IDEAS home Printed from https://ideas.repec.org/p/mad/wpaper/2025-283.html

Assessing Market Liquidity Amidst Crisis: Evidence from Indian Stock Market

Author

Listed:
  • Sidharth J

    ((corresponding author) Madras School of Economics, Chennai, Tamil Nadu, India, 600025)

Abstract

Liquidity is very important for the stock market as it effects the portfolio decisions of investors and influences future outlook of the economy. Liquidity is especially important to withstand economic shocks and to facilitate faster recovery. The study examines the impact of two significant market crises, the 2008 Global Financial Crisis and the COVID-19 pandemic, on liquidity in the Indian stock market. Data for 655 companies listed at the National Stock Exchange (NSE) is utilized over a time period of 17 years from 2005 to 2022 to analyze multiple dimensions of liquidity. Preliminary results suggest that both crises had a substantial effect on market liquidity. The 2008 financial crisis exhibits a more pronounced and prolonged impact compared to COVID-19 pandemic. The severity of 2008 financial crisis surpassed that of COVID-19 across all liquidity dimensions. Trading volumes saw an uptrend during COVID-19 crisis, contrasting with decline in all other liquidity measures. Conversely, the 2008 financial crisis witnessed reductions in trading volume alongside broader declines in liquidity measures.

Suggested Citation

  • Sidharth J, 2025. "Assessing Market Liquidity Amidst Crisis: Evidence from Indian Stock Market," Working Papers 2025-283, Madras School of Economics,Chennai,India.
    • Handle: RePEc:mad:wpaper:2025-283
    as

    Download full text from publisher

    File URL: https://www.mse.ac.in/wp-content/uploads/2025/06/Working-paper-283.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jing, Bing-Yi & Yuan, Junqing & Zhou, Wang, 2009. "Jackknife Empirical Likelihood," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1224-1232.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khan, Ruhul Ali, 2023. "Two-sample nonparametric test for proportional reversed hazards," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    2. Chiang, Harold D. & Matsushita, Yukitoshi & Otsu, Taisuke, 2025. "Multiway empirical likelihood," Journal of Econometrics, Elsevier, vol. 249(PA).
    3. Yongli Sang, 2021. "A Jackknife Empirical Likelihood Approach for Testing the Homogeneity of K Variances," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(7), pages 1025-1048, October.
    4. Matsushita, Yukitoshi & Otsu, Taisuke, 2018. "Likelihood inference on semiparametric models: average derivative and treatment effect," LSE Research Online Documents on Economics 85870, London School of Economics and Political Science, LSE Library.
    5. Yang, Hanfang & Zhao, Yichuan, 2018. "Smoothed jackknife empirical likelihood for the one-sample difference of quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 58-69.
    6. Zhao, Yichuan & Meng, Xueping & Yang, Hanfang, 2015. "Jackknife empirical likelihood inference for the mean absolute deviation," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 92-101.
    7. Zhouping Li & Jinfeng Xu & Wang Zhou, 2016. "On Nonsmooth Estimating Functions via Jackknife Empirical Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 49-69, March.
    8. Li, Minqiang & Peng, Liang & Qi, Yongcheng, 2011. "Reduce computation in profile empirical likelihood method," MPRA Paper 33744, University Library of Munich, Germany.
    9. Zhang, Rongmao & Peng, Liang & Qi, Yongcheng, 2012. "Jackknife-blockwise empirical likelihood methods under dependence," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 56-72, February.
    10. Da Chen & Linlin Dai & Yichuan Zhao, 2024. "Jackknife empirical likelihood for the correlation coefficient with additive distortion measurement errors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(4), pages 1129-1159, December.
    11. Heng Wang & Ping-Shou Zhong, 2017. "Order-restricted inference for means with missing values," Biometrics, The International Biometric Society, vol. 73(3), pages 972-980, September.
    12. Deemat C. Mathew & Reeba Mary Alex & Sudheesh K. Kattumannil, 2024. "Jackknife empirical likelihood ratio test for testing mean time to failure order," Statistical Papers, Springer, vol. 65(1), pages 79-92, February.
    13. Harold D. Chiang & Bing Yang Tan, 2020. "Empirical likelihood and uniform convergence rates for dyadic kernel density estimation," Papers 2010.08838, arXiv.org, revised May 2022.
    14. Chen, Ruxin & Tabri, Rami V., 2021. "Jackknife empirical likelihood for inequality constraints on regular functionals," Journal of Econometrics, Elsevier, vol. 221(1), pages 68-77.
    15. Amorim, G. & Thas, O. & Vermeulen, K. & Vansteelandt, S. & De Neve, J., 2018. "Small sample inference for probabilistic index models," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 137-148.
    16. Hanfang Yang & Yichuan Zhao, 2017. "Smoothed jackknife empirical likelihood for the difference of two quantiles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 1059-1073, October.
    17. Vanderford Courtney & Sang Yongli & Dang Xin, 2020. "Two symmetric and computationally efficient Gini correlations," Dependence Modeling, De Gruyter, vol. 8(1), pages 373-395, January.
    18. Yukitoshi Matsushita & Taisuke Otsu, 2018. "Likelihood Inference on Semiparametric Models: Average Derivative and Treatment Effect," The Japanese Economic Review, Springer, vol. 69(2), pages 133-155, June.
    19. Yongli Sang & Xin Dang & Yichuan Zhao, 2020. "Depth-based weighted jackknife empirical likelihood for non-smooth U-structure equations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 573-598, June.
    20. Zhao, Yichuan & Su, Yueju & Yang, Hanfang, 2020. "Jackknife empirical likelihood inference for the Pietra ratio," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mad:wpaper:2025-283. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Geetha G (email available below). General contact details of provider: https://edirc.repec.org/data/mseacin.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.