Research article Special Issues

Forecasting turbulence in the Asian and European stock market using regime-switching models

  • Received: 23 October 2017 Accepted: 03 January 2018 Published: 01 June 2018
  • JEL Codes: C34, C53, G11, G15

  • An early warning system to timely forecast turbulences in the Asian and European stock market is proposed. To ensure comparability, the model is constructed analogously to the early warning system for the US stock market presented by Hauptmann et al. (2014). Based on the time series of discrete monthly returns of the Nikkei 225 and the EuroStoxx 50, filtered probabilities are estimated by two successive Markov-switching models with two regimes each. The market is thus separated in three states: calm, turbulent positive and turbulent negative. Subsequently, a forecasting model using logistic regression and economic input factors is selected. In an empirical asset management case study it is illustrated that the investment performance is improved when considering the signals of the established warning system. Moreover, the US, Asian and European model are compared and interdependencies are highlighted.

    Citation: Janina Engel, Markus Wahl, Rudi Zagst. Forecasting turbulence in the Asian and European stock market using regime-switching models[J]. Quantitative Finance and Economics, 2018, 2(2): 388-406. doi: 10.3934/QFE.2018.2.388

    Related Papers:

    [1] Kento Okuwa, Hisashi Inaba, Toshikazu Kuniya . Mathematical analysis for an age-structured SIRS epidemic model. Mathematical Biosciences and Engineering, 2019, 16(5): 6071-6102. doi: 10.3934/mbe.2019304
    [2] Kento Okuwa, Hisashi Inaba, Toshikazu Kuniya . An age-structured epidemic model with boosting and waning of immune status. Mathematical Biosciences and Engineering, 2021, 18(5): 5707-5736. doi: 10.3934/mbe.2021289
    [3] Toshikazu Kuniya, Hisashi Inaba . Hopf bifurcation in a chronological age-structured SIR epidemic model with age-dependent infectivity. Mathematical Biosciences and Engineering, 2023, 20(7): 13036-13060. doi: 10.3934/mbe.2023581
    [4] Gang Huang, Edoardo Beretta, Yasuhiro Takeuchi . Global stability for epidemic model with constant latency and infectious periods. Mathematical Biosciences and Engineering, 2012, 9(2): 297-312. doi: 10.3934/mbe.2012.9.297
    [5] Mostafa Adimy, Abdennasser Chekroun, Claudia Pio Ferreira . Global dynamics of a differential-difference system: a case of Kermack-McKendrick SIR model with age-structured protection phase. Mathematical Biosciences and Engineering, 2020, 17(2): 1329-1354. doi: 10.3934/mbe.2020067
    [6] Pengyan Liu, Hong-Xu Li . Global behavior of a multi-group SEIR epidemic model with age structure and spatial diffusion. Mathematical Biosciences and Engineering, 2020, 17(6): 7248-7273. doi: 10.3934/mbe.2020372
    [7] Yicang Zhou, Zhien Ma . Global stability of a class of discrete age-structured SIS models with immigration. Mathematical Biosciences and Engineering, 2009, 6(2): 409-425. doi: 10.3934/mbe.2009.6.409
    [8] Hui Cao, Dongxue Yan, Ao Li . Dynamic analysis of the recurrent epidemic model. Mathematical Biosciences and Engineering, 2019, 16(5): 5972-5990. doi: 10.3934/mbe.2019299
    [9] Wenjie Qin, Jiamin Zhang, Zhengjun Dong . Media impact research: a discrete SIR epidemic model with threshold switching and nonlinear infection forces. Mathematical Biosciences and Engineering, 2023, 20(10): 17783-17802. doi: 10.3934/mbe.2023790
    [10] Toshikazu Kuniya, Mimmo Iannelli . R0 and the global behavior of an age-structured SIS epidemic model with periodicity and vertical transmission. Mathematical Biosciences and Engineering, 2014, 11(4): 929-945. doi: 10.3934/mbe.2014.11.929
  • An early warning system to timely forecast turbulences in the Asian and European stock market is proposed. To ensure comparability, the model is constructed analogously to the early warning system for the US stock market presented by Hauptmann et al. (2014). Based on the time series of discrete monthly returns of the Nikkei 225 and the EuroStoxx 50, filtered probabilities are estimated by two successive Markov-switching models with two regimes each. The market is thus separated in three states: calm, turbulent positive and turbulent negative. Subsequently, a forecasting model using logistic regression and economic input factors is selected. In an empirical asset management case study it is illustrated that the investment performance is improved when considering the signals of the established warning system. Moreover, the US, Asian and European model are compared and interdependencies are highlighted.


