• F. Perez-Cruz, A. Navia-Vazquez, P.L. Alarcon-Diana, and A. Artes-Rodrıguez, An IRWLS procedure for SVR, Proc. EUSIPCO'00, Tampere, 2000.
   Google Scholar
• R. Slowinski and C. Zopounidis, Application of the rough set approach to evaluation of bankruptcy risk, Int. J. Intell. Syst. Account. Financ. Manage. 4(1) (1995), pp. 27–41. doi: 10.1002/j.1099-1174.1995.tb00078.x
   Google Scholar
• A.J. Smola and B. Schölkopf, A tutorial on support vector regression, NeuroCOLT, Technical Report NC-TR-98-030, Royal Holloway College, University of London, 1998.
   Google Scholar
• F. Telmoudi, M. El Ghourabi, and M. Limam, RST-GCBR clustering based RGA-SVM model for corporate failure prediction, J. Intell. Syst. Account. Financ. Manage. 18 (2011), pp. 105–120. doi: 10.1002/isaf.323
   Google Scholar
• M.B. Vermaat, R.J.M.M. Does, and A.G.M. Steerneman, A modified quantile estimator using extreme value theory with applications, Econ. Qual. Control 20(1) (2005), pp. 31–39. doi: 10.1515/EQC.2005.31
   Google Scholar
• T.Y. Wang, R. Ho, H.C. Wu, and L.C. Chuang, Developing a business failure prediction model via RST, GRA and CBR, Expert Syst. Appl. 36 (2009), pp. 1593–1600. doi: 10.1016/j.eswa.2007.11.068
   Web of Science ®Google Scholar
• G. Wardell, H. Moskowitz, and R.D. Plante, Run length distributions of special cause control chart for autocorrelated process, Technometrics 36 (1994a), pp. 3–27. doi: 10.1080/00401706.1994.10485393
   Web of Science ®Google Scholar
• G. Wardell, H. Moskowitz, and R.D. Plante, Run length distributions of residual control charts for autocorrelated processes, J. Qual. Technol. 26 (1994b), pp. 308–317.
   Web of Science ®Google Scholar
• C.H. Wu, G.H. Tzeng, Y.J. Goo, and W.C. Fang, A real valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy, Expert Syst. Appl. 32 (2007), pp. 397–408. doi: 10.1016/j.eswa.2005.12.008
   Web of Science ®Google Scholar