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Asymmetry and Long Memory Features in Volatility: Evidence From Korean Stock Market

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  • Sang Hoon Kang

    (Gyeongsang National University)

  • SEONG-MIN YOON

    (Pusan National University)

Abstract

We investigate the asymmetry and long memory features in the volatility of the Korean stock market. For this purpose, we examine some GARCH class models that can capture these volatility stylized factors in the KOSPI 200 Index return data. From the results of estimation and diagnostic tests, we find that the decrease in volatility asymmetry in the crisis period is due to the introduction of derivatives markets (index futures and option trading) and the market liberalization, and that the degree of long memory features becomes lower after the financial crisis, implying that the financial crisis has the efficiency of the Korean stock market.

Suggested Citation

  • Sang Hoon Kang & SEONG-MIN YOON, 2008. "Asymmetry and Long Memory Features in Volatility: Evidence From Korean Stock Market," Korean Economic Review, Korean Economic Association, vol. 24, pages 383-412.
    • Handle: RePEc:kea:keappr:ker-20081231-24-2-04
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    References listed on IDEAS

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    4. Boubaker, Heni & Sghaier, Nadia, 2015. "Semiparametric generalized long-memory modeling of some mena stock market returns: A wavelet approach," Economic Modelling, Elsevier, vol. 50(C), pages 254-265.

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    More about this item

    Keywords

    Volatility; Asymmetry; Long Memory; FIAPARCH; Korean Stock Market;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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