TY - GEN
T1 - Analysis and compensation of asynchronous stock time series
AU - Jahandari, Sina
AU - Materassi, Donatello
N1 - Publisher Copyright:
© 2017 American Automatic Control Council (AACC).
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/6/29
Y1 - 2017/6/29
N2 - The accurate computation of statistical quantities from sampled data is of paramount importance in the analysis of economic and financial time series. The Epps effect is an empirically observed phenomenon where the sample correlation between the logarithmic returns of two stock prices decreases as the sampling frequency of data increases. The full explanation of this phenomenon is currently an open problem and several potential contributing factors are reported in the scientific literature. However, asynchronous sampling times in the stock prices is one of the key components originating the Epps effect. This article investigates in a quantitative way how asynchronous price data contribute to the Epps effect by modeling stock prices as correlated geometric Brownian motions and considering trading times as Poisson point processes. Under these assumptions we show that the Epps effect can be considered as a statistical artifact producing a bias on the sample correlation of the logarithmic returns. We also provide an analytic expression describing this bias. This expression can be used to compensate the bias on the sample correlation in order to obtain an unbiased estimate.
AB - The accurate computation of statistical quantities from sampled data is of paramount importance in the analysis of economic and financial time series. The Epps effect is an empirically observed phenomenon where the sample correlation between the logarithmic returns of two stock prices decreases as the sampling frequency of data increases. The full explanation of this phenomenon is currently an open problem and several potential contributing factors are reported in the scientific literature. However, asynchronous sampling times in the stock prices is one of the key components originating the Epps effect. This article investigates in a quantitative way how asynchronous price data contribute to the Epps effect by modeling stock prices as correlated geometric Brownian motions and considering trading times as Poisson point processes. Under these assumptions we show that the Epps effect can be considered as a statistical artifact producing a bias on the sample correlation of the logarithmic returns. We also provide an analytic expression describing this bias. This expression can be used to compensate the bias on the sample correlation in order to obtain an unbiased estimate.
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U2 - 10.23919/ACC.2017.7963097
DO - 10.23919/ACC.2017.7963097
M3 - Conference contribution
AN - SCOPUS:85027061335
T3 - Proceedings of the American Control Conference
SP - 1085
EP - 1090
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 American Control Conference, ACC 2017
Y2 - 24 May 2017 through 26 May 2017
ER -