Abstract
Geometric Process (GP) model is proposed as an alternative model for financial time series. The model contains two components: the mean of an underlying renewal process and the ratio which measures the direction and strength of the dynamic trend pattern over time. They simultaneously account for the uncertainty on the mean and the autoregressive and time-varying nature of the volatility. Compare to the popular GARCH and SV models, this model is simple and easy to implement using the least squares (LS) method.We extend the GP model to analyze the daily asset price range which exhibit threshold and asymmetric effects for some exogenous variables. Models are selected according to mean square error (MSE). Finally forecasting are performed for the best model that allows for both threshold and asymmetric effects.
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Chan, J.S.K., Lam, C.P.Y., Choy, S.T.B. (2014). An Innovative Financial Time Series Model: The Geometric Process Model. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Modeling Dependence in Econometrics. Advances in Intelligent Systems and Computing, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-319-03395-2_5
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DOI: https://doi.org/10.1007/978-3-319-03395-2_5
Publisher Name: Springer, Cham
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