The Cross-Sectional Intrinsic Entropy-A Comprehensive Stock Market Volatility Estimator

Area of Science:

• Quantitative Finance
• Financial Econometrics
• Market Volatility Analysis

Background:

• Traditional volatility measures often overlook the temporal dynamics of market uncertainty.
• Accurate estimation of stock market volatility is crucial for risk management and investment strategies.

Purpose of the Study:

• To introduce and validate a novel Cross-Sectional Intrinsic Entropy (CSIE) model for estimating daily stock market volatility.
• To compare the CSIE model's performance against established volatility estimators using historical market data.

Main Methods:

• Defined and computed CSIE using daily Open, High, Low, Close (OHLC) prices and trading volume for all NYSE and NASDAQ-listed stocks.
• Conducted a comparative analysis of CSIE time series against historical volatility from various estimators (close-to-close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang, IE).
• Utilized a dataset spanning approximately 6000 days from January 1, 2001, to January 23, 2022.

Main Results:

• The CSIE market volatility estimator demonstrated at least 10 times greater sensitivity to market changes compared to market index volatility.
• Beta values indicated that market indices exhibit 50% to 90% lower volatility risk than the overall market.

Conclusions:

• CSIE provides a more sensitive and comprehensive measure of daily stock market volatility.
• Market indices may underrepresent the true volatility risk present in the broader market.