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Measuring the risk of Chinese Fintech industry: evidence from the stock index

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  • Yao, Yinhong
  • Li, Jianping
  • Sun, Xiaolei

Abstract

This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition.

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  • Yao, Yinhong & Li, Jianping & Sun, Xiaolei, 2021. "Measuring the risk of Chinese Fintech industry: evidence from the stock index," Finance Research Letters, Elsevier, vol. 39(C).
    • Handle: RePEc:eee:finlet:v:39:y:2021:i:c:s1544612319311055
    DOI: 10.1016/j.frl.2020.101564
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    as
    1. Kuan, Chung-Ming & Yeh, Jin-Huei & Hsu, Yu-Chin, 2009. "Assessing value at risk with CARE, the Conditional Autoregressive Expectile models," Journal of Econometrics, Elsevier, vol. 150(2), pages 261-270, June.
    2. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    3. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2015. "Financial Network Systemic Risk Contributions," Review of Finance, European Finance Association, vol. 19(2), pages 685-738.
    4. Li, Jingyu & Yao, Yanzhen & Li, Jianping & Zhu, Xiaoqian, 2019. "Network-based estimation of systematic and idiosyncratic contagion: The case of Chinese financial institutions," Emerging Markets Review, Elsevier, vol. 40(C), pages 1-1.
    5. Caporale, Tony, 2012. "Time varying CAPM betas and banking sector risk," Economics Letters, Elsevier, vol. 115(2), pages 293-295.
    6. Illing, Mark & Liu, Ying, 2006. "Measuring financial stress in a developed country: An application to Canada," Journal of Financial Stability, Elsevier, vol. 2(3), pages 243-265, October.
    7. Artie W. Ng & Benny K.B. Kwok, 2017. "Emergence of Fintech and cybersecurity in a global financial centre," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 25(4), pages 422-434, November.
    8. Chen, Rongda & Yu, Jingjing & Jin, Chenglu & Bao, Weiwei, 2019. "Internet finance investor sentiment and return comovement," Pacific-Basin Finance Journal, Elsevier, vol. 56(C), pages 151-161.
    9. Sun, Xiaolei & Yao, Xiaoyang & Wang, Jun, 2017. "Dynamic interaction between economic policy uncertainty and financial stress: A multi-scale correlation framework," Finance Research Letters, Elsevier, vol. 21(C), pages 214-221.
    10. Yao, Qiwei & Tong, Howell, 1996. "Asymmetric least squares regression estimation: a nonparametric approach," LSE Research Online Documents on Economics 19423, London School of Economics and Political Science, LSE Library.
    11. Canh, Nguyen Phuc & Wongchoti, Udomsak & Thanh, Su Dinh & Thong, Nguyen Trung, 2019. "Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model," Finance Research Letters, Elsevier, vol. 29(C), pages 90-100.
    12. 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.
    13. Lee, In & Shin, Yong Jae, 2018. "Fintech: Ecosystem, business models, investment decisions, and challenges," Business Horizons, Elsevier, vol. 61(1), pages 35-46.
    14. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
    15. James W. Taylor, 2008. "Estimating Value at Risk and Expected Shortfall Using Expectiles," Journal of Financial Econometrics, Oxford University Press, vol. 6(2), pages 231-252, Spring.
    16. Troster, Victor & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Macedo, Demian Nicolás, 2019. "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters, Elsevier, vol. 30(C), pages 187-193.
    17. Shangyu Xie & Yong Zhou & Alan T. K. Wan, 2014. "A Varying-Coefficient Expectile Model for Estimating Value at Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 576-592, October.
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    1. HOROBEȚ Alexandra & MNOHOGHITNEI Irina & BELAȘCU Lucian & CROITORU Ionuț Marius, 2023. "Esg Reporting And Capital Market Investors: Insights From The Global Technology And Fintech Industries," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 18(2), pages 178-195, August.
    2. Xin Li & Xiujuan Tian, 2022. "Research on SMEs’ Reputation Mechanism and Default Risk Based on Investors’ Financial Participation," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    3. Yinhong Yao & Jianping Li, 2022. "Operational risk assessment of third-party payment platforms: a case study of China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-20, December.
    4. Marcelo Brutti Righi & Fernanda Maria Muller & Marlon Ruoso Moresco, 2022. "A risk measurement approach from risk-averse stochastic optimization of score functions," Papers 2208.14809, arXiv.org, revised May 2023.

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