论文标题
横截面内在熵。全面的股票市场波动估计器
The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator
论文作者
论文摘要
考虑到股票市场不确定性的时间维度，本文介绍了基于内在熵模型的股票市场波动的横断面估计。拟议的横截面固有熵（CSIE）定义并计算为整个市场的每日波动性估计，基于日常交易价格：开放，高，低价和近距离价格（OHLC），以及纽约证券交易所（NYSE）（NYSE）和国家证券交易协会（NYSE）和全国证券交易的所有符号的每日交易量以及自动销售的自动报价（NASDA）（NASDA）（NASDA）（NASDA）。 We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor's 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the纳斯达克综合指数分别在各种时间间隔中。我们的研究使用大约6000天的参考点，从2001年1月1日开始，直到2022年1月23日，纽约证券交易所和纳斯达克股票。我们发现，与通过市场指数捕获的波动性估计值相比，CSIE市场波动率估计器的敏感性始终敏感至少十倍。此外，与在各个时间间隔和滚动窗口中的波动性风险相比，Beta值确认了整个市场指数的波动风险始终降低，降低了50％至90％。
To take into account the temporal dimension of uncertainty in stock markets, this paper introduces a cross-sectional estimation of stock market volatility based on the intrinsic entropy model. The proposed cross-sectional intrinsic entropy (CSIE) is defined and computed as a daily volatility estimate for the entire market, grounded on the daily traded prices: open, high, low, and close prices (OHLC), along with the daily traded volume for all symbols listed on The New York Stock Exchange (NYSE) and The National Association of Securities Dealers Automated Quotations (NASDAQ). We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor's 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. Our study uses approximately 6000 day reference points, starting on 1 Jan. 2001, until 23 Jan. 2022, for both the NYSE and the NASDAQ. We found that the CSIE market volatility estimator is consistently at least 10 times more sensitive to market changes, compared to the volatility estimate captured through the market indices. Furthermore, beta values confirm a consistently lower volatility risk for market indices overall, between 50% and 90% lower, compared to the volatility risk of the entire market in various time intervals and rolling windows.
