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Risks in emerging markets equities: Time-varying versus spatial risk analysis

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  • Owusu Junior, Peterson
  • Alagidede, Imhotep

Abstract

For 12 emerging market economies (EMEs) time-varying and spatial risks are contrasted to assess systemic vulnerabilities that may affect equity returns as well as to aid portfolio diversification as a risk-minimising tool for Eurozone Crisis-Global Financial Crisis (EZC–GFC) and post-GFC periods. We use the Fissler & Ziegel (2016) loss (FZL) function for its desirable elicitability feature to rank competing models of time-varying tail risk estimates whereas nonparametric spatial autocorrelations, based on Tobler’s first law of geography, are applied to the Bank for International Settlement’s Global Liquidity Indicators (GLIs) to estimate time-invariant risk. In the process we have proposed the “Financial Distance” as an extension of Ghemawat’s (2001) CAGE distance dimensions. The results reveal that the overall spatial autocorrelation between the 12 EMEs is smaller and negative for post-GFC as opposed to positive and bigger for Eurozone Crisis and Global Financial Crisis (EZC–GFC) periods. Among other things, this suggests EMEs may have employed prudent liquidity policies to enhance their resilience to systemic susceptibilities having learnt bitter experiences during crisis episodes. For investors, international portfolio diversification tend to yield its expected risk-minimising outcomes during this period. We find this revelation renders irrelevant the rankings of characteristic FZL estimates for both periods since time-invariant systematic debacles have no respect for time-varying tail risks estimates of specific equities.

