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基于内在熵模型的股票市场指数波动率估计器

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

作者信息

Vințe Claudiu, Ausloos Marcel, Furtună Titus Felix

机构信息

Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania.

School of Business, Brookfield, University of Leicester, Leicester LE2 1RQ, UK.

出版信息

Entropy (Basel). 2021 Apr 19;23(4):484. doi: 10.3390/e23040484.

DOI:10.3390/e23040484
PMID:33921771
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8074134/
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.

摘要

把握股票市场指数的历史波动率并进行准确估计，是金融证券行业及衍生工具定价领域相关人士的两大主要关注点。本文展示了运用内在熵模型来替代估计股票市场指数波动率的结果。与广泛使用的仅考虑与交易价格相关要素（即交易日的开盘价、最高价、最低价和收盘价，简称OHLC）的波动率模型不同，内在熵模型还考虑了所考虑时间范围内的交易量。我们调整了之前为交易所交易证券引入的日内内在熵模型，以便将每日的OHLC价格与相应日交易量与所考虑期间总交易量的比率联系起来。内在熵模型将该比率概念化为赋予相应价格水平的熵概率或市场可信度。内在熵是使用交易市场指数（标准普尔500指数、道琼斯30指数、纽约证券交易所综合指数、纳斯达克综合指数、日经225指数和恒生指数）的历史每日数据来计算的。我们将内在熵模型产生的结果与使用广泛采用的行业波动率估计器对相同数据集获得的波动率估计进行比较。内在熵模型被证明在各个时间框架内始终能提供可靠的估计，同时变异系数显示出特别高的值，与其他先进波动率估计器提供的估计相比，其估计值落在显著更低的区间范围内。

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本文引用的文献

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Market instability and the size-variance relationship.市场不稳定与规模-方差关系。
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Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic.世界主要市场之间的随机性、信息熵和波动性相互依存关系：新冠疫情的作用
Entropy (Basel). 2020 Jul 30;22(8):833. doi: 10.3390/e22080833.
3
The Role of Entropy in the Development of Economics.熵在经济学发展中的作用。
Entropy (Basel). 2020 Apr 16;22(4):452. doi: 10.3390/e22040452.