    [1] Abiad A (2003) Early-warning systems: a survey and a regime-switching approach. IMF Work Paper 32.
    [2] Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19: 716–723. doi: 10.1109/TAC.1974.1100705
    [3] Barrell R, Davis P, Karim D, et al. (2010) Bank regulation, property prices and early warning systems for banking crises in OECD countries. J Bank Financ 34: 2255–2264. doi: 10.1016/j.jbankfin.2010.02.015
    [4] Baum L, Petrie T, Soules G, et al. (1970) A maximization technique occuring in the statistical analysis of probabilitistic functions of Markov chains. Ann Math Stat 41: 164–171. doi: 10.1214/aoms/1177697196
    [5] Brockwell P, Davis R (1991) Time Series: Theory and Method. 2 Eds., New York: Springer.
    [6] Chen S (2009) Predicting the bear stock market: Macroeconomic variablesas leading indicators. J Bank Financ 33: 211–223. doi: 10.1016/j.jbankfin.2008.07.013
    [7] Davis P, Karim D (2008) Comparing early warning systems for banking crises. J Financ Stabil 4: 89–120. doi: 10.1016/j.jfs.2007.12.004
    [8] Davis P, Karim D (2008) Could early warning systems have helped to predict the sub-prime crisis? Natl Inst Econ Rev 206: 35–47. doi: 10.1177/0027950108099841
    [9] Demirg¨uc-Kunt A, Detragiache E (2005) Cross-country empirical studies of systemic bank distress: a survey. IMF Working Paper 05.
    [10] Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser 39: 1–38.
    [11] Diebold F, Lee J, Weinbach G (1989) Regime switching with time-varying transition probabilities. Feder Reserv Bank Philad Work Paper 93: 183–302.
    [12] Duttagupta R, Cashin P (2008) The anatomy of banking crises. IMF Working Paper 08.
    [13] Hamilton J (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57: 357–384. doi: 10.2307/1912559
    [14] Hamilton J (1994) Time Series Analysis. Princeton: Princeton University Press.
    [15] Hauptmann J, Zagst R (2011) Systemic risk. In: Wu, D. W., Quant Financ Risk Management , Berlin: Springer, 18: 321–338.
    [16] Hauptmann J, Hoppenkamps A, Min A, et al. (2014) Forecasting market turbulence using regimeswitching models. Financ Mark Portf Management 28: 139–164. doi: 10.1007/s11408-014-0226-0
    [17] Kamin S, Schindler J, Samuel S (2001) The contribution of domestic and external factors to emerging market devaluation crises: an early warning systems approach. FRB Int Financ Discuss Paper 711.
    [18] Kaminsky G, Reinhart C (1996) The Twin Crises: The Causes of Banking and Balance-of-Payments Problems. FRB Int Financ Discuss Paper 544.
    [19] Lahiri K, Wang JG (1994) Predicting cyclical turning points with leading index in a markov switching model. J Forecasting 13: 245–263. doi: 10.1002/for.3980130302
    [20] Li WX, Chen C, French J (2015) Toward an early warning system of financial crises: What can indexfutures and options tell us? Q Rev Econ Financ 55: 87–99. doi: 10.1016/j.qref.2014.07.004
    [21] Maheu M, McCurdy T (2000) Identifying bull and bear markets in stock returns. J Bus Econ Stat 18: 100–112.
    [22] Martinez-Peria M (2002) A regime-switching approach to the study of speculative attacks: a focus on EMS crises. Empir Econom 27: 299–334. doi: 10.1007/s001810100102
    [23] Meichle M, Ranaldo A, Zanetti A (2011) Do financial variables help predict the state of the business cycle in small open economies? Evidence from Switzerland. Financ Mark Portf Manag 25: 435– 453. doi: 10.1007/s11408-011-0173-y
    [24] Shao J (2003) Mathematical Statistics. 2 Eds., New York: Springer.
    [25] Timmermann A (2000) Moments of Markov switching models. J Econ 96: 75–111. doi: 10.1016/S0304-4076(99)00051-2
    [26] Wecker WE (1979) Predicting the Turning Points of a Time Series. J Bus 52: 35–50. doi: 10.1086/296032
  • This article has been cited by:

