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Non-linearity, persistence and spillover effects in stock returns: the role of the volatility index

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  • Po-Chin Wu
  • Sheng-Chieh Pan
  • Xue-Ling Tai

Abstract

This paper employs panel smooth transition autoregression models, using the volatility index (VIX) as the transition variable, to evaluate the persistence, efficiency, and spillover effects of stock markets in G-10 and ASEAN-5 countries. The persistence effects are nonlinear and vary with time and across countries, depending on the value of the VIX. When VIXs are over specific thresholds (30.69 and 31.04), the stock returns in G-10 and ASEAN-5 display moderate and similar persistence effects and make lagged stock returns become more important when evaluating current stock returns. The spillover effects occur from VIX to stock returns. The speculation behavior of stock markets in ASEAN-5 is higher than that in G-10; however, the result in the efficiency of stock markets is the opposite. Copyright Springer Science+Business Media New York 2015

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  • Po-Chin Wu & Sheng-Chieh Pan & Xue-Ling Tai, 2015. "Non-linearity, persistence and spillover effects in stock returns: the role of the volatility index," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 597-613, August.
  • Handle: RePEc:kap:empiri:v:42:y:2015:i:3:p:597-613
    DOI: 10.1007/s10663-014-9266-y
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    2. Long, Wen & Zhao, Manyi & Tang, Yeran, 2021. "Can the Chinese volatility index reflect investor sentiment?," International Review of Financial Analysis, Elsevier, vol. 73(C).

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    More about this item

    Keywords

    Panel smooth transition autoregression (PSTAR) model; Volatility index (VIX); Persistence; Spillover effect; Regime switching; C23; G10;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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