Can the Probability of Being in a Crisis Phase Detect the Bursting of Speculative Bubbles?

Abdessamad OUCHEN

Abstract


The choice of our topic is due to the recurrence of financial crises. The world today is deeply unstable and subject to uncertainties and big surprises. Finance is known for two regimes: state of stability and state of crisis. Therefore, in order to understand the cyclical asymmetries in the series of yields of the main indices of the world, one has to resort to non-linear specifications that distinguish between upswings and downturns. We estimated a switching model in both states and with a specification autoregressive of order 1, the monthly first difference of the S&P 500 during the period running from December 1999 to December 2015. This model allowed us to confirm the existence of two regimes distinct on the Wall Street Stock Exchange, namely the state of crisis and that of stability. It allowed the detection of three bubbles: the dot.com bubble (1998-2000), the real estate bubble (1995-2006) and the Chinese financial bubble (2014-2015). Indeed, the probability of being in crisis phase (probability smoothing) is greater than 0.6 after the crisis of TMT (2000-2001), after the financial crisis between 2007 and 2008, after the European debt crisis in 2010 and after the Chinese financial crisis in 2015.

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References


Aglietta, Michel, and Rigot Sandra. (2009). Crise et rénovation de la finance. Paris : Odile Jacob. ISBN : 978-2738192608.

Ben Hammouda, Hakim, Oulmane Nassim, and Sadni Jallab Mustapha. (2010). Crise... Naufrage des économistes ? Louvain-la-Neuve : Groupe De Boeck. ISBN : 978-2804168377.

Bollerslev, Tim. 1986. Generalized autoregressive conditional heteroskedasticity, Journal of econometrics, Vol. 31, N°3, 307-327. DOI: 10.1016/0304-4076(86)90063-1.

Boucher, Cristophe, and Hélène Raymond. (2009). Les crises bancaires et financières. Sous la direction de De Boissieu, C. Les Systèmes financiers : mutations, crises et régulation [Banking and financial crises. Under the supervision of De Boissieu, C. Financial Systems: Mutations, Crises and Regulation]. Paris: Economica. ISBN : 978-2717853247.

Bourbonnais, Régis. (2015). Econométrie. Paris : Dunod, Paris. ISBN : 978-2100721511.

Bourbonnais, Régis, and Terraza Michel. (2008). Analyse des séries temporelles. Paris : Dunod. ISBN : 978-2100517077 .

Box, E.P., George, and Gwilym, M. Jenkins. (1978). Time Series Analysis: Forecasting and Control. San Francisco: Ed. Holden-Day. ISBN: 978-0816211043.

Damodar, Guijarati. (2004). Econométrie, Paris: De Boeck. ISBN: 978-2804146368.

De Boissieu, Christian. 2009. Les Systèmes financiers : mutations, crises et régulation, Paris : Economica. ISBN : 978-2717853247.

Engle, Robert. (1986). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom inflation. Econometrica, Vol. 50, N° 4, 987-1007 (1986), DOI: 10.2307/1912773.

Evans, George. (1991). Pitfalls in testing for Explosive Bubbles in Asset Prices, American Economic Review, Vol. 81, N°4, 922-930.

Froot, A. Kenneth, and Obstfeld, Maurice. (1991). Intrinsic Bubbles: The Case of Stock Prices, American Economic Review, Vol. 81, N°5, 1189-1214. http://www.jstor.org/stable/2006913

Granger, W.J. Clive, and Andersen, A.P. (1978). An Introduction to Bilinear Time Series Models. Gottingen: Vandenhoek and Ruprecht. (1978). ISBN: 978-1461273653.

Hamilton, D. James. (1989). A New Approach to the economic analysis of non-stationary time series and the business cycle. Econometrica, Vol. 57, N° 2, 357-384. DOI: 10.2307/1912559.

Hamilton, D. James. (1994). Time Series Analysis. New Jersey: Princeton University Press. ISBN: 978-0691042893.

Lacoste, Olivier. (2009). Comprendre les crises financières. Paris : Groupe Eyrolles. ISBN : 978-2212543223.

Le Page, Jean-Marie. (2003). Crises financières internationales et risques systémiques. Paris : De Boeck. ISBN: 978-2804144012.

Medhioub, Imad. (2007). Asymétrie des cycles économiques et changement de régimes : cas de la Tunisie, L'actualité économique, Vol.83, N°4, 529-553. DOI : 10.7202/019391.

Mignon, Valérie, and Lardic, Sandrine. (2002). Econométrie des séries temporelles macroéconomiques et financières. Paris: Economica. ISBN : 978-2717844542.

Porter-Hudak, Susan. (1991). An application of the seasonal fractionally differenced model to the monetary aggegrates. Journal of the American Statistical Association, Vol. 85, 338-344. DOI: 10.2307/2289769.

Qualha, Sandra. (2002). Modèles markoviens à changements de régimes sur le marché des actions et étude de la dynamique des asymétries des rendements. University of Montreal, Faculty Department of Economics.

Ray, K. Bonnie. (1993). Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model. International Journal of Forecasting, Vol. 9, N°2, 255-269. DOI: 10.1016/0169-2070(93)90009-C.

Terasvirta, Timo, and Anderson, M. Heather. (1992). Characterizing Non linearities in Business Cycles Using Smooth transition Autoregressive models. Journal of Applied Econometrics, Vol. 7, N° S1, 119-136. DOI: 10.1002/jae.3950070509.

Tiao, C. George, and Tsay, S. Ruey. (1994). Some advances in non-linear and adaptive modelling in Time Series, Journal of Forecasting, 13, 109-131 (1994). DOI: 10.2307/2289868.


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This work is licensed under a Creative Commons Attribution 4.0 International License.