Suggested Citation

  • Owusu Junior, Peterson & Alagidede, Imhotep, 2020. "Risks in emerging markets equities: Time-varying versus spatial risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
  • Handle: RePEc:eee:phsmap:v:542:y:2020:i:c:s0378437119319405
    DOI: 10.1016/j.physa.2019.123474
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    as
    1. Ariu, Andrea, 2016. "Crisis-proof services: Why trade in services did not suffer during the 2008–2009 collapse," Journal of International Economics, Elsevier, vol. 98(C), pages 138-149.
    2. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, January.
    3. Luc Anselin & Sanjeev Sridharan & Susan Gholston, 2007. "Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 82(2), pages 287-309, June.
    4. M. Burzoni & I. Peri & C. M. Ruffo, 2017. "On the properties of the Lambda value at risk: robustness, elicitability and consistency," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1735-1743, November.
    5. Acharya, Viral V. & Pedersen, Lasse Heje, 2005. "Asset pricing with liquidity risk," Journal of Financial Economics, Elsevier, vol. 77(2), pages 375-410, August.
    6. Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & Maasoumi, Esfandiar & McAleer, Michael & Pérez-Amaral, Teodosio, 2019. "Choosing expected shortfall over VaR in Basel III using stochastic dominance," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 95-113.
    7. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
    8. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    9. Bank for International Settlements, 2011. "Global liquidity - concept, measurement and policy implications," CGFS Papers, Bank for International Settlements, number 45, december.
    10. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    11. Fang, Vivian W. & Noe, Thomas H. & Tice, Sheri, 2009. "Stock market liquidity and firm value," Journal of Financial Economics, Elsevier, vol. 94(1), pages 150-169, October.
    12. Zhu, Dongming & Galbraith, John W., 2010. "A generalized asymmetric Student-t distribution with application to financial econometrics," Journal of Econometrics, Elsevier, vol. 157(2), pages 297-305, August.
    13. James W. Taylor, 2019. "Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 121-133, January.
    14. Valentina Bruno & Hyun Song Shin, 2020. "Currency Depreciation and Emerging Market Corporate Distress," Management Science, INFORMS, vol. 66(5), pages 1935-1961, May.
    15. Beata Bierut, 2013. "Global liquidity as an early warning indicator of asset price booms: G5 versus broader measures," DNB Working Papers 377, Netherlands Central Bank, Research Department.
    16. Sadka, Ronnie, 2006. "Momentum and post-earnings-announcement drift anomalies: The role of liquidity risk," Journal of Financial Economics, Elsevier, vol. 80(2), pages 309-349, May.
    17. Szabolcs Blazsek & Hector Hernández, 2018. "Analysis of electricity prices for Central American countries using dynamic conditional score models," Empirical Economics, Springer, vol. 55(4), pages 1807-1848, December.
    18. Ahmed, Shaghil & Coulibaly, Brahima & Zlate, Andrei, 2017. "International financial spillovers to emerging market economies: How important are economic fundamentals?," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 133-152.
    19. Matteo Burzoni & Ilaria Peri & Chiara Maria Ruffo, 2016. "On the properties of the Lambda value at risk: robustness, elicitability and consistency," Papers 1603.09491, arXiv.org, revised Feb 2017.
    20. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "Testing for time-varying long-range dependence in volatility for emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 577-588.
    21. Carlo Acerbi & Dirk Tasche, 2002. "Expected Shortfall: A Natural Coherent Alternative to Value at Risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 31(2), pages 379-388, July.
    22. Stefan Weber, 2006. "Distribution‐Invariant Risk Measures, Information, And Dynamic Consistency," Mathematical Finance, Wiley Blackwell, vol. 16(2), pages 419-441, April.
    23. Kenourgios, Dimitris & Dimitriou, Dimitrios, 2015. "Contagion of the Global Financial Crisis and the real economy: A regional analysis," Economic Modelling, Elsevier, vol. 44(C), pages 283-293.
    24. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    25. Rodrigo Cifuentes & Hyun Song Shin & Gianluigi Ferrucci, 2005. "Liquidity Risk and Contagion," Journal of the European Economic Association, MIT Press, vol. 3(2-3), pages 556-566, 04/05.
    26. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    27. Mariano, Roberto S. & Preve, Daniel, 2012. "Statistical tests for multiple forecast comparison," Journal of Econometrics, Elsevier, vol. 169(1), pages 123-130.
    28. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    29. Johanna F. Ziegel, 2016. "Coherence And Elicitability," Mathematical Finance, Wiley Blackwell, vol. 26(4), pages 901-918, October.
    30. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    31. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    32. Douglas Dow & Amal Karunaratna, 2006. "Developing a multidimensional instrument to measure psychic distance stimuli," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 37(5), pages 578-602, September.
    33. Mollah, Sabur & Quoreshi, A.M.M. Shahiduzzaman & Zafirov, Goran, 2016. "Equity market contagion during global financial and Eurozone crises: Evidence from a dynamic correlation analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 151-167.
    34. Dietrich Domanski & Ingo Fender & Patrick McGuire, 2011. "Assessing global liquidity," BIS Quarterly Review, Bank for International Settlements, December.
    35. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    36. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    37. Rama Cont & Romain Deguest & Xuedong He, 2011. "Loss-Based Risk Measures," Papers 1110.1436, arXiv.org, revised Apr 2013.
    38. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.
    39. Tobias Fissler & Johanna F. Ziegel & Tilmann Gneiting, 2015. "Expected Shortfall is jointly elicitable with Value at Risk - Implications for backtesting," Papers 1507.00244, arXiv.org, revised Jul 2015.
    40. Mobarek, Asma & Muradoglu, Gulnur & Mollah, Sabur & Hou, Ai Jun, 2016. "Determinants of time varying co-movements among international stock markets during crisis and non-crisis periods," Journal of Financial Stability, Elsevier, vol. 24(C), pages 1-11.
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    Cited by:

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    2. Rehman, Mobeen Ur & Owusu Junior, Peterson & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Time-varying risk analysis for commodity futures," Resources Policy, Elsevier, vol. 78(C).

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    More about this item

    Keywords

    Elicitability; Time-varying risk; Time-invariant risk; Spatial autocorrelation; Global liquidity indicators;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • G1 - Financial Economics - - General Financial Markets

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