Forecasting Malaysian gold using GARCH model

Ping, Pung Yean; Miswan, Nor Hamizah; Ahmad, Mohd Hamdan

doi:10.12988/ams.2013.13255

Cited by 14 publications

(7 citation statements)

References 2 publications

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“…Based on ARCH-GARCH modelling on Kijang Emas gold data from August 2005 to July 2018, several noteworthy results were found. First, GARCH(1,1) was found to be the best model in capturing long term memory process of gold volatility in comparison to higher order of ARCH(4) process and in line with Ping et al (2013). Gold price volatility was persistent across all periods.…”

Section: R E S U L T Smentioning

confidence: 81%

Modelling the Time-Varying Gold Price Volatility in Malaysia (2005 â€“ 2018)

Wahab

Arif

2018

Asia Pr soc sci

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This study provides new insights into the recent time-varying pattern of gold volatility surrounding the Subprime Global Financial Crisis (GFC) 2008 with special emphasis given to small open economy of Malaysia. Using daily data of gold bullion Kijang Emas issued by the Bank Negara Malaysia from August 2005 till July 2018, the study modelled the gold volatility using different models of ARCH-GARCH family. Under the context of emerging economy, the study found significant enhanced gold volatility during the middle of GFC, signifying that gold price is highly sensitive to specific economic disturbance. Next, our result displayed inverted asymmetry effect where investors interpret gold as a safe-haven investment during good gold returns, hence contributed to enhanced gold volatility. Finally, bilateral exchange rate (MYR/USD) affected volatility with positive sign, indicating that gold investment could be adversely affected during the depreciation of the Malaysian Ringgit.

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Section: R E S U L T Smentioning

confidence: 81%

Modelling the Time-Varying Gold Price Volatility in Malaysia (2005 â€“ 2018)

Wahab

Arif

2018

Asia Pr soc sci

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“…GARCH is proven to have better performance than ARIMA if the data have high volatility. This can be verified by conducting the Lagrange Multiplier (LM) test [28,34]. The results indicate that ARIMA cannot predict well due to the volatility of the data.…”

Section: Generalized Autoregressive Conditional Heteroscedasticity (Garch)mentioning

confidence: 85%

“…From the review, GARCH has been deployed in different domains for forecasting data with high volatility, such as the prices of gold [28,29], stocks and the financial market [30][31][32][33], agricultural products [34], and power and electricity [12,35] from 2005 to 2021. GARCH is proven to have better performance than ARIMA if the data have high volatility.…”

Section: Generalized Autoregressive Conditional Heteroscedasticity (Garch)mentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.

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“…So far, mixed results were documented and general findings remained inconclusive. Ping et al (2013) conducted forecast study on gold bullion Kijang Emas from 2001 to 2012 using ARIMA-GARCH model. They concluded that ARIMA(1, 1, 1) -GARCH(1, 1) is the best model in forecasting gold prices based on SIC and AIC performance criteria.…”

Section: Modelling Gold Volatilitymentioning

confidence: 99%

Assessing recent pattern of gold price volatility in Malaysia (2005-2018)

Wahab

Arif

2020

IJSSS

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Gold price has been subjected to volatility clustering since the abolishment of the gold standard in August 1971. While there are myriad studies focused on developed economies, studies on the pattern of volatility in gold especially under the context of small open economy of Malaysia are still lacking. Attempting to fill in the gap, this study provides insight into the trend of gold price volatility as well as to examine the time variant nature of gold volatility surrounding the US subprime crisis. This study also provides a new perspective on the transmission between bilateral exchange rate (MYR/USD) and gold price. This study covered recent daily data from August 2005 to July 2018 using different models of ARCH-GARCH including ARCH, GARCH, EGARCH and TGARCH. Accordingly, gold price volatility was persistent across all periods. Enhanced volatility was evident during the 2008 global financial crisis (GFC) owing to high speculative activities and clustered news over the period. In term of asymmetry response, there was inverted asymmetry effect where positive shock raised volatility in gold. Finally, bilateral exchange rate (MYR/USD) affected gold price volatility with positive relationship, implicating the high sensitivity of gold investment during the depreciation of local currency.

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Forecasting Malaysian gold using GARCH model

Cited by 14 publications

References 2 publications

Modelling the Time-Varying Gold Price Volatility in Malaysia (2005 â€“ 2018)

Modelling the Time-Varying Gold Price Volatility in Malaysia (2005 â€“ 2018)

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

Assessing recent pattern of gold price volatility in Malaysia (2005-2018)

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