One-day-ahead forecast of state of turbulence based on today's economic situation

Authors

  • Marcin Chlebus University of Warsaw

DOI:

https://doi.org/10.24136/eq.2018.018

Keywords:

forecasting, state of turbulence, regime switching, risk management, risk measure, market risk

Abstract

Research background: In the literature little discussion was made about predicting state of time series in daily manner. The ability to recognize the state of a time series gives, for example, an opportunity to measure the level of risk in a state of tranquility and a state of turbulence independently, which can provide more accurate measurements of the market risk in a financial institution.

Purpose of the article: The aim of article is to find an appropriate tools to predict, based on today's economic situation, the state, in which time series of financial data will be tomorrow.

Methods: This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (Logit, Probit, Cloglog), 4 definitions of a dependent variable (1%, 5%, 10%, 20% of worst realization of returns), 3 sets of independent variables (un-transformed data, PCA analysis and factor analysis). Additionally, an optimal cut-off point analysis is performed. The evaluation of the models was based on the LR test, Hosmer-Lemeshow test, Gini coefficient analysis and CROC criterion based on the ROC curve. The analyses were performed for 43 individual shares and 5 portfolios of shares quoted on the Warsaw Stock Exchange. The study has been conducted for the period from 1 January 2006 to 31 January 2012.

Findings & Value added: Six combinations of assumptions have been chosen as appropriate (any model for a binary variable, the dependent variable defined as 5% or 10% of worst realization of returns, untransformed data, 5% or 10% cut-off point respectively). Models built on these assumptions meet all the formal requirements and have a high predictive and discriminant ability to one-day-ahead forecast of state of turbulence based on today's economic situation.

Downloads

Download data is not yet available.

References

Allison, P. (2005). Logistic regression using SAS: theory and application. Cary, NC: John Wiley & Sons.
View in Google Scholar

Anderson, R. (2007). The credit scoring toolkit: theory and practice for retail credit risk management and decision automation. Oxford University Press.
View in Google Scholar

Beckmann, D., Menkhoff, L., & Sawischlewski, K. (2006). Robust lessons about practical early warning systems. Journal of Policy Modeling, 28(2). doi: 10.1016/j.jpolmod.2005.10.002.

DOI: https://doi.org/10.1016/j.jpolmod.2005.10.002
View in Google Scholar

Bussiere, M., & Fratzscher., M. (2008). Low probability, high impact: policy making and extreme events. Journal of Policy Modeling, 30(1). doi: 10.1016/ j.jpolmod.2007.03.007.

DOI: https://doi.org/10.1016/j.jpolmod.2007.03.007
View in Google Scholar

Bussiere, M., & Fratzscher., M. (2006). Towards a new early warning system of financial crises. Journal of International Money and Finance, 25(6). doi: 10.1016/j.jimonfin.2006.07.007.

DOI: https://doi.org/10.1016/j.jimonfin.2006.07.007
View in Google Scholar

Davis, P., & Karim, D. (2008). Comparing early warning systems for banking crises. Journal of Financial Stability, 4(2). doi: 10.1016/j.jfs.2007.12.004.

DOI: https://doi.org/10.1016/j.jfs.2007.12.004
View in Google Scholar

Demirguc-Kunt, A., & Detragiache, E. (1998). The determinants of banking crises in developing and developed countries. IMF Staff Papers, 45(1). doi: 10.5089/ 9781451947175.001.

DOI: https://doi.org/10.2307/3867330
View in Google Scholar

Demirguc-Kunt, A., & Detragiache, E. (2000). Monitoring banking sector fragility: a multivariate Logit approach. World Bank Economic Review, 14(2). DOI: http://dx.doi.org/10.5089/9781451947175.001.

DOI: https://doi.org/10.1093/wber/14.2.287
View in Google Scholar

Eichengreen, B., Rose, A., Wyplosz, C., Dumas, B., & Weber, A. (1995). Exchange market mayhem: the antecedents and aftermath of speculative attacks. Economic Policy, 10(21). doi: 10.2307/1344591.

DOI: https://doi.org/10.2307/1344591
View in Google Scholar

Greene, W. (2003). Econometric analysis. Prentice Hall.
View in Google Scholar

Hosmer, D., & Lemeshow, S. (2000). Applied logistic regression. John Wiley & Sons.

