Abstract
A model-free methodology is for the first time used in this paper to estimate a daily volatility index (VIBEX-NEW) for the Spanish financial market. We show that daily changes in VIBEX-NEW display a negative, tight contemporaneous relationship with IBEX daily returns, contrary to other common volatility indicators based on implied volatility or historical volatility, which make it a suitable volatility index for the Spanish stock market. Finally, even though the VIBEX-NEW volatility index has not been constructed with a forecasting goal in mind, it can produce forecasts of IBEX-35 realized volatility at least as good as those emerging from historical and conditional volatility measures from a GARCH(1,1). A feasible volatility correction methodology is proposed to achieve it.
Highlights
In 1993, the CBOE (Chicago Board Option Exchange) first published the VIX index with the intention of providing information on the perception of risk in the US stockM
We examine the stability of the loss function dynamic for the VIBEX-NEW, which informs about the stability of the volatility risk premium in the Spanish Financial Market
Since VIBEXNEW includes OTM call and put prices its information content is wider than the content in the model-based volatility index and its computation is more simple than the model-based which allows the valuation of derivatives on the volatility index
Summary
In 1993, the CBOE (Chicago Board Option Exchange) first published the VIX index with the intention of providing information on the perception of risk in the US stock. Blair et al (2001), Engle and Gallo (2007), Giot and Laurent (2007), Fleming et al (1995), and Corrado and Miller (2005), among others, conclude in favor of the forecasting ability of US volatility indices, whereas Becker et al (2007) and Bali and Weinbaum (2007) conclude that the VIX index does not add information to model-based volatility forecasts The results of this exercise show that VIBEXNEW demonstrates a forecasting ability similar to that of historical or implied volatility indicators, after we apply a methodology to reduce, but no remove, the forecasting bias. The article ends with conclusions and questions that remain open for further research
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