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The Effects of Overnight Events on Daytime Return: A Market Microstructure Analysis of Market Quality

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  • Sreekha Pullaykkodi

    (National Institute of Technology Karnataka)

  • Rajesh H. Acharya

    (National Institute of Technology Karnataka)

Abstract

This paper examines the trading and non-trading returns to diagnose the impact of market microstructure changes on market quality. The daily data of ten agricultural commodities traded on the National Commodity and Derivative Exchange (NCDEX) were used for the study. The data has been divided into three categories: year-wise, pre- and post-reform, pre-ban, and post-ban period. The study employs variance ratio analysis, and the results suggest high daytime and opening variances. A first-order autocorrelation detects the return predictability in the data series. A Value at Risk (VaR) and Expected Shortfall (ES) methods were employed to get more detail about the downside risk of the series. It suggested that daytime return has more risk compared to overnight return. Overall, this study suggests that market microstructure effects are visible in the Indian agricultural commodity market and hardly observe any improvement in the market quality. Since we reveal the impact of policy changes on market quality, the results will be useful for policymakers.

Suggested Citation

  • Sreekha Pullaykkodi & Rajesh H. Acharya, 2024. "The Effects of Overnight Events on Daytime Return: A Market Microstructure Analysis of Market Quality," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(3), pages 497-542, September.
    • Handle: RePEc:kap:apfinm:v:31:y:2024:i:3:d:10.1007_s10690-023-09424-9
    DOI: 10.1007/s10690-023-09424-9
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    1. Oldfield, George S, Jr & Rogalski, Richard J, 1980. "A Theory of Common Stock Returns over Trading and Non-Trading Periods," Journal of Finance, American Finance Association, vol. 35(3), pages 729-751, June.
    2. John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1993. "Trading Volume and Serial Correlation in Stock Returns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(4), pages 905-939.
    3. Sentana, Enrique & Wadhwani, Sushil B, 1992. "Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data," Economic Journal, Royal Economic Society, vol. 102(411), pages 415-425, March.
    4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    5. Lou, Dong & Polk, Christopher & Skouras, Spyros, 2019. "A tug of war: Overnight versus intraday expected returns," Journal of Financial Economics, Elsevier, vol. 134(1), pages 192-213.
    6. Sumon Kumar Bhaumik & Manisha Chakrabarty & Ali M. Kutan & Ekta Selarka, 2021. "How Effective are Stock Market Reforms in Emerging Market Economies? Evidence from a Panel VAR Model of the Indian Stock Market," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(4), pages 795-818, December.
    7. Puffer, Marlene K., 1991. "Private information and weekend volatility in the Tokyo and New York stock markets," Journal of Banking & Finance, Elsevier, vol. 15(2), pages 407-423, April.
    8. Bessembinder, Hendrik & Hertzel, Michael G, 1993. "Return Autocorrelations around Nontrading Days," The Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 155-189.
    9. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    10. K. L. Krishna & J. V. Meenakshi, 2022. "Agricultural Productivity Growth and Structural Transformation in Rural India: Some Recent Evidence," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 277-302, September.
    11. Van Ness, Bonnie & Van Ness, Robert & Yildiz, Serhat, 2021. "Private information in trades, R2, and large stock price movements," Journal of Banking & Finance, Elsevier, vol. 131(C).
    12. Liu, Qingfu & Tse, Yiuman, 2017. "Overnight returns of stock indexes: Evidence from ETFs and futures," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 440-451.
    13. Meng, Xiaochun & Taylor, James W., 2020. "Estimating Value-at-Risk and Expected Shortfall using the intraday low and range data," European Journal of Operational Research, Elsevier, vol. 280(1), pages 191-202.
    14. Ying Jiang & Neil Kellard & Xiaoquan Liu, 2020. "Night trading and market quality: Evidence from Chinese and US precious metal futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(10), pages 1486-1507, October.
