Stock market daily volatility and information measures of predictability

• Published in: Physica A Vol. 518; pp. 22 - 29
• Main Authors: D’Amico, Guglielmo, Gismondi, Fulvio, Petroni, Filippo, Prattico, Flavio
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.03.2019
• Subjects: Entropy; Forecasting; GARCH; Information theory; Predictability; Entropy; GARCH; Predictability; Forecasting; Information theory
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2018.11.049

The main purpose of this work is to investigate the relation between some measures in information theory and the accuracy of volatility forecasting using a model of asset returns. First we highlight the dependence between volatility forecasting and entropy and then we determine the relation between predictability and volatility. The study is conducted using a database of 65 stocks of the Dow Jones Composite Average from 1973 to 2014 and by computing the daily volatility of the market index. To this end we use the standard GARCH approach to model and forecast the daily volatility. The main result of this paper is the establishment of a relationship between the accuracy of the volatility forecast and the entropy of the time series of price returns. Since the entropy changes in time, before computing a forecast of the volatility it is recommended to compute the entropy of the time series that furnishes an important indicator on the limit of successive volatility forecast. •We apply Information Theory to financial markets.•We show the relation between entropy e predictability.•We apply our analysis on a DJ index.