DOI: https://doi.org/10.1002/0471722146
View in Google Scholar

Kamin, S. (1999). The current international financial crisis: how much is new? Board of Governors of the Federal Reserve System International Finance Working Paper, 636. doi: 10.2139/ssrn.171714.

DOI: https://doi.org/10.17016/IFDP.1999.636
View in Google Scholar

Kaminsky, G., Lizondo, S., & Reinhart, C. (1998). Leading indicators of currency crises. IMF Staff Papers, 45(1). doi: 10.5089/9781451955866.001.

DOI: https://doi.org/10.2307/3867328
View in Google Scholar

Kaminsky, G. (1998). Currency and banking crises: the early warnings of distress. Board of Governors of the Federal Reserve System International Finance Working Paper, 629. doi: 10.5089/9781451858938.001.

DOI: https://doi.org/10.17016/IFDP.1998.629
View in Google Scholar

Kim, H. J. (2008). Common factor analysis versus principal component analysis: choice for symptom cluster research. Asian Nursing Research, 2(1). doi: 10.1016/s1976-1317(08)60025-0.

DOI: https://doi.org/10.1016/S1976-1317(08)60025-0
View in Google Scholar

Kim, T. Y., Hwang, C., & Lee, J. (2004). Korean economic condition indicator using a neural network trained on the 1997 crisis. Journal of Data Science, 2.

DOI: https://doi.org/10.6339/JDS.2004.02(4).158
View in Google Scholar

King, G., & Langche, Z. (2001). Logistic regression in rare events data. Political Analysis, 9. doi: 10.1093/oxfordjournals.pan.a004868.

DOI: https://doi.org/10.1093/oxfordjournals.pan.a004868
View in Google Scholar

Komulainen, T., & Lukkarila, J. (2003). What drives financial crises in emerging markets? BOFIT Discussion Papers, 5. doi: 10.2139/ssrn.1015459.

DOI: https://doi.org/10.2139/ssrn.1015459
View in Google Scholar

Nagler, J. (1994). Scobit: an alternative estimator to Logit and Probit. American Journal of Political Science, 38(1). doi: 10.2307/2111343.

DOI: https://doi.org/10.2307/2111343
View in Google Scholar

Oh, K. J., Kim, T. Y., & Kim C. (2006). An early warning system for detection of financial crisis using financial market volatility. Expert Systems, 23. doi: 10.1111/j.1468-0394.2006.00326.x. `

DOI: https://doi.org/10.1111/j.1468-0394.2006.00326.x
View in Google Scholar

Powers, D. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. Journal Of Machine Learning Technologies, 2(1).
View in Google Scholar

Steyerberg, E., Van Calster, B., & Pencina, M. (2011). Performance measures for prediction models and markers: evaluation of predictions and classifications. Revista Espanola de Cardiologia (English Edition), 64(9). doi: 10.1016/j.rec.2011.05.004.

DOI: https://doi.org/10.1016/j.rec.2011.05.004
View in Google Scholar

Studies on the validation of internal rating systems. Basel Committee on Banking Supervision: Basel. Retrieved from http://www.bis.org/publ/bcbs_wp14.pdf. (08.08.2016).
View in Google Scholar

Tasche, D. (2008). Validation of internal rating systems and PD estimates. In G. Christodoulakis & S. Satchell (Eds.). The analytics of risk model validation. Academic Press. doi: 10.1016/b978-0-7506-8158-2.x5001-x.

DOI: https://doi.org/10.1016/B978-0-7506-8158-2.X5001-X
View in Google Scholar

Youden, W. (1950). Index for rating diagnostic tests. Cancer, 3(1). doi: 10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3.

DOI: https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3
View in Google Scholar

Downloads

Published

2018-09-30

How to Cite

Chlebus, M. (2018). One-day-ahead forecast of state of turbulence based on today’s economic situation. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(3), 357–389. https://doi.org/10.24136/eq.2018.018

Issue

Section

Articles

Similar Articles

<< < 2 3 4 5 6 7 8 9 10 11 > >> 

You may also start an advanced similarity search for this article.