Study on the Interval Time Series Model

  • 王 維敬

Student thesis: Master's Thesis


In financial economics a large number of analysis and models were developed based on the daily closing prices or even at lower frequencies such as weekly or monthly It may discard some valuable intra-daily information such as the highest and lowest prices We may regard the highest and lowest prices as an interval valued observations and use some symbolic data methodologies to deal with them When modelling the interval time series the most difficulty is to avoid the maximum and minimum values to be crossed with each other Most of literatures deal this problem by changing the interval time series to be the center and radius of the intervals Nevertheless due to the normal assumption for the innovation term the radius processes may not ensure to be positive Alternatively Teles and Brito (2015) proposed the space-time autoregressive (STAR) models STAR model can exactly ensure the predicted upper values to be larger than lower values but can not in generating simulated data In this paper we combine the STAR model with multivariate GARCH to deal with the heteroscedasticity of financial data Alternatively we propose a model which directly uses the concept of symbolic data and considers the time varying noise terms simultaneously namely heteroscedastic auto-inter regressive (HAIR) model In model comparison we consider a practically oriented experiment based on 2016 S&P500 index and provide the comparisons of models we mentioned In real data analysis we investigate the in-sample and out-of-sample behavior for each model
Date of Award2017 Jul 21
Original languageEnglish
SupervisorLiang-Ching Lin (Supervisor)

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