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

TL;DR

This paper introduces an intrinsic entropy model incorporating trading volume to estimate stock index volatility, demonstrating its reliability and distinct characteristics compared to traditional models across multiple indices.

Contribution

The paper adapts the intrinsic entropy model to include volume data, providing a novel approach for more comprehensive volatility estimation of stock indices.

Findings

The intrinsic entropy model yields consistent volatility estimates across different time frames.

It produces lower and more stable volatility intervals than traditional estimators.

The model shows high coefficient of variation, indicating sensitivity to market fluctuations.

Abstract

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.