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On the Distribution Estimation of Power Threshold Garch Processes
Author(s) -
Gonçalves Esmeralda,
Leite Joana,
MendesLopes NazarÉ
Publication year - 2016
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12173
Subject(s) - mathematics , autoregressive conditional heteroskedasticity , autoregressive model , heteroscedasticity , econometrics , distribution (mathematics) , setar , conditional probability distribution , estimation , statistics , star model , volatility (finance) , time series , mathematical analysis , economics , autoregressive integrated moving average , management
The aim of this article is to estimate the probability distribution of power threshold generalized autoregressive conditional heteroskedasticity processes by establishing bounds for their finite dimensional laws. These bounds only depend on the parameters of the model and on the distribution function of its independent generating process. The application of this study to some particular models allows us to conjecture that this procedure is an adequate alternative to the corresponding estimation using the empirical distribution functions, particularly useful in the development of control charts for this kind of models.