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Modeling different kinds of spatial dependence in stock returns

Author

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  • Matthias Arnold
  • Sebastian Stahlberg
  • Dominik Wied

Abstract

The paper modifies previously suggested GMM approaches to spatial autoregression in stock returns. Our model incorporates global dependencies, dependencies inside industrial branches and local dependencies. As can be seen from Euro Stoxx 50 returns, this combination of spatial modeling and finance allows for superior risk forecasts in portfolio management. Copyright Springer-Verlag 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:empeco:v:44:y:2013:i:2:p:761-774
    DOI: 10.1007/s00181-011-0528-2
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Badi H. Baltagi, 2015. "Seemingly Unrelated Regressions," Springer Texts in Business and Economics, in: Solutions Manual for Econometrics, edition 3, chapter 0, pages 233-257, Springer.
    4. Badi Baltagi & Alain Pirotte, 2011. "Seemingly unrelated regressions with spatial error components," Empirical Economics, Springer, vol. 40(1), pages 5-49, February.
    5. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    6. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    7. Robert M. De Jong & James Davidson, 2000. "Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices," Econometrica, Econometric Society, vol. 68(2), pages 407-424, March.
    8. Eckel, Stefanie & Löffler, Gunter & Maurer, Alina & Schmidt, Volker, 2011. "Measuring the effects of geographical distance on stock market correlation," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 237-247, March.
    9. James P. LeSage & R. Kelley Pace, 2008. "Spatial Econometric Modeling Of Origin‐Destination Flows," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 941-967, December.
    10. 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.
    11. Lin, Xu & Lee, Lung-fei, 2010. "GMM estimation of spatial autoregressive models with unknown heteroskedasticity," Journal of Econometrics, Elsevier, vol. 157(1), pages 34-52, July.
    12. Viviana Fernandez, 2011. "Spatial linkages in international financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 237-245.
    13. Harald Badinger & Peter Egger, 2011. "Estimation of higher‐order spatial autoregressive cross‐section models with heteroscedastic disturbances," Papers in Regional Science, Wiley Blackwell, vol. 90(1), pages 213-235, March.
    14. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    15. Lee, Lung-fei & Liu, Xiaodong, 2010. "Efficient Gmm Estimation Of High Order Spatial Autoregressive Models With Autoregressive Disturbances," Econometric Theory, Cambridge University Press, vol. 26(1), pages 187-230, February.
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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. Karatetskaya Efrosiniya & Lakshina Valeriya, 2018. "Volatility Spillovers With Spatial Effects On The Oil And Gas Market," HSE Working papers WP BRP 72/FE/2018, National Research University Higher School of Economics.
    3. 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.
    4. Matteo Foglia & Eliana Angelini, 2019. "The Time-Spatial Dimension of Eurozone Banking Systemic Risk," Risks, MDPI, vol. 7(3), pages 1-25, July.
    5. Mardi Dungey & Moses Kangogo & Vladimir Volkov, 2022. "Dynamic effects of network exposure on equity markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(4), pages 569-629, December.
    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. Yun Feng & Xin Li, 2022. "The Cross-Shareholding Network and Risk Contagion from Stochastic Shocks: An Investigation Based on China’s Market," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 357-381, January.
    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. Rusmanto, Toto & Soedarmono, Wahyoe & Tarazi, Amine, 2020. "Credit information sharing in the nexus between charter value and systemic risk in Asian banking," Research in International Business and Finance, Elsevier, vol. 53(C).
    11. Tobias Berens & Dominik Wied & Daniel Ziggel, 2014. "Automated Portfolio Optimization Based on a New Test for Structural Breaks," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 2(2), pages 243-264, April.
    12. Capasso, Salvatore & D'Uva, Marcella & Fiorelli, Cristiana & Napolitano, Oreste, 2023. "Cross-border Italian sovereign risk transmission in EMU countries," Economic Modelling, Elsevier, vol. 126(C).
    13. Cristiana Fiorelli & Alfredo Cartone & Matteo Foglia, 2021. "Shadow rates and spillovers across the Eurozone: a spatial dynamic panel model," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(1), pages 223-245, February.
    14. 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.
    15. Kangogo, Moses & Volkov, Vladimir, 2021. "Dynamic effects of network exposure on equity markets," Working Papers 2021-03, University of Tasmania, Tasmanian School of Business and Economics.
    16. Hüttner, Amelie & Scherer, Matthias & Gräler, Benedikt, 2020. "Geostatistical modeling of dependent credit spreads: Estimation of large covariance matrices and imputation of missing data," Journal of Banking & Finance, Elsevier, vol. 118(C).
    17. Capasso Salvatore & D’Uva Marcella, & Fiorelli Cristiana & Napolitano Oreste, 2022. "Assessing the Impact of Country-Specific Sovereign Risk on Financial and Banking System in EMU: the Role of Italy," CSEF Working Papers 654, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    18. Guoli Mo & Chunzhi Tan & Weiguo Zhang & Xuezeng Yu, 2023. "Dynamic spatiotemporal correlation coefficient based on adaptive weight," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-43, December.
    19. Syed Mujahid Hussain & Amjad Naveed & Sheraz Ahmed & Nisar Ahmad, 2022. "Disaggregating the impact of oil prices on European industrial equity indices: a spatial econometric analysis," Empirical Economics, Springer, vol. 62(6), pages 2673-2692, June.
    20. 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.
    21. Heil, Thomas L.A. & Peter, Franziska J. & Prange, Philipp, 2022. "Measuring 25 years of global equity market co-movement using a time-varying spatial model," Journal of International Money and Finance, Elsevier, vol. 128(C).
    22. Thilo A. Schmitt & Rudi Schäfer & Dominik Wied & Thomas Guhr, 2016. "Spatial dependence in stock returns: local normalization and VaR forecasts," Empirical Economics, Springer, vol. 50(3), pages 1091-1109, May.
    23. Wahyoe Soedarmono & Romora Edward Sitorus & Amine Tarazi, 2015. "Bank charter value, systemic risk and credit reporting systems: Evidence from the Asia-Pacific region," Working Papers hal-01174111, HAL.
    24. Yun Feng & Xin Li, 2021. "Does cross-shareholding lead to China's stock returns comovement? Evidence from a GMM-based spatial AR model," Empirical Economics, Springer, vol. 61(6), pages 3213-3237, December.
    25. F. Blasques & P. Gorgi & S. J. Koopman & J. Sampi, 2023. "Does trade integration imply growth in Latin America? Evidence from a dynamic spatial spillover model," Tinbergen Institute Discussion Papers 23-007/IVI, Tinbergen Institute.

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    More about this item

    Keywords

    GMM estimation; Heteroscedasticity; Spatial dependence; Stock returns; Value at Risk; C13; C51; G12;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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