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Spatial linkages in international financial markets

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  • Viviana Fernandez

Abstract

Spatial dependency has been studied in several research areas, such as environmental criminology, economic geography, environmental sciences, and urban economics. However, it has essentially been overlooked in other subfields of economics and in the field of finance as a whole. A key element at stake is the definition of contiguity. In the context of financial markets, defining a metric distance is not a simple matter. In this article, we explore the notion of spatial dependency by formulating a spatial version of the capital asset pricing model (S-CAPM). Such a model specification makes it possible to account for alternative measures of distance between firms, such as market capitalization, the market-to-book, and other financial ratios. Our model is tested on a panel of 126 Latin American firms. In addition, we derive Value-at-Risk (VaR) measures from our S-CAPM formulation. We complement our discussion with Monte Carlo simulations aimed at quantifying the benefits of diversification in terms of VaR reduction.

Suggested Citation

  • Viviana Fernandez, 2011. "Spatial linkages in international financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 237-245.
  • Handle: RePEc:taf:quantf:v:11:y:2011:i:2:p:237-245
    DOI: 10.1080/14697680903127403
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Asgharian, Hossein & Hess, Wolfgang & Liu, Lu, 2013. "A spatial analysis of international stock market linkages," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4738-4754.
    2. Huyugüzel Kışla, Gül & Özlem Önder, A., 2018. "Spatial analysis of sovereign risks: The case of emerging markets," Finance Research Letters, Elsevier, vol. 26(C), pages 47-55.
    3. Keiler, Sebastian & Eder, Armin, 2013. "CDS spreads and systemic risk: A spatial econometric approach," Discussion Papers 01/2013, Deutsche Bundesbank.
    4. Leopoldo Catania & Anna Gloria Billé, 2017. "Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1178-1196, September.
    5. Matteo Foglia & Eliana Angelini, 2019. "The Time-Spatial Dimension of Eurozone Banking Systemic Risk," Risks, MDPI, Open Access Journal, vol. 7(3), pages 1-25, July.
    6. Tam, Pui Sun, 2014. "A spatial–temporal analysis of East Asian equity market linkages," Journal of Comparative Economics, Elsevier, vol. 42(2), pages 304-327.
    7. Mate-Sanchez, Mariluz & López Hernández, Fernando A. & Lacambra, Jesus Mur, 2012. "Analyzing long-term average adjustment of financial ratios with spatial interactions," Economic Modelling, Elsevier, vol. 29(4), pages 1370-1376.
    8. Blasques, Francisco & Koopman, Siem Jan & Lucas, Andre & Schaumburg, Julia, 2016. "Spillover dynamics for systemic risk measurement using spatial financial time series models," Journal of Econometrics, Elsevier, vol. 195(2), pages 211-223.
    9. Ou Bianling & Zhao Xin & Wang Mingxi, 2015. "Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices," Journal of Systems Science and Information, De Gruyter, vol. 3(5), pages 463-471, October.
    10. Chen, Na & Jin, Xiu & Zhuang, Xintian & Yuan, Ying, 2020. "Spatial pricing with multiple risk transmission channels and specific factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    11. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto Calogero & Pelizzon, Loriana, 2017. "The impact of network connectivity on factor exposures, asset pricing and portfolio diversification," SAFE Working Paper Series 166, Leibniz Institute for Financial Research SAFE.
    12. Ding, Dong & Sickles, Robin C., 2018. "Capital Regulation, Efficiency, and Risk Taking: A Spatial Panel Analysis of U.S. Banks," Working Papers 18-004, Rice University, Department of Economics.
    13. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    14. Matthias Arnold & Sebastian Stahlberg & Dominik Wied, 2013. "Modeling different kinds of spatial dependence in stock returns," Empirical Economics, Springer, vol. 44(2), pages 761-774, April.
    15. Girum Dagnachew Abate & Niels Haldrup, 2017. "Space-time modeling of electricity spot prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).

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