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The Impact of Non-trading Periods on the Measurement of Volatility

Author

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  • Yaw-Huei Wang

    (Department of Finance at the National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10716, Taiwan)

  • Yu-Jen Hsiao

    (Department of Finance at National Central University, No. 300, Jhongda Road, Jhongli City, Taoyuan County 32001, Taiwan)

Abstract

Based upon the theory of the "arrival of news", the main purpose of this paper is to investigate the impact of non-trading periods on the measurement of volatility for the S&P 500, FTSE 100, and TAIEX indices. Using an adaptation of the GJR (1,1) model, we find that both weekday holiday periods and half-day trading periods have significant impacts on the estimation of volatility for the S&P 500 and FTSE 100 indices. On the other hand, weekends have significant impacts for the TAIEX index. Our findings imply that for the UK and US markets, much less relevant information is produced during weekends, while more relevant information continues to be produced during other types of non-trading periods. However, the weekend volatility of the Taiwan market is specially driven because the US macro news is announced on Fridays and the trading time of the US market is later than that of the Taiwan market without any overlapping.

Suggested Citation

  • Yaw-Huei Wang & Yu-Jen Hsiao, 2010. "The Impact of Non-trading Periods on the Measurement of Volatility," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 607-620.
    • Handle: RePEc:wsi:rpbfmp:v:13:y:2010:i:04:n:s0219091510002098
    DOI: 10.1142/S0219091510002098
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    Citations

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    Cited by:

    1. Díaz-Mendoza, Ana-Carmen & Pardo, Angel, 2020. "Holidays, weekends and range-based volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Yu-Lun Chen & Yin-Feng Gau & Wen-Ju Liao, 2016. "Trading activities and price discovery in foreign currency futures markets," Review of Quantitative Finance and Accounting, Springer, vol. 46(4), pages 793-818, May.
    3. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

    More about this item

    Keywords

    Volatility; non-trading periods; GARCH; GJR; intraday;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance

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