    15. Dilip Kumar, 2020. "Value-at-Risk in the Presence of Structural Breaks Using Unbiased Extreme Value Volatility Estimator," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 587-610, September.
    16. Masulis, Ronald W & Ng, Victor K, 1995. "Overnight and Daytime Stock-Return Dynamics on the London Stock Exchange: The Impact of the "Big Bang" and the 1987 Stock-Market Crash," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 365-378, October.
    17. Ham, Hyuna & Ryu, Doojin & Webb, Robert I., 2022. "The effects of overnight events on daytime trading sessions," International Review of Financial Analysis, Elsevier, vol. 83(C).
    18. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    19. Tsiakas, Ilias, 2008. "Overnight information and stochastic volatility: A study of European and US stock exchanges," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 251-268, February.
    20. Akbas, Ferhat & Boehmer, Ekkehart & Jiang, Chao & Koch, Paul D., 2022. "Overnight returns, daytime reversals, and future stock returns," Journal of Financial Economics, Elsevier, vol. 145(3), pages 850-875.
    21. Madhavan, Ananth, 2000. "Market microstructure: A survey," Journal of Financial Markets, Elsevier, vol. 3(3), pages 205-258, August.
    22. Lockwood, Larry J & Linn, Scott C, 1990. "An Examination of Stock Market Return Volatility during Overnight and Intraday Periods, 1964-1989," Journal of Finance, American Finance Association, vol. 45(2), pages 591-601, June.
    23. Biswabhusan Bhuyan & Subhamitra Patra & Ranjan Kumar Bhuian, 2020. "Market Adaptability and Evolving Predictability of Stock Returns: An Evidence from India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(4), pages 605-619, December.
    24. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2016. "Forecasting stock volatility using after-hour information: Evidence from the Australian Stock Exchange," Economic Modelling, Elsevier, vol. 52(PB), pages 592-608.
    25. Frey, Rudiger & McNeil, Alexander J., 2002. "VaR and expected shortfall in portfolios of dependent credit risks: Conceptual and practical insights," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1317-1334, July.
    26. Doron Avramov & Tarun Chordia & Amit Goyal, 2006. "Liquidity and Autocorrelations in Individual Stock Returns," Journal of Finance, American Finance Association, vol. 61(5), pages 2365-2394, October.
    27. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    28. Chan, Kalok, 1993. "Imperfect Information and Cross-Autocorrelation among Stock Prices," Journal of Finance, American Finance Association, vol. 48(4), pages 1211-1230, September.
    29. Raushan Kumar, 2021. "Predicting Wheat Futures Prices in India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 121-140, March.
    30. Zhu, Xuehong & Zhang, Hongwei & Zhong, Meirui, 2017. "Volatility forecasting using high frequency data: The role of after-hours information and leverage effects," Resources Policy, Elsevier, vol. 54(C), pages 58-70.
    31. Sarveshwar Kumar Inani, 2018. "Price Discovery and Efficiency of Indian Agricultural Commodity Futures Market: An Empirical Investigation," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(1), pages 129-154, March.
    32. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2019. "Information, Trading, and Volatility: Evidence from Firm-Specific News," The Review of Financial Studies, Society for Financial Studies, vol. 32(3), pages 992-1033.
    33. Amihud, Yakov & Mendelson, Haim, 1987. "Trading Mechanisms and Stock Returns: An Empirical Investigation," Journal of Finance, American Finance Association, vol. 42(3), pages 533-553, July.
    34. Stoll, Hans R & Whaley, Robert E, 1990. "Stock Market Structure and Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 37-71.
    35. Cecchetti, Stephen G & Lam, Pok-sang, 1994. "Variance-Ratio Tests: Small-Sample Properties with an Application to International Output Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 177-186, April.
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    More about this item

    Keywords

    Market microstructure; Market quality; Trading and non-trading returns; Value at risk and Expected Shortfall; Autocorrelation; Private information;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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