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Cost of health problems caused by stock market volatility: An empirical study in Taiwan

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  • Weng, Pei-Shih (Pace)
  • Hsiao, Yu-Jen
  • Hsiao, Kai-Yuan
  • Chang, Wei-Shan

Abstract

This study highlights the often-overlooked health impacts and related costs endured by individual investors due to market volatility. Leveraging Taiwan's unique market characteristics and healthcare system, we underscore the economic repercussions of health degradation induced by market instability. Analyzing daily medical costs for depression and hypertension patients over 11 years reveals the impact of market turbulence. Specifically, a standard deviation increase in daily market volatility aligns with 4% and 12% surges in daily emergency and outpatient spending, respectively. To avert confounding biases, our empirical models account for market regimes and weather factors, and the outcomes are corroborated through robustness tests.

Suggested Citation

  • Weng, Pei-Shih (Pace) & Hsiao, Yu-Jen & Hsiao, Kai-Yuan & Chang, Wei-Shan, 2023. "Cost of health problems caused by stock market volatility: An empirical study in Taiwan," Finance Research Letters, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:finlet:v:57:y:2023:i:c:s1544612323005780
    DOI: 10.1016/j.frl.2023.104206
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    1. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    2. Saban Nazlioglu & Alper Gormus & Ugur Soytas, 2019. "Oil Prices and Monetary Policy in Emerging Markets: Structural Shifts in Causal Linkages," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(1), pages 105-117, January.
    3. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    4. Murat Körs & Mehmet Baha Karan, 2023. "Stock exchange volatility forecasting under market stress with MIDAS regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 295-306, January.
    5. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    6. McInerney, Melissa & Mellor, Jennifer M. & Nicholas, Lauren Hersch, 2013. "Recession depression: Mental health effects of the 2008 stock market crash," Journal of Health Economics, Elsevier, vol. 32(6), pages 1090-1104.
    7. Chad Cotti & Richard A. Dunn & Nathan Tefft, 2015. "The Dow is Killing Me: Risky Health Behaviors and the Stock Market," Health Economics, John Wiley & Sons, Ltd., vol. 24(7), pages 803-821, July.
    8. Brad M. Barber & Yi-Tsung Lee & Yu-Jane Liu & Terrance Odean, 2009. "Just How Much Do Individual Investors Lose by Trading?," The Review of Financial Studies, Society for Financial Studies, vol. 22(2), pages 609-632, February.
    9. Fu, Zheng & Chen, Zhiguo & Sharif, Arshian & Razi, Ummara, 2022. "The role of financial stress, oil, gold and natural gas prices on clean energy stocks: Global evidence from extreme quantile approach," Resources Policy, Elsevier, vol. 78(C).
    Full references (including those not matched with items on IDEAS)

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