Communications for Statistical Applications and Methods

  • 제25권2호
  • /
  • Pages.185-197
  • /
  • 2018
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

A case study for intercontinental comparison of herd behavior in global stock markets

  • 투고 : 2017.10.10
  • 심사 : 2017.12.29
  • 발행 : 2018.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Measuring market fear is an important way of understanding fundamental economic phenomena related to financial crises. There have been several approaches to measure market fear or panic level in a financial market. Recently, herd behavior has gained its popularity as important economic phenomena explaining the fear in the financial market. In this paper, we investigate herd behavior in global stock markets with a focus on intercontinental comparison. While various risk measures are available for the detection of herd behavior in the market, we use the standardized herd behavior index in Dhaene et al. (Insurance: Mathematics and Economics, 50, 357-370, 2012b) and Lee and Ahn (Dependence Modeling, 5, 316-329, 2017) for the comparison of herd behaviors in global stock markets. A global stock market data from Morgan Stanley Capital International is used to study herd behavior especially during periods of financial crises.

키워드

참고문헌

  1. Adamu A (2009). The effects of global financial crisis on Nigerian economy, Social Science Research Network.
  2. Al-Rjoub S (2011). Business cycles, financial crises, and stock volatility in Jordan stock exchange. International Journal of Economic Perspectives, 5, 83-95.
  3. Almeida RP, Costa HC, and Costa NCA (2012). Herd Behavior in Lation American Stock Markets. Latin American Business Review 13, 81-102. https://doi.org/10.1080/10978526.2012.700271
  4. Baglioni A and Cherubini U (2013). Within and between systemic country risk. Theory and evidence from the sovereign crisis in Europe. Journal of Economic Dynamics and Control, 37, 1581-1597. https://doi.org/10.1016/j.jedc.2013.02.005
  5. Barrieu P and Scandolo G (2015). Assessing financial model risk. European Journal of Operational Research, 242, 546-556. https://doi.org/10.1016/j.ejor.2014.10.032
  6. Baek EA and Oh MS (2016). Volatility spillover between the Korean KOSPI and the Hong Kong HSI stock markets. Communications for Statistical Applications and Methods, 23, 203-213. https://doi.org/10.5351/CSAM.2016.23.3.203
  7. Bikhchandani S and Sharma S (2001). Herd behavior in financial markets. International Monetary Fund, 47, 279-310.
  8. Bollerslev T (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327.
  9. Cheng H and Glascock JL (2006). Stock market linkages before and after the Asian Financial Crisis: evidence from three greater China Economic Area Stock Markets and the US. Review of Pacific Basin Financial Markets and Policies, 9, 297-315.
  10. Dasgupta A, Prat A, and Verardo M (2011). Institutional trade persistence and long-term equity returns, The Journal of Finance, 66, 635-653. https://doi.org/10.1111/j.1540-6261.2010.01644.x
  11. Dhaene J, Linders D, Dony J, Forys MB, and Schoutens W (2012a). FIX: the fear index-measuring market fear, Springer Proceedings in Mathematics and Statistics, 19, 37-55.
  12. Dhaene J, Linders D, Schoutens W, and Vyncke D (2012b). The herd behavior index: a new measure for the implied degree of co-movement in stock markets, Insurance: Mathematics and Economics, 50, 357-370. https://doi.org/10.1016/j.insmatheco.2012.01.005
  13. Engle RF (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, 987-1007.
  14. Exchange, Chicago Board Options (2009). The CBOE volatility index-VIX, White Paper, 1-23.
  15. Horta P, Mendes C, and Vieira I (2010). Contagion effects of the subprime crisis in the European NYSE Euronext markets, Portuguese Economic Journal, 9, 115-140. https://doi.org/10.1007/s10258-010-0056-6
  16. Kim C, Choi Y, Lee W, and Ahn JY (2013). Analyzing Herd Behavior in Global Stock Markets: An Intercontinental Comparison, arXiv:1308.3966v1 [q-fin.ST]
  17. Kim C and Pantzails C (2003). Global/Industrial Diversification and analyst herding, Financial Ana-lysts Journal, 59, 69-79.
  18. Lee W and Ahn JY (2015). Financial interpretation of herd behavior index and its statistical estima-tion. Journal of the Korean Statistical Society, 44, 295-311. https://doi.org/10.1016/j.jkss.2014.09.005
  19. Lee W and Ahn JY (2017). Measuring herd behavior: properties and pitfalls, Dependence Modeling, 5, 316-329.
  20. Metiu N (2012). Sovereign risk contagion in the Eurozone, Economics Letters, 117, 35-38. https://doi.org/10.1016/j.econlet.2012.04.074
  21. Morrison WM (2013). China's economic rise: history, trends, challenges, and implications for the United States, CRS Report for Congress, RL33534.
  22. Nanto DK (2004). 9/11 Terrorism: Global Economic Costs, Congressional Information Service, Library of Congress.
  23. Nieh CC, Yang CH, and Kao YS (2012). Who has more influence on Asian Stock Markets around the Subprime Mortgage Crisis-the U.S. or China?, Applied Economics Letters, 19, 329-335.
  24. Oh R, Shin DW, and Oh MS (2017). Bayesian analysis of financial volatilities addressing long-memory, conditional heteroscedasticity and skewed error distribution, Communications for Statistical Applications and Methods, 24, 507-518. https://doi.org/10.5351/CSAM.2017.24.5.507
  25. Salman N, Abbas M, Ahmad M, Majid MA, Khan N, and Raheel A (2012). Impact of financial crisis on Asian stock Markets, European Financial Management, 3, 736-741.
  26. Scarsini M (2004). Institutional herding, Review of Financial Studies, 17, 165-206. https://doi.org/10.1093/rfs/hhg035
  27. Scholes MS (2000). Crisis and risk management, The American Economic Review, 90, 17-21.
  28. Skintzi VD and Refenes APN (2005). Implied correlation index: A new measure of diversification. Journal of Futures Markets, 25, 171-197. https://doi.org/10.1002/fut.20137