TY - JOUR
T1 - A new PIN model with application of the change-point detection method
AU - Kao, Chu Lan Michael
AU - Lin, Emily
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/11
Y1 - 2023/11
N2 - The existing PIN models impose a restriction on the number of possible intensity pairs. However, our investigation shows that the number of empirical intensity pairs is significantly more than the one these models assume, and this number changes daily. Therefore, we propose a new model which, by using the change-point detection technique, can adjust this number according to the data. The model also considers autocorrelation, which is lacking in the existing PIN models. In addition, we show that the proposed model can examine how public information transfers to individual stock price and quantify transfer delay.
AB - The existing PIN models impose a restriction on the number of possible intensity pairs. However, our investigation shows that the number of empirical intensity pairs is significantly more than the one these models assume, and this number changes daily. Therefore, we propose a new model which, by using the change-point detection technique, can adjust this number according to the data. The model also considers autocorrelation, which is lacking in the existing PIN models. In addition, we show that the proposed model can examine how public information transfers to individual stock price and quantify transfer delay.
KW - Change-point detection technique
KW - Information transfer delay
KW - Probability of informed trading (PIN)
UR - http://www.scopus.com/inward/record.url?scp=85172004307&partnerID=8YFLogxK
U2 - 10.1007/s11156-023-01194-9
DO - 10.1007/s11156-023-01194-9
M3 - Article
AN - SCOPUS:85172004307
SN - 0924-865X
VL - 61
SP - 1513
EP - 1528
JO - Review of Quantitative Finance and Accounting
JF - Review of Quantitative Finance and Accounting
IS - 4
ER -