Estimating volatility models in yields of stock market: 2000-2014
DOI:
https://doi.org/10.15381/pc.v20i1.11482Keywords:
rate of return, time series, ARCH, GARCHAbstract
The time series of high frequency observed in the financial and currency markets are characterized by asymmetric, leptokurtic, volatility clustering, show a high persistence in volatility, correlations in the Square, leverage effect, etc. These features are known in the econometric literature as stylized facts. To collect these characteristics of the time series have been raised nonlinear models, among which stand out the ARCH and GARCH models and their possible variants each. In this paper, we will · analyze the different results obtained from the estimation of the proposed models, applied to yields of stock indices.
There are different methods for measuring volatility clustering in financial series, in which the assumption of the error distribution determines the structure of the estimated log likelihood function. In this document the flexibility of ARCH models is exploited to capture the groupings of the volatility of returns. The results indicate that the GARCH ( 3/7, 3/7 ) models in variance exceeds other specification , try to measure the volatility clustering of the major stock indexes in the world.
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Copyright (c) 2015 Rafael Bustamante Romaní
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