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Estimating Index Volatility with Volume-Weighted Intrinsic Entropy

Article arXiv papers · Author: Claudiu Vinte et al.
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Summary

This paper presents an intrinsic entropy model for estimating historical volatility in stock market indices. Unlike estimators based only on daily open, high, low, and close prices, the proposed approach also incorporates trading volume. It adapts an earlier intraday model by linking daily price levels to each day's share of total volume over the period being analyzed, treating that share as a measure of market credence at the price level.

The authors calculate estimates from historical daily data for several US and Asian indices and compare them with commonly used industry estimators across multiple time frames. They report that the entropy model produces estimates they characterize as reliable, with a notably high coefficient of variation and values in a substantially lower range than those from the other advanced estimators. The summary provides no detailed validation design or evidence about how the estimates perform in trading or derivatives pricing, so the reported comparison should not be read as proof of forecasting or investment value.

Key ideas

  • The intrinsic entropy estimator uses trading volume alongside daily OHLC prices.
  • Each day's volume share is treated as market credence associated with its price level.
  • The model is evaluated on historical data from US and Asian stock market indices.
  • Its estimates are compared with established volatility estimators across multiple time frames.
  • The reported estimates have high variation and lie below those from the other advanced estimators.

Tags

EquitiesVolatilityStatisticsUS marketsChina markets
Full text
# A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model


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









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.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.