TY - JOUR
T1 - Modeling financial interval time series
AU - Lin, Liang Ching
AU - Sun, Li Hsien
N1 - Publisher Copyright:
© 2019 Lin, Sun. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/2
Y1 - 2019/2
N2 - In financial economics, a large number of models are developed based on the daily closing price. When using only the daily closing price to model the time series, we may discard valuable intra-daily information, such as maximum and minimum prices. In this study, we propose an interval time series model, including the daily maximum, minimum, and closing prices, and then apply the proposed model to forecast the entire interval. The likelihood function and the corresponding maximum likelihood estimates (MLEs) are obtained by stochastic differential equation and the Girsanov theorem. To capture the heteroscedasticity of volatility, we consider a stochastic volatility model. The efficiency of the proposed estimators is illustrated by a simulation study. Finally, based on real data for S&P 500 index, the proposed method outperforms several alternatives in terms of the accurate forecast.
AB - In financial economics, a large number of models are developed based on the daily closing price. When using only the daily closing price to model the time series, we may discard valuable intra-daily information, such as maximum and minimum prices. In this study, we propose an interval time series model, including the daily maximum, minimum, and closing prices, and then apply the proposed model to forecast the entire interval. The likelihood function and the corresponding maximum likelihood estimates (MLEs) are obtained by stochastic differential equation and the Girsanov theorem. To capture the heteroscedasticity of volatility, we consider a stochastic volatility model. The efficiency of the proposed estimators is illustrated by a simulation study. Finally, based on real data for S&P 500 index, the proposed method outperforms several alternatives in terms of the accurate forecast.
UR - http://www.scopus.com/inward/record.url?scp=85061488569&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0211709
DO - 10.1371/journal.pone.0211709
M3 - 期刊論文
C2 - 30763341
AN - SCOPUS:85061488569
SN - 1932-6203
VL - 14
JO - PLoS ONE
JF - PLoS ONE
IS - 2
M1 - e0211709
ER -