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Discerning lead-lag between fear index and realized volatility

Listed author(s):
  • Wahab, Fatin Farhana
  • Masih, Mansur

In theory, historical volatility gauges the fluctuations of underlying assets or securities by monitoring changes in price over predetermined time period, while implied volatility looks into the future in its attempts to forecast the movement of the asset’s price based on current ones. Option trader tends to combine both volatilities with realized volatility serving as the baseline and implied volatility redefining the relative values of the options. Henceforth, the purpose of this study is twofold; first is to investigate the nature of lead-lag between the ‘fear index’ (VIX) and its corresponding realized volatility (RVI) of S&P 500 indices. Second, we examine the dynamic analysis of implied volatility transmission across inter-market correlation with newly adapted volatility indices from CBOE, VIX, OVX and GVZ to indicate which market is leading. Contrary to the popular perception, the paper finds that S&P 500 implied volatility is lagging its historical variance markedly, and surprisingly even its price index is leading the implied volatility as well. The study also concludes that Gold spearheads the market with stocks being the most sensitive to shocks. Our findings have clear policy implications for trading strategies and using volatilities in risk management.

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File URL: https://mpra.ub.uni-muenchen.de/79433/1/MPRA_paper_79433.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 79433.

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Date of creation: 11 May 2017
Handle: RePEc:pra:mprapa:79433
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  1. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
  2. Bekaert, Geert & Harvey, Campbell R. & Lundblad, Christian, 2005. "Does financial liberalization spur growth?," Journal of Financial Economics, Elsevier, vol. 77(1), pages 3-55, July.
  3. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
  4. repec:eee:jrpoli:v:52:y:2017:i:c:p:201-206 is not listed on IDEAS
  5. Luo, Xingguo & Qin, Shihua, 2017. "Oil price uncertainty and Chinese stock returns: New evidence from the oil volatility index," Finance Research Letters, Elsevier, vol. 20(C), pages 29-34.
  6. Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-681.
  7. Bernard Dumas & Jeff Fleming & Robert E. Whaley, 1998. "Implied Volatility Functions: Empirical Tests," Journal of Finance, American Finance Association, vol. 53(6), pages 2059-2106, December.
  8. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
  9. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
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