Modeling and forecasting the additive bias corrected extreme value volatility estimator

Dilip Kumar and S. Maheswaran

International Review of Financial Analysis, 2014, vol. 34, issue C, 166-176

Abstract: In this paper, we provide a framework to model and forecast daily volatility based on the newly proposed additive bias corrected extreme value volatility estimator (the Add RS estimator). The theoretical framework of the additive bias corrected extreme value volatility estimator is based on the closed form solution for the joint probability of the running maximum and the terminal value of the random walk. Using the opening, high, low and closing prices of S&P 500, CAC 40, IBOVESPA and S&P CNX Nifty indices, we find that the logarithm of the Add RS estimator is approximately Gaussian and that a simple linear Gaussian long memory model can be applied to forecast the logarithm of the Add RS estimator. The forecast evaluation analysis indicates that the conditional Add RS estimator provides better forecasts of realized volatility than alternative range-based and return-based models.

Keywords: Volatility modeling; Volatility forecasting; Forecast evaluation; Bias corrected extreme value estimator (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 G10 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:34:y:2014:i:c:p:166-176

DOI: 10.1016/j.irfa.2014.06.002

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