Open AccessAplikasi GARCH dalam Mengatasi Volatilitas Pada Data Keuangan
Author(s) -
. Hartati,
Imelda Saluza
Publication year - 2017
Publication title -
jurnal matematika
Language(s) - English
Resource type - Journals
eISSN - 2655-0016
pISSN - 1693-1394
DOI - 10.24843/jmat.2017.v07.i02.p87
Subject(s) - volatility clustering , autoregressive conditional heteroskedasticity , volatility (finance) , heteroscedasticity , econometrics , financial models with long tailed distributions and volatility clustering , economics , autoregressive model , financial market , finance , implied volatility , forward volatility
The financial market is a place or means convergence between demand and supply of a wide range of financial instruments Long-term (over one year). Activities that occur in the financial markets in the long term will form a series of data is often called a time series that contains a set of information from time to time. Practical experience shows that many time series exhibit their periods with great volatility. The greater the volatility, the greater the chance to experience a gain or loss. Important properties are often owned by the data time series in finance, especially to return data that the probability distribution of returns are fat tails (tail fat) and volatility clustering or often referred to as a case heteroskedastisitas. Not all models are able to capture the nature of heteroscedasticity, one of the models that are able to do is Generalized Autoregressive Heteroskedasticity Condition (GARCH). So the purpose of this study was to determine the GARCH model in dealing with the volatility that occurred in the financial data. The results showed that the GARCH model is best suited to see volatility in the financial data.