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Detecting Risk Transfer in Financial Markets using Different Risk Measures

  • Marcin Fałdziński

    ()

    (Nicolaus Copernicus University)

  • Magdalena Osińska

    ()

    (Nicolaus Copernicus University)

  • Tomasz Zdanowicz

    ()

    (Nicolaus Copernicus University)

High movements of asset prices constitute intrinsic elements of financial crises. There is a common agreement that extreme events are responsible for that. Making inference about the risk spillover and its effect on markets one should use such methods and tools that can fit properly for catastrophic events. In the paper Extreme Value Theory (EVT) invented particularly for modelling extreme events was used. The purpose of the paper is to model risky assets using EVT and to analyse the transfer of risk across the financial markets all over the world using the Granger causality in risk test. The concept of testing in causality in risk was extended to Spectral Risk Measure i.e., respective hypotheses were constructed and checked by simulation. The attention is concentrated on the Chinese financial processes and their relations with those in the rest of the globe. The original idea of the Granger causality in risk assumes usage of Value at Risk as a risk measure. We extended the scope of application of the test to Expected Shortfall and Spectral Risk Measure. The empirical results exhibit very interesting dependencies.

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Article provided by CEJEME in its journal Central European Journal of Economic Modelling and Econometrics.

Volume (Year): 4 (2012)
Issue (Month): 1 (March)
Pages: 45-64

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Handle: RePEc:psc:journl:v:4:y:2012:i:1:p:45-64
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  1. Kian-Ping Lim & Muzafar Shah Habibullah & Melvin J. Hinich, 2009. "The Weak-form Efficiency of Chinese Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 8(2), pages 133-163, May.
  2. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
  3. Kevin Dowd & John Cotter & Ghulam Sorwar, 2008. "Spectral Risk Measures: Properties and Limitations," Journal of Financial Services Research, Springer, vol. 34(1), pages 61-75, August.
  4. 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.
  5. Cotter, John & Dowd, Kevin, 2006. "Extreme spectral risk measures: An application to futures clearinghouse margin requirements," Journal of Banking & Finance, Elsevier, vol. 30(12), pages 3469-3485, December.
  6. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 53-89.
  7. Acerbi, Carlo, 2002. "Spectral measures of risk: A coherent representation of subjective risk aversion," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1505-1518, July.
  8. Fotios C. Harmantzis & Linyan Miao & Yifan Chien, 2006. "Empirical study of value-at-risk and expected shortfall models with heavy tails," Journal of Risk Finance, Emerald Group Publishing, vol. 7(2), pages 117-135, March.
  9. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
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