Forecasting Malaysian gold using a hybrid of ARIMA and GJR-GARCH models

Ahmad, Mohd Hamdan; Ping, Pung Yean; Yaziz, Siti Roslindar; Miswan, Nor Hamizah

doi:10.12988/ams.2015.5124

Cited by 5 publications

(5 citation statements)

References 6 publications

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“… D t − k =1 if normalε t − k < 0, and 0 otherwise. According to Chen et al (2008) and Ahmad et al (2015), bad news is defined where normalε t − k < 0; otherwise, normalε t − k ≥ 0 is considered good news, where USD news affects the USD/TWD spot rate change on the t-k th trading day ( normalR t − k ). It has a differential effect on the model of conditional variance; good news on the USD has an impact of a k , while bad news for USD has an impact of a k + b k .…”

Section: The Percentage Of the Daily Exchange Rate Is Calculated Asmentioning

confidence: 99%

“…Given that p = 1, r = 1, and q = 1 is determined by Akaike's Information Criterion and Schwarz's Information Criterion, Heteroskedastic Test is verified using ARCH Test. We use k = 1 and the GJR-GARCH (1,1) model to capture the heteroscedasticity of the residual variance equation (Ahmad et al, 2015), and the formula becomes:…”

Section: Models Of Previous Day Trading Information Setsmentioning

confidence: 99%

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The Impact of Trading Information Sets on Exchange Rate Change and Volatility: Evidence From Taiwan

Liu

2021

Sage Open

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This study explores the Taiwan Dollar (TWD) as the currency of a small island economy, uses the trading information sets from overseas and the market itself to examine the impacts on the adjustment of daily spot exchange rates. The daily USD/TWD is explained by the trading information sets, contain which the daily trading activities and the ratio of the real body on the daily candlestick chart of technical analysis on the Taipei Foreign Exchange Market, as well as the US-dollar index return to explain the USD/TWD spot rate change. The results showed that some of the USD/TWD changes were related to the US-dollar index return on overseas, and that the effect of the US-dollar index return was not limited to the adjustment rate from the previous closing rate to the opening rate on the day, which would affect the adjustment spot exchange rate in the intraday opening-to-closing period. There is a significant positive relationship between the real body ratio of the daily candlestick chart and the return of the exchange rate, supporting the real body ratio related to the change of the exchange rate. The study model can greatly improve the model interpretation ability of the change of exchange rate by about 50% after considering the trading activity factors. Finally, this study found that the volatility has a positive effect on Mondays and the 2008-financial crisis, and based on the shock that the news of depreciation was higher than the news of appreciation, so there exist asymmetry volatility.

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Section: The Percentage Of the Daily Exchange Rate Is Calculated Asmentioning

confidence: 99%

Section: Models Of Previous Day Trading Information Setsmentioning

confidence: 99%

The Impact of Trading Information Sets on Exchange Rate Change and Volatility: Evidence From Taiwan

Liu

2021

Sage Open

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“…In time series analysis, a popular model to predict future points in the series is autoregressive moving average (ARMA) model. However, in cases where data show evidence of non-stationarity, a generalization of ARMA, called autoregressive integrated moving average (ARIMA) model should be applied [2]. To use such models, non-stationarity which can exist in mean and/or in variance must be removed.…”

Section: Arma/arima Modelsmentioning

confidence: 99%

“…In this study, ARIMA model is developed since it can provide an evolution equation with a simple interpretation [1]. However, since the exchange rate series present a high volatility, ARIMA model is hybridized with GARCH family models [2]. GARCH models can be categorized as symmetric and asymmetric models [3].…”

Section: Introductionmentioning

confidence: 99%

Modelling and forecasting US Dollar/Malaysian Ringgit exchange rate

Mustafa¹,

Ahmad²,

Ismail³

2017

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Exchange rate forecasts are important because these forecasts help in hedging decisions, capital budgeting decisions and earnings assessments. While numerous methods are available for forecasting exchange rates, the current study employs time series models to forecast daily data of US Dollar exchange rate against Malaysian Ringgit (USD/MYR). Using hybrid ARIMA-GARCH and hybrid ARIMA-EGARCH models, the modelling and forecasting performances are compared using Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE) respectively. Such findings are important since exchange rate forecasts can help to evaluate the foreign denominated cash flows involved in international transactions.

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“…The model of Box-Jenkins -GARCH is proven as the promising method to analyse and forecast a higher volatile data series such as gold price [1][2][3][4][5][6], electricity price [7,8], internet traffic [9] and traffic flow [10]. However, there is no discussion on the appropriate sample size using the model in the previous literature.…”

Section: Introductionmentioning

confidence: 99%

Determination of sample size for higher volatile data using new framework of Box-Jenkins model with GARCH: A case study on gold price

Yaziz

Zakaria

Ahmad

2017

J. Phys.: Conf. Ser.

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Abstract.The model of Box-Jenkins -GARCH has been shown to be a promising tool for forecasting higher volatile time series. In this study, the framework of determining the optimal sample size using Box-Jenkins model with GARCH is proposed for practical application in analysing and forecasting higher volatile data. The proposed framework is employed to daily world gold price series from year 1971 to 2013. The data is divided into 12 different sample sizes (from 30 to 10200). Each sample is tested using different combination of the hybrid BoxJenkins -GARCH model. Our study shows that the optimal sample size to forecast gold price using the framework of the hybrid model is 1250 data of 5-year sample. Hence, the empirical results of model selection criteria and 1-step-ahead forecasting evaluations suggest that the latest 12.25% (5-year data) of 10200 data is sufficient enough to be employed in the model of Box-Jenkins -GARCH with similar forecasting performance as by using 41-year data. IntroductionThe model of Box-Jenkins -GARCH is proven as the promising method to analyse and forecast a higher volatile data series such as gold price [1][2][3][4][5][6], electricity price [7,8], internet traffic [9] and traffic flow [10]. However, there is no discussion on the appropriate sample size using the model in the previous literature. Therefore, this paper is aimed to propose the framework using the Box-Jenkins -GARCH on how to determine the optimal sample size for forecasting purposes. According to Hyndman and Kostenko, the number of data required for any statistical model depends on at least two things: the number of model parameters to estimate and the amount of random variation in the data [11]. In other words, a reasonable approach to determine the appropriate sample size for forecasting model is to ensure that there is enough data to estimate the model and the model performs well out-ofsample evaluation.In order to obtain a parsimonious estimated model, the Akaike Information Criteria (AIC) and the Schwarz Information Criterion (SIC) are applied. While, to evaluate the forecasting performance, the out-of-sample 1-step-ahead forecasting evaluations that are the mean square error (MSE), the root mean square error (RMSE) and the mean absolute error (MAE) are applied. As the sample size increasing, minimising the AIC is equivalent to minimising the out-of-sample 1-step-ahead MSE [12]. The method of the selection criteria and the forecasting evaluations are incorporated in the proposed framework in finding the optimal sample size. The proposed framework is illustrated using world daily gold price. To the best of our knowledge, this study is considered a pioneer in determining the optimal sample size for the model of Box-Jenkins -GARCH.

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Forecasting Malaysian gold using a hybrid of ARIMA and GJR-GARCH models

Cited by 5 publications

References 6 publications

The Impact of Trading Information Sets on Exchange Rate Change and Volatility: Evidence From Taiwan

The Impact of Trading Information Sets on Exchange Rate Change and Volatility: Evidence From Taiwan

Modelling and forecasting US Dollar/Malaysian Ringgit exchange rate

Determination of sample size for higher volatile data using new framework of Box-Jenkins model with GARCH: A case study on gold price

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