Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Malaysia market properties and shares

Miswan, Nor Hamizah; Hamzah, Khairum; Ngatiman, Nor Azazi; Zamzamin, Zaminor Zamzamir

doi:10.12988/ams.2014.47548

Cited by 10 publications

(9 citation statements)

References 5 publications

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“…Many researchers believe that GARCH and EGARCH models cannot provide the best results compared with ARIMA models, and that ARIMA is the best model for forecasting and modeling stock prices (Miswan et al 2014;Pahlavani and Roshan 2015). Hence, the ARIMA model is appropriate to predict stock returns accurately with prospective market strategies to be followed by investors.…”

Section: Methodsmentioning

confidence: 99%

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020

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This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

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Section: Methodsmentioning

confidence: 99%

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020

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“…e trend of the PACF plot tends to cut off at lag 3 or lag 4 for AD and CL, which implies that the order or the parameters of the partial autocorrelation function were AR (4) or AR (4). e PACF plot tends to cut off at lag 2 or lag 3 for C and FC, which implies that the order or the parameters of the partial autocorrelation function were AR (2) or AR (3). e PACF plot tends to cut off at lag 5 or lag 7 for ES, which implies that the order of the parameters of the partial autocorrelation function were AR (5) or AR (7).…”

Section: Determining the Arima Model Ordermentioning

confidence: 98%

“…Final models were estimated using minimum AIC and BIC values. GARCH (1,1) for AD, GARCH (1,1) for C, GARCH (1,2) for CL, GARCH (1,2) for ES, GARCH (1,1) for FC, GARCH (3,4) for GC, GARCH (1,1) for KC, and GARCH (1,1) for the US were selected using minimum AIC and BIC values.…”

Section: Model Identificationmentioning

confidence: 99%

“…Sometimes, the market is unstable with enormous oscillations. Variation of the market variance over time is the volatility, and this changes from hugely high and low prices [3,4]. e time series usage in volatile research in finance and econometrics is not limited to approximation matters, statistical abstractions, and pattern identification.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting of Global Market Prices of Major Financial Instruments

Divisekara

Nawarathna

2020

Journal of Probability and Statistics

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One of the easiest and fastest ways of building a healthy financial future is investing in the global market. However, the prices of the global market are highly volatile due to the impact of economic crises. Therefore, future prediction and comparison lead traders to make the low-risk decisions with price. The present study is based on time series modelling to forecast the daily close price values of financial instruments in the global market. The forecasting models were tested with two sample sizes, namely, 5-year close price values for correlation analysis and 3-year close price values for model building from 2013 January to 2018 January. The forecasting capabilities were compared for both ARIMA and GARCH class models, namely, TGARCH, APARCH, and EGARCH. The best-fitting model was selected based on the minimum value of the Akaike information criterion (AIC) and Bayesian information criteria (BIC). Finally, the comparison was carried out between ARIMA and GARCH class models using the measurement of forecast errors, based on the Root Mean Square Deviation (RMSE), Mean Absolute Error (MAE), and Mean absolute percentage error (MAPE). The GARCH model was the best-fitted model for Australian Dollar, Feeder cattle, and Coffee. The APARCH model provides the best out-of-sample performance for Corn and Crude Oil. EGARCH and TGARCH were the better-fitted models for Gold and Treasury bond, respectively. GARCH class models were selected as the better models for forecasting than the ARIMA model for daily close price values in global financial market instruments.

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“…The third step, diagnostic checks, is used for the purpose of determining any possible inadequacies in the model, and the process is repeated in case there are any is inadequacies. The last step, once arriving at an adequate model, is concerned with generating "optimal" forecasts by recursive calculation [9].The ARIMA process consists of three processes: is the number of autoregressive (AR) terms, is the number of difference taken and is the number of moving average ( )terms [10]. Seasonal and non-seasonal ARIMA models: The general non-seasonal model is known as ARIMA (p,d,q) while the general seasonal model is known as ARIMA (p,d,q) (P,D,Q).…”

Section: Arima Modelmentioning

confidence: 99%

A comparison among two composite models (without regression processing) and (with regression processing), applied on Malaysian imports

Milad¹,

Ibrahim²,

Marappan³

2015

ams

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The current paper reports an empirical study on the use of a composite model for developing a statistical model to predict the value of imports of the crude material in Malaysia. It is a combination of both regression and autocorrelation integrated moving average (ARIMA) models to produce a better forecast. The study reported in this paper mainly aimed at comparing the two composite models: the first model (Without Regression Processing) and the second model (With Regression Processing). The initialization set of the data has 91 data points, starting from first quarter 1991 to third quarter 2013. The general steps followed in comparing the two models were test significance of parameters and the minimum value of the statistical measure of forecast error (Thiel Coefficient) of its forecasting power. The results showed the composite model (With Regression Processing) had better significant and a lower percentage of U-statistics. This implies that the model had a substantially better fit.

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Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Malaysia market properties and shares

Cited by 10 publications

References 5 publications

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

Forecasting of Global Market Prices of Major Financial Instruments

A comparison among two composite models (without regression processing) and (with regression processing), applied on Malaysian imports

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