Abstract
In this paper, we account for memory failure or otherwise in the daily evolution of stock return and volatility within the purview of short and long ranges based on the arrival of fundamental news. This accounts for the return on assets in the current period to be a function of returns realized in the pasts. To achieve this objective, we estimated ARMA, ARFIMA, GARCH, FIGARCH and HYGARCH models. After implementing maximum likelihood estimation technique, we found out that the ARMA coefficients were not significant, the GARCH coefficients were significant and the memory coefficients in terms of ARFIMA, FIGARCH and HYGARCH were statistically significant. In the light of these, we propose the rejection of efficient hypothesis in the long range and document a single memory in volatility in the short range. The study recommends that ARFIMA and HYGARCH are the best forecasting models for return and volatility respectively in the Nigerian stock market.
Highlights
Changes in prices reflect the random arrivals of fundamental news in the stock market
In the study we applied batteries of ARMA and GARCH to examine the daily evolution of stock return and volatility in Nigerian stock market over short and long range horizons
We show that the long-range memory parameter is significant in the HYGARCH model, and we recommend that HYGARCH model is the best predictive model for speculators in Nigeria, since the HYGARCH error seems to have permanent effects on conditional volatility
Summary
Changes in prices reflect the random arrivals of fundamental news in the stock market. In the study we applied batteries of ARMA and GARCH to examine the daily evolution of stock return and volatility in Nigerian stock market over short and long range horizons. In furtherance of the significance of information in stock-return volatility, Balibey & Turkyilmaz (2014) examined the convenience of the FIGARCH (1, d, 1) and FIAPARCH (1, d, 1) models in evaluating asymmetry features and long memory in the volatility of the Turkish Stock Market.
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