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Nonlinear Shift Contagion Modeling: Further Evidence from High Frequency Stock Data

In: Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration

Author

Listed:
  • Mohamed El Hedi Arouri
  • Fredj Jawadi
  • Wael Louhichi
  • Duc Khuong Nguyen

Abstract

The issue of whether financial markets around the world have become more and more dependent in recent years has received much attention from academics, investors, fund managers and policymakers as it is central to asset pricing, risk management and policy transmission. Indeed, most of these financial decisions are actually made in an international context and based mechanically on the knowledge of the risk-return trade-off that in turn depends largely on the degree of cross-market interdependence. To the extent that financial markets of a country could be affected by harmful impacts of a collapse occurred in another market due to their mutual dependence, the rise of successive economic and financial crisis over the last two decades (e.g., the Mexican debt crisis in 1994–1995, the Asian financial crisis in 1997, the Internet bubble collapse in 2000, and more recently the current global financial crisis in 2007–2009) contributes to further encourage reflections on the cross-market linkages.

Suggested Citation

  • Mohamed El Hedi Arouri & Fredj Jawadi & Wael Louhichi & Duc Khuong Nguyen, 2011. "Nonlinear Shift Contagion Modeling: Further Evidence from High Frequency Stock Data," Palgrave Macmillan Books, in: Greg N. Gregoriou & Razvan Pascalau (ed.), Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration, chapter 7, pages 143-160, Palgrave Macmillan.
  • Handle: RePEc:pal:palchp:978-0-230-29521-6_7
    DOI: 10.1057/9780230295216_7
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    Cited by:

    1. Okorie, David Iheke & Lin, Boqiang, 2021. "Stock markets and the COVID-19 fractal contagion effects," Finance Research Letters, Elsevier, vol. 38(C).

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