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Grasping the historical volatility of stock market indices and accuratelyestimating are two of the major focuses of those involved in the financialsecurities industry and derivative instruments pricing. This paper presents theresults of employing the intrinsic entropy model as a substitute for estimatingthe volatility of stock market indices. Diverging from the widely usedvolatility models that take into account only the elements related to thetraded prices, namely the open, high, low, and close prices of a trading day(OHLC), the intrinsic entropy model takes into account the traded volumesduring the considered time frame as well. We adjust the intraday intrinsicentropy model that we introduced earlier for exchange-traded securities inorder to connect daily OHLC prices with the ratio of the corresponding dailyvolume to the overall volume traded in the considered period. The intrinsicentropy model conceptualizes this ratio as entropic probability or marketcredence assigned to the corresponding price level. The intrinsic entropy iscomputed using historical daily data for traded market indices (S&P 500, Dow30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). Wecompare the results produced by the intrinsic entropy model with the volatilityestimates obtained for the same data sets using widely employed industryvolatility estimators. The intrinsic entropy model proves to consistentlydeliver reliable estimates for various time frames while showing peculiarlyhigh values for the coefficient of variation, with the estimates falling in asignificantly lower interval range compared with those provided by the otheradvanced volatility estimators.

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model