This paper analyzes the relationship between the Equity Market Volatility Index and the nine S&P 500 sectors indexes, as well as investigating which of the 42 category-specific Equity Market Volatility (EMV) trackers has a greater impact on each sector index. To achieve this purpose, ARDL and NARDL models are proposed to measure the sector index response to EMV index. To examine which categories of EMV trackers most influence the dynamics of each sector index, an artificial neural network is employed. Findings suggest that there is a symmetric, negative, and significant impact of the EMV on all sector indexes. Energy, materials, and financials are the sectors most sensitive to changes in EMV. The ANN results demonstrate that EMV Categorical Trackers describe accurately the sector indexes return. Although each sector reacts to different categories, the factors that affect two or more sectors are: commodity markets, financial regulation, exchange rates, and trade policy.
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