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The online version of this article (https://doi.org/10.1007/s10690-018-9262-5) contains supplementary material, which is available to authorized users.
I thank Xin Jin, and participants at the German Finance Association (DGF) Annual Meeting in October 2017 in Ulm, Germany, and the Forecasting Financial Markets Conference in May 2017 in Liverpool, UK, for comments on earlier versions of this paper.
This paper studies the predictive ability of corridor implied volatility (CIV) measure. It is motivated by the fact that CIV is measured with better precision and reliability than the model-free implied volatility due to the lack of liquid options in the tails of the risk-neutral distribution. By adding CIV measures to the modified GARCH specifications, the out-of-sample predictive ability of CIV is measured by the forecast accuracy of conditional volatility. It finds that the narrowest CIV measure, covering about 10% of the RND, dominate the 1-day ahead conditional volatility forecasts regardless of the choice of GARCH models in high volatile period; as market moves to non volatile periods, the optimal width broadens. For multi-day ahead forecasts narrow and mid-range CIV measures are favoured in the full sample and high volatile period for all forecast horizons, depending on which loss functions are used; whereas in non turbulent markets, certain mid-range CIV measures are favoured, for rare instances, wide CIV measures dominate the performance. Regarding the comparisons between best performed CIV measures and two benchmark measures (market volatility index and at-the-money Black–Scholes implied volatility), it shows that under the EGARCH framework, none of the benchmark measures are found to outperform best performed CIV measures, whereas under the GARCH and NAGARCH models, best performed CIV measures are outperformed by benchmark measures for certain instances.
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- Testing the Predictive Ability of Corridor Implied Volatility Under GARCH Models
- Springer Japan
Asia-Pacific Financial Markets
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