Time-varying information share and autoregressive loading factors: evidence from S&P 500 cash and E-mini futures markets

Yang Hou (), Steven Li () and Fenghua Wen ()
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Yang Hou: University of Waikato
Steven Li: RMIT University
Fenghua Wen: Central South University

Review of Quantitative Finance and Accounting, 2021, vol. 57, issue 1, No 4, 110 pages

Abstract: Abstract The error correction coefficients, known as the loading factors, are a key component for information share. To date, only constant loading factors have been considered for information share. This paper attempts to consider the autoregressive loading factors and their implications on the information share. Based on the minute-by-minute data from the S&P 500 cash and E-mini futures markets, this paper reveals that the loading factors are indeed autoregressive. Furthermore, we propose three AR(1) processes for the loading factors and assess their performance in information share compared to the constant loading factor model. Overall, this research provides supporting empirical evidence for using autoregressive loading factors for the information share measurement.

Keywords: Price discovery; Information share; S&P 500 E-mini futures; AGDCC GARCH; Loading factor; Error correction coefficient (search for similar items in EconPapers)
JEL-codes: G13 G14 G15 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11156-020-00940-7

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