    1. Dimitri Breda, Odo Diekmann, Stefano Maset, Rossana Vermiglio, A numerical approach for investigating the stability of equilibria for structured population models, 2013, 7, 1751-3758, 4, 10.1080/17513758.2013.789562
    2. Toshikazu Kuniya, Global Behavior of a Multi-Group SIR Epidemic Model with Age Structure and an Application to the Chlamydia Epidemic in Japan, 2019, 79, 0036-1399, 321, 10.1137/18M1205947
    3. Toshikazu Kuniya, Jinliang Wang, Hisashi Inaba, A multi-group SIR epidemic model with age structure, 2016, 21, 1531-3492, 3515, 10.3934/dcdsb.2016109
    4. Mimmo Iannelli, Fabio Milner, 2017, Chapter 10, 978-94-024-1145-4, 277, 10.1007/978-94-024-1146-1_10
    5. Toshikazu Kuniya, Stability Analysis of an Age-Structured SIR Epidemic Model with a Reduction Method to ODEs, 2018, 6, 2227-7390, 147, 10.3390/math6090147
    6. Hisashi Inaba, 2017, Chapter 6, 978-981-10-0187-1, 287, 10.1007/978-981-10-0188-8_6
    7. Xue-Zhi Li, Junyuan Yang, Maia Martcheva, 2020, Chapter 1, 978-3-030-42495-4, 1, 10.1007/978-3-030-42496-1_1
    8. Toshikazu Kuniya, Existence of a nontrivial periodic solution in an age-structured SIR epidemic model with time periodic coefficients, 2014, 27, 08939659, 15, 10.1016/j.aml.2013.08.008
    9. D.H. Knipl, G. Röst, Large number of endemic equilibria for disease transmission models in patchy environment, 2014, 258, 00255564, 201, 10.1016/j.mbs.2014.08.012
    10. Jinliang Wang, Ran Zhang, Toshikazu Kuniya, The dynamics of an SVIR epidemiological model with infection age: Table 1., 2016, 81, 0272-4960, 321, 10.1093/imamat/hxv039
    11. Toshikazu Kuniya, Hopf bifurcation in an age-structured SIR epidemic model, 2019, 92, 08939659, 22, 10.1016/j.aml.2018.12.010
    12. Toshikazu Kuniya, Structure of epidemic models: toward further applications in economics, 2021, 72, 1352-4739, 581, 10.1007/s42973-021-00094-8
    13. Toshikazu Kuniya, Hisashi Inaba, Hopf bifurcation in a chronological age-structured SIR epidemic model with age-dependent infectivity, 2023, 20, 1551-0018, 13036, 10.3934/mbe.2023581
    14. Wenxuan Li, Suli Liu, Dynamic analysis of a stochastic epidemic model incorporating the double epidemic hypothesis and Crowley-Martin incidence term, 2023, 31, 2688-1594, 6134, 10.3934/era.2023312
    15. Daisuke Fujii, Taisuke Nakata, Takeshi Ojima, Martial L Ndeffo-Mbah, Heterogeneous risk attitudes and waves of infection, 2024, 19, 1932-6203, e0299813, 10.1371/journal.pone.0299813
    16. Nurbek Azimaqin, Xianning Liu, Yangjiang Wei, Yingke Li, Explicit Formula of the Basic Reproduction Number for Heterogeneous Age‐Structured SIR Epidemic Model, 2025, 0170-4214, 10.1002/mma.10994
    17. A. Peker-Dobie, Manipulating the Hopf and Generalized Hopf Bifurcations in an Epidemic Model via Braga’s Methodology, 2025, 35, 0218-1274, 10.1142/S0218127425500932
  • Reader Comments
  • © 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5763) PDF downloads(1270) Cited by(18)

Article outline

Figures and Tables

Figures(5)  /  Tables(12)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog