In this paper we analyse the performances of a novel approach to modelling non-linear conditional heteroscedastic time series characterised by asymmetries in both the conditional mean and variance. This is achieved by combining a TAR model for the conditional mean with a Changing Parameters Volatility (CPV) model for the conditional variance. Empirical results are given for the daily returns of the S&P 500, NASDAQ composite and FTSE 100 stock market indexes.
A Non-linear time series approach to modelling Asymmetry in Stock market Indexes
AMENDOLA, Alessandra;STORTI, Giuseppe
2002-01-01
Abstract
In this paper we analyse the performances of a novel approach to modelling non-linear conditional heteroscedastic time series characterised by asymmetries in both the conditional mean and variance. This is achieved by combining a TAR model for the conditional mean with a Changing Parameters Volatility (CPV) model for the conditional variance. Empirical results are given for the daily returns of the S&P 500, NASDAQ composite and FTSE 100 stock market indexes.File in questo prodotto:
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