Modeling bank default intensity in the USA using autoregressive duration models

Siakoulis, Vasilios (2015): Modeling bank default intensity in the USA using autoregressive duration models.

Abstract

This paper employs a duration based approach in order to model the inter-arrival times of bank failures in the US banking system for the period 1934 - 2014. Conditional duration models that allow duration between bank failures to depend linearly or nonlinearly on its past history are estimated and evaluated. We find evidence of strong persistence along with non-monotonic hazard rates which imply a financial contagion pattern according to which, a high frequency of bank failures generates turbulence which shortly after leads to additional fails, whereas prolonged periods without abnormal events signify the absence of contagious dependence which increases the relative periods between bank failure appearance. In addition, we find that mean duration levels of tranquility spells or equivalently the bank fail events intensity is subject to long run shifts. Further, we obtain statistical significant results when we allow duration to depend linearly on past information variables that capture systemic bank crisis factors along with stock and bond market effects.

• Item Type: MPRA Paper
• Language: English
• Keywords: Autoregressive Conditional Duration; Bank Failures; Financial Contagion; Structural breaks
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C41 - Duration Analysis ; Optimal Timing Strategies; G - Financial Economics > G0 - General > G01 - Financial Crises; G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates

References

Allen, F., and Gale, D., 2000. Financial Contagion . Journal of political economy, 108, 1

Allen, D., Chan, F., McAleer, M., Peiris, S., 2008. Finite sample properties of the QMLE for the Log-ACD model Application to Australian Stocks. Journal of Econometrics 147, 163-185

Allen, D., Lazarov, Z., McAleer, M., Peiris, S., 2009. Comparison of Alternative ACD Models via Density and Interval Forecasts: Evidence from the Australian Stock Market. Mathematics and Computers in Simulation 79, 2535-2555

Bai, J., 1997. Estimating multiple breaks one at a time. Econometric Theory 13, 315-352

Bai, J., Perron, P., 2003. Computation and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics 18, 1-22

Bauwens, L., Giot, P., 2000. The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks, Annales d'Economie et de Statistique 60, 117-149

Bauwens, L., Giot, P., Grammig, J., Veredas, D., 2004. A comparison of financial duration models via density forecasts. International Journal of Forecasting 20, 589-609

Bauwens, L., Hautsch, N., 2009. Modelling Financial High Frequency Data Using Point Processes, in: Handbook of Financial Time Series, Edited by: Andersen TG, Davis RA, Kreiss JP, Mikosch T, Springer-Verlag Berlin Heidelberg, pages 953-979

Berkowitz, J., 2001, Testing density forecasts, with applications to risk management. Journal of Business & Economic Statistics 19, 4, 465-474

Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307-327

Christoffersen, P., and Pelletier, D., 2004. Backtesting value-at-risk: A duration-based approach. Journal of Financial Econometrics 2, 1, 84-108

Das, S., Duffie, D., Kapadia, N., Saita, L., 2007. Common failings: how corporate defaults are correlated. The Journal of Finance 62, 93-117

Demyanyk, Y., Hasan, I., 2010. Financial crises and bank failures: A review of prediction methods. Omega, 315-324.

Diebold, F., Gunther A., Tay, S., 1998. Evaluating density forecasts with applications to financial risk management. International Economic Review 39, 863-883

Duffie, D., Saita, L., Wang, K., 2007. Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics 83, 3, 635-665

Duffie, D., Eckner, A., Horel, G., Saita, L., 2009. Frailty Correlated Default. The Journal of Finance 64, 5, 2089-2123

Duffur, A., and Engle, R., 2000. The ACD model: Predictability of time between consecutive trades. ICMA Centre Discussion Papers in Finance 2000-05

Engle, RF., 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 4, 987-1006

Engle, RF., Russell, JR., 1998. Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica 66, 1127-1162

Fernandes, M., Grammig, J., 2006. A family of autoregressive conditional duration models. Journal of Econometrics 130, 1-23

Fischer, A., and Zurlinden, M., 2004. Are interventions self-exciting? Open Economies Review 15, 223-237

Giesecke, K., Azizpour, S., Seminar For Discussions, Ding, X., Kim, B., Mudchanatongsuk, S., 2008. Self-Exciting Corporate Defaults: Contagion vs. Frailty (2008). Working Paper, Stanford University

Giesecke, K., Longstaff, A., Schaefer, S., Strebulaev, I., 2010. Corporate bond default risk: A 150 year perspective. NBER Working Paper 15848.

Grammig, J., Maurer, K., 2000. Non-monotonic hazard functions and the Autoregressive Conditional Duration model. Econometrics Journal 3, 16-38

Hamidieh, K., Stoev, S., Michailidis, G., 2013. Intensity-based estimation of extreme loss event probability and value at risk. Applied Stochastic Models in Business and Industry 29, 3, 171-186

Hautsch, N., 2012. Econometrics of Financial High-Frequency Data. Springer-Verlag Berlin Heidelberg

Lunde, A., 1999. A generalized Gamma autoregressive conditional duration model. Working Paper, Department of Economics, Politics and Public Administration, Aalborg University, Denmark.

Mishkin, F., and White, E., 2002. U.S. Stock Market Crashes and Their Aftermath: Implications for Monetary Policy. NBER Working Paper, 8992

Pacurar, M., 2008. Autoregressive conditional duration models in finance: a survey of the theoretical and empirical literature. Journal of Economic Surveys 22, 711-751

Perron, P., Qu, Z., 2010. Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices. Journal of Business & Economic Statistics 28, 2, 275-290

Rigobon, R. 2002. International Financial Contagion: Theory and Evidence in Evolution. The Research Foundation of AIMR Working Paper (No. 08/2002).

Sun, W., Rachev, S., Fabozzi, F., Kalev, P., 2008. Fractals in trade duration: capturing long-range dependence and heavy tailedness in modeling trade duration, Annals of Finance 4, 217-241

Zhang, M., Russell, J., Tsay, R., 2001. A Nonlinear Conditional Autoregressive Duration Model with Applications to Financial Transactions Data. Journal of Econometrics 104, 179-207