Improved Dividend Estimation from Intraday Quotes

Entropy

Pontus Söderbäck¹  Jörgen Blomvall¹  Martin Singull² 

[1] Department of Management and Engineering, Production Economics, Linköping University, 581 83 Linköping, Sweden;Department of Mathematics, Linköping University, 581 83 Linköping, Sweden;

关键词: big data adaptation;    dividend estimation;    options markets;    weighted least squares;   

DOI  :  10.3390/e24010095

来源: DOAJ

【 摘 要 】

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.

【 授权许可】

Unknown