Open AccessOn Mixture GARCH Models: Long, Short Memory and Application in Finance
Author(s) -
Halim Zeghdoudi,
M. Amrani
Publication year - 2021
Publication title -
journal of mathematics and statistics studies
Language(s) - English
Resource type - Journals
ISSN - 2709-4200
DOI - 10.32996/jmss.2021.2.2.1
Subject(s) - autoregressive conditional heteroskedasticity , heteroscedasticity , econometrics , long memory , volatility (finance) , autoregressive model , computer science , short term memory , economics , working memory , psychology , cognition , neuroscience
In this work, we study the famous model of volatility; called model of conditional heteroscedastic autoregressive with mixed memory MMGARCH for modeling nonlinear time series. The MMGARCH model has two mixing components, one is a GARCH short memory and the other is GARCH long memory. the main objective of this search for finds the best model between mixtures of the models we made (long memory with long memory, short memory with short memory and short memory with long memory) Also, the existence of its stationary solution is discussed. The Monte Carlo experiments demonstrate we discovered theoretical. In addition, the empirical application of the MMGARCH model (1, 1) to the daily index DOW and NASDAQ illustrates its capabilities; we find that for the mixture between APARCH and EGARCH is superior to any other model tested because it produces the smallest errors.