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THE PREDICTABILITY OF STOCK MARKET RETURNS IN SOUTH AFRICA: PARAMETRIC VS. NON‐PARAMETRIC METHODS
Author(s) -
BONGABONGA LUMENGO,
MWAMBA MUTEBA
Publication year - 2011
Publication title -
south african journal of economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.502
H-Index - 31
eISSN - 1813-6982
pISSN - 0038-2280
DOI - 10.1111/j.1813-6982.2011.01280.x
Subject(s) - heteroscedasticity , autoregressive conditional heteroskedasticity , econometrics , parametric statistics , predictability , autoregressive model , univariate , parametric model , semiparametric model , economics , stock (firearms) , stock market , conditional probability distribution , nonparametric statistics , volatility (finance) , mathematics , statistics , multivariate statistics , geography , context (archaeology) , archaeology
This paper compares the forecasting performance of a sub‐class of univariate parametric and non‐parametric models in predicting stock market returns in South Africa. To account for conditional heteroskedasticity in stock returns data, the non‐parametric model is generated by the conditional heteroskedastic non‐linear autoregressive (NAR) model, while the parametric model is produced by the generalised autoregressive conditional heteroskedastic in mean (GARCH‐M) model. The results of the paper show that the NAR as a non‐parametric model performs better than the GARCH‐M model in short‐term forecasting horizon, and this indicates the importance of a distribution‐free model in predicting stock returns in South Africa.