Predictability of the KLCI Price Movement: Evidence from the Time Series Models

Venus Khim-Sen Liew, Kian-Ping Lim & Chong-Yi Lai. 2004. Predictability of the KLCI Price Movement: Evidence from the Time Series Models. INTI Journal, Vol. 1, No. 4, 239 – 248. Malaysia: INTI College.

Abstract: This study utilizes autoregressive integrated moving average (ARIMA) time series models to predict the price movement of Kuala Lumpur Composite Index (KLCI). ARIMA-GARCH models, which are ARIMA time series models with GARCH errors (relaxing the normality assumption), are also considered. All fitted models excluding those that exhibit nonstationary autoregressive roots are utilized to generate out-of-sample forecast over the forecast horizons of 1 day, 1 week, 1 month, 3 months, 6 months, 9 months and 1 year. Our forecasting evaluation exercise is based on the commonly use mean absolute percentage error (MAPE). Our results show that all the 17 forecasting models produce MAPE of less than 10% for forecast horizons of 3 months or shorter. For forecast horizons of 6 months and above, the forecast errors ranging from at most 7% in the case of autoregressive integrated (ARI) models to 16% in the case of integrated moving average (IMA) models. The best performing models are the ARIMA (11, 1, 0) models, with and without GARCH errors, which dominate over the others for 5 out of 7 forecast horizons. The results of study also show that ARIMA (1, 1, 0) model is the next best alternative model as far as parsimonious is concerned. To sum, this study demonstrates that securities market forecasters may resort to time series approach, which is less resource demanding, for satisfactory short-run forecasts of stock prices.

Time Series Modelling and Forecasting of Sarawak Black Pepper Price

Liew Khim Sen, Mahendran Shitan & Huzaimi Hussain. 2003. Time Series Modelling and Forecasting of Sarawak Black Pepper Price. Jurnal Akademik, June Issue, 39 – 55. Malaysia: UiTM Sarawak.

Abstract: Pepper is an important agriculture commodity especially for the state of Sarawak. It is important to forecast its price, as this could help the policy makers in coming up with production and marketing plan to improve the Sarawak’s economy as well as the farmers’welfare. In this paper, we take up time series modelling and forecasting of the Sarawak black pepper price. Our empirical results show that Autoregressive Moving Average (ARMA) time series models fit the price series well and they have correctly predicted the future trend of the price series within the sample period of study. Amongst a group of 25 fitted models, ARMA (1, 0) model is selected based on post-sample forecast criteria.

Ringgit-Yen Rate Fits the Non-linear Smooth Transition Autoregressive and Linear Autoregressive Models

Liew Khim Sen and Ahmad Zubaidi Baharumshah. 2002. How Well the Ringgit-Yen Rate Fits the Non-linear Smooth Transition Autoregressive and Linear Autoregressive Models. Pertanika Journal of Social Science and Humanities, Vol. 10, No. 2, 131 – 141. Malaysia: UPM Press.

This study compares the performance of Smooth Transition Autoregressive (STAR) non-linear model and the conventional linear Autoregressive (AR) time series model in forecasting the Ringgit-Yen rate. Based on standard linearity test procedure, we find empirical evidence that the adjustment of the Ringgit-Yen rate towards its long-run Purchasing Power Parity equilibrium follows a non-linearity path. In terms of forecasting ability, results of this study suggest that both the STAR and AR models exceed or match the performance of SRW model based mean absolute forecast error (MAFE) mean absolute percentage forecast error (MAPFE) and mean square forecast error (RMSFE). The results also show that the STAR model outperform the AR model, its linear competitor. Our finding is consistent with the emerging line of research that emphasised the importance of allowing non-linearity in the adjustment of exchange rate toward its long run equilibrium.

The Performance of AICC as an Order Selection Criterion in ARMA Time Series Models

Liew Khim Sen and Mahendran Shitan. 2002. The Performance of AICC as an Order Selection Criterion in ARMA Time Series Models. Pertanika Journal of Science and Technology, Vol. 10, No. 1, 25 – 33. Malaysia: UPM Press.

This study is undertaken with the objective of investigating the performance of Akaike’s Information Corrected Criterion (AICC) as an order determination criterion for the selection of Autoregressive Moving-average or ARMA (p, q) time series models. A simulation investigation was carried out to determine the probability of the AICC statistic picking up the true model. Results obtained showed that the probability of the AICC criterion picking up the correct model was moderately good. The problem of over parameterization existed but under parameterization was found to be minimal. Hence, for any two comparable models, it is always safe to choose the one with lower order of p and q.