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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Improved Dividend Estimation from Intraday Quotes.

Pontus Söderbäck1, Jörgen Blomvall1, Martin Singull2

  • 1Department of Management and Engineering, Production Economics, Linköping University, 581 83 Linköping, Sweden.

Entropy (Basel, Switzerland)
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

Utilizing intraday data significantly enhances dividend estimation quality for S&P 500 options. This advanced method, using present values and weighted least squares, outperforms traditional single-time estimates.

Keywords:
big data adaptationdividend estimationoptions marketsweighted least squares

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Area of Science:

  • Quantitative Finance
  • Financial Econometrics
  • Market Microstructure

Background:

  • Financial markets generate vast amounts of intraday data.
  • Current methods often reduce this granular data to single-time points, potentially compromising estimation accuracy.
  • The under-utilization of intraday data impacts the quality of financial quantity estimations.

Purpose of the Study:

  • To investigate the impact of using intraday data on dividend estimation quality.
  • To adapt and generalize existing linear regression methods for high-frequency financial data.
  • To compare the performance of intraday data-based estimation against traditional single-time methods.

Main Methods:

  • Adapted linear regression (ordinary least squares) for intraday data.
  • Generalized the methodology to express dividends as present values of future dividends.
  • Formulated a weighted least squares approach to address heteroscedasticity, with weights derived from market data.
  • Empirical comparison using out-of-sample S&P 500 European options market data.

Main Results:

  • Estimations using intraday data demonstrated statistically significant improvements in quality compared to single-time estimates.
  • Expressing dividends as present values further enhanced estimation quality.
  • The weighted least squares formulation, accounting for heteroscedasticity, also contributed to improved estimation accuracy.

Conclusions:

  • Intraday data offers a substantial advantage for accurate dividend estimation in financial markets.
  • The proposed generalized methodology, incorporating present values and weighted least squares, provides superior estimation quality.
  • This research highlights the importance of leveraging high-frequency data for more reliable financial modeling.