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Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility

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  • Jaesun Noh
  • Tae-Hwan Kim

Abstract

We show that historical volatility from high frequency returns outperforms implied volatility when standardized returns by historical volatility tends to be normally distributed. For the FTSE 100 futures, we find that historical volatility using high frequency returns outperforms implied volatility in forecasting future volatility. However, we find that implied volatility outperforms historical volatility in forecasting future volatility for the S&P 500 futures. The results also indicate that historical volatility using high frequency returns could be an unbiased forecast for the FTSE 100 futures.

Suggested Citation

  • Jaesun Noh & Tae-Hwan Kim, 2006. "Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility," Applied Economics, Taylor & Francis Journals, vol. 38(4), pages 395-413.
    • Handle: RePEc:taf:applec:v:38:y:2006:i:4:p:395-413
    DOI: 10.1080/00036840500391229
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    Cited by:

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    2. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.
    3. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    4. Dimitrios P. Louzis & Spyros Xanthopoulos-Sisinis & Apostolos P. Refenes, 2012. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Applied Economics, Taylor & Francis Journals, vol. 44(27), pages 3533-3550, September.
    5. Alex YiHou Huang, 2012. "Volatility forecasting by quantile regression," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 423-433, February.

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