IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v10y2022i11p209-d962407.html
   My bibliography  Save this article

The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach

Author

Listed:
  • Mukul Bhatnagar

    (Commerce Department (USB), Chandigarh University, Mohali 140413, India)

  • Ercan Özen

    (Department of Finance and Banking, University of Uşak, Uşak 64000, Turkey)

  • Sanjay Taneja

    (University School of Business, Chandigarh University, Mohali 140413, India)

  • Simon Grima

    (Department of Insurance and Risk Management, Faculty of Economics, Management and Accountancy, University of Malta, MSD Msida 2080, Malta
    Faculty of Business, Management and Economics, University of Latvia, LV-1586 Riga, Latvia)

  • Ramona Rupeika-Apoga

    (Faculty of Business, Management and Economics, University of Latvia, LV-1586 Riga, Latvia)

Abstract

Fintech allows investors to explore previously unavailable investment opportunities; it provides new return opportunities while also introducing new risks. The aim of this study is to investigate the relationship between risk and return in the fintech industry in the Indian stock market. This article is based on market-based research that focuses on demonstrating the volatility in the fintech market’s prices and demystifying the opportunities. Secondary data were collected from the Bombay Stock Exchange’s official fintech industry website from January 2017 to July 2022 to determine whether there is any dynamic link between risk and return in the Indian fintech market. The variance-based Mean-GARCH (GARCH-M) model was used to determine whether there is a dynamic link between risk and return in the Indian fintech market. The findings emphasize the importance of taking the risk of investing in India’s fintech industry. The implications for stock investors’ and fund managers’ portfolio composition and holding periods of equities or market exposure are significant. Finally, depending on their investment horizons, the Indian fintech industry may yield significant profits for risk-taking individuals.

Suggested Citation

  • Mukul Bhatnagar & Ercan Özen & Sanjay Taneja & Simon Grima & Ramona Rupeika-Apoga, 2022. "The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach," Risks, MDPI, vol. 10(11), pages 1-16, November.
  • Handle: RePEc:gam:jrisks:v:10:y:2022:i:11:p:209-:d:962407
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/10/11/209/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/10/11/209/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Juan Camilo Cardenas & Jeffrey Carpenter, 2008. "Behavioural Development Economics: Lessons from Field Labs in the Developing World," Journal of Development Studies, Taylor & Francis Journals, vol. 44(3), pages 311-338.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Kroner, Kenneth F. & Lastrapes, William D., 1993. "The impact of exchange rate volatility on international trade: Reduced form estimates using the GARCH-in-mean model," Journal of International Money and Finance, Elsevier, vol. 12(3), pages 298-318, June.
    4. Erik Feyen & Jon Frost & Leonardo Gambacorta & Harish Natarajan & Matthew Saal, 2021. "Fintech and the digital transformation of financial services: implications for market structure and public policy," BIS Papers, Bank for International Settlements, number 117.
    5. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    6. Baljinder Kaur & Sood Kiran & Simon Grima & Ramona Rupeika-Apoga, 2021. "Digital Banking in Northern India: The Risks on Customer Satisfaction," Risks, MDPI, vol. 9(11), pages 1-18, November.
    7. Andreas Oehler & Matthias Horn & Stefan Wendt, 2022. "Investor Characteristics and their Impact on the Decision to use a Robo-advisor," Journal of Financial Services Research, Springer;Western Finance Association, vol. 62(1), pages 91-125, October.
    8. Chen, Gong-meng & Firth, Michael & Meng Rui, Oliver, 2002. "Stock market linkages: Evidence from Latin America," Journal of Banking & Finance, Elsevier, vol. 26(6), pages 1113-1141, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ziyao Wang & Yufei Xia & Yating Fu & Ying Liu, 2023. "Volatility Spillover Dynamics and Determinants between FinTech and Traditional Financial Industry: Evidence from China," Mathematics, MDPI, vol. 11(19), pages 1-23, September.
    2. Sun Meng & Yan Chen, 2023. "Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    3. Eman Ismail & Yasser Tawfik Halim & Mohamed Samy EL-Deeb, 2023. "Corporate reputation and shareholder investment: a study of Egypt's tourism listed companies," Future Business Journal, Springer, vol. 9(1), pages 1-15, December.

    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. Stentoft, Lars, 2005. "Pricing American options when the underlying asset follows GARCH processes," Journal of Empirical Finance, Elsevier, vol. 12(4), pages 576-611, September.
    2. Bauer, Rob M M J & Nieuwland, Frederick G M C & Verschoor, Willem F C, 1994. "German Stock Market Dynamics," Empirical Economics, Springer, vol. 19(3), pages 397-418.
    3. Lars Stentoft, 2008. "American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 540-582, Fall.
    4. Claudio Bonilla & Jean Sepulveda, 2011. "Stock returns in emerging markets and the use of GARCH models," Applied Economics Letters, Taylor & Francis Journals, vol. 18(14), pages 1321-1325.
    5. Chuong Luong & Nikolai Dokuchaev, 2018. "Forecasting of Realised Volatility with the Random Forests Algorithm," JRFM, MDPI, vol. 11(4), pages 1-15, October.
    6. Peter Christoffersen & Ruslan Goyenko & Kris Jacobs & Mehdi Karoui, 2018. "Illiquidity Premia in the Equity Options Market," The Review of Financial Studies, Society for Financial Studies, vol. 31(3), pages 811-851.
    7. Kaehler, Jürgen, 1991. "Modelling and forecasting exchange-rate volatility with ARCH-type models," ZEW Discussion Papers 91-02, ZEW - Leibniz Centre for European Economic Research.
    8. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    9. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    10. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010. "Option pricing for GARCH-type models with generalized hyperbolic innovations," Post-Print halshs-00469529, HAL.
    11. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    12. Huang, Alex YiHou & Peng, Sheng-Pen & Li, Fangjhy & Ke, Ching-Jie, 2011. "Volatility forecasting of exchange rate by quantile regression," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 591-606, October.
    13. Menn, Christian & Rachev, Svetlozar T., 2005. "A GARCH option pricing model with [alpha]-stable innovations," European Journal of Operational Research, Elsevier, vol. 163(1), pages 201-209, May.
    14. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    15. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    16. Szakmary, Andrew & Ors, Evren & Kyoung Kim, Jin & Davidson, Wallace III, 2003. "The predictive power of implied volatility: Evidence from 35 futures markets," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2151-2175, November.
    17. Yang, Lixiong & Lee, Chingnun & Shie, Fu Shuen, 2014. "How close a relationship does a capital market have with other markets? A reexamination based on the equal variance test," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 198-226.
    18. Tore Selland Kleppe & Jun Yu & Hans J. skaug, 2011. "Simulated Maximum Likelihood Estimation for Latent Diffusion Models," Working Papers 10-2011, Singapore Management University, School of Economics.
    19. Kaehler, Jürgen & Marnet, Volker, 1993. "Markov-switching models for exchange-rate dynamics and the pricing of foreign-currency options," ZEW Discussion Papers 93-03, ZEW - Leibniz Centre for European Economic Research.
    20. Lars Stentoft, 2008. "Option Pricing using Realized Volatility," CREATES Research Papers 2008-13, Department of Economics and Business Economics, Aarhus University.

    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:gam:jrisks:v:10:y:2022:i:11:p:209-:d:962407. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.