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An empirical investigation of investor sentiment and volatility of realty sector market in India: an application of the DCC–GARCH model

Author

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  • Naga Pillada

    (CHRIST Deemed to Be University
    Platinum Gardenia)

  • Sangeetha Rangasamy

    (CHRIST Deemed to Be University)

Abstract

Understanding how an irrational investors’ sentiment affects the realty market returns, especially during the pandemic, is imperative to take any financial decisions. The effect of investor sentiment on the movement of the realty market leading to market volatility is dynamically represented in a numerical form. The study incorporates daily market data and their implicit indices to construct a sector-specific investor sentiment index by using the principal component analysis method. To analyse the relationship between the variables, a quantitative approach is used by incorporating an econometric model—dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC–GARCH). The directionality of the relationship between the variables is assessed by the Diebold–Yilmaz method. This study is done to investigate the return deviation in the realty sector due to sentiment impact during the pandemic in the Indian context. The findings indicate the existence of an asymmetric impact of the sentiment, leading to extreme volatility and returns in the realty sector. The results confirmed the presence of bi-directional relationship between asset returns and investor sentiment and quantified the relationship numerically. This study focused on the development, applicability, and validity of a sentiment index pertaining to the Indian realty sector. This study highlights the impact of a qualitative non-fundamental factor like sentiment as a measurable factor in determining the volatility on market returns.

Suggested Citation

  • Naga Pillada & Sangeetha Rangasamy, 2023. "An empirical investigation of investor sentiment and volatility of realty sector market in India: an application of the DCC–GARCH model," SN Business & Economics, Springer, vol. 3(2), pages 1-16, February.
    • Handle: RePEc:spr:snbeco:v:3:y:2023:i:2:d:10.1007_s43546-023-00434-3
    DOI: 10.1007/s43546-023-00434-3
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    1. Stijn Claessens & M. Ayhan Kose, 2017. "Macroeconomic Implications of Financial Imperfections: A Survey," Koç University-TUSIAD Economic Research Forum Working Papers 1719, Koc University-TUSIAD Economic Research Forum.
    2. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    3. Xiaoyong Xiao & Jing Huang, 2018. "Dynamic Connectedness of International Crude Oil Prices: The Diebold–Yilmaz Approach," Sustainability, MDPI, vol. 10(9), pages 1-16, September.
    4. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Lixu Chi & Xintian Zhuang & Dalei Song, 2012. "Investor sentiment in the Chinese stock market: an empirical analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 19(4), pages 345-348, March.
    7. Monika Piazzesi & Martin Schneider, 2009. "Inflation and the price of real assets," Staff Report 423, Federal Reserve Bank of Minneapolis.
    8. Roddy Allan & Ervi Liusman & Teddy Lu & Desmond Tsang, 2021. "The COVID-19 Pandemic and Commercial Property Rent Dynamics," JRFM, MDPI, vol. 14(8), pages 1-24, August.
    9. Piazzesi, Monika & Leombroni, Matteo & Rogers, Ciaran & Schneider, Martin, 2020. "Inflation and the Price of Real Assets," CEPR Discussion Papers 14390, C.E.P.R. Discussion Papers.
    10. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    11. Titus SUCIU, 2015. "From The Classical Finance To The Behavioral Finance," Journal of Public Administration, Finance and Law, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 7(7), pages 80-88, June.
    12. Prashant Das & Julia Freybote & Gianluca Marcato, 2015. "An Investigation into Sentiment-Induced Institutional Trading Behavior and Asset Pricing in the REIT Market," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 160-189, August.
    13. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    14. Baker, Malcolm & Wurgler, Jeffrey & Yuan, Yu, 2012. "Global, local, and contagious investor sentiment," Journal of Financial Economics, Elsevier, vol. 104(2), pages 272-287.
    15. Shi, Qi, 2020. "A much robust and updated evidences of the alternative real-estate based asset pricing," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    16. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    17. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    18. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    19. Brown, Gregory W. & Cliff, Michael T., 2004. "Investor sentiment and the near-term stock market," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 1-27, January.
    20. Matteo Rossi, 2016. "The capital asset pricing model: a critical literature review," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 18(5), pages 604-617.
    21. Eugene F. Fama, 2014. "Two Pillars of Asset Pricing," American Economic Review, American Economic Association, vol. 104(6), pages 1467-1485, June.
    22. Vikas Barbate & Rajesh N. Gade & Shirish S. Raibagkar, 2021. "COVID-19 and Its Impact on the Indian Economy," Vision, , vol. 25(1), pages 23-35, March.
    23. Haritha P H & Abdul Rishad, 2020. "An empirical examination of investor sentiment and stock market volatility: evidence from India," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-15, December.
    24. Eugene F. Fama & Kenneth R. French, 2004. "The Capital Asset Pricing Model: Theory and Evidence," Journal of Economic Perspectives, American Economic Association, vol. 18(3), pages 25-46, Summer.
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    More about this item

    Keywords

    DCC–GARCH; Diebold–Yilmaz test; Investor sentiment index; Indian realty stock market; Principal component analysis;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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