Modelling gold price using ARIMA–TGARCH

Roslindar, Yaziz Siti; Azlinna, Azizan Noor; Hura, Ahmad Maizah; Zakaria, Rosnah

doi:10.12988/ams.2016.511716

Cited by 8 publications

(9 citation statements)

References 16 publications

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“…In recent years, different studies have applied hybrid forecasting models in various fields, and have shown a good performance for rainfall data [ 72 ], for the price of gold [ 73 ], for forecasting daily load patterns of energy [ 74 ], and for stock market prices [ 75 ].…”

Section: Methodsmentioning

confidence: 99%

A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings

Ramírez

Zarzo

Perles

et al. 2021

Sensors

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The famous Renaissance frescoes in Valencia’s Cathedral (Spain) have been kept under confined temperature and relative humidity (RH) conditions for about 300 years, until the removal of the baroque vault covering them in 2006. In the interest of longer-term preservation and in order to maintain these frescoes in good condition, a unique monitoring system was implemented to record both air temperature and RH. Sensors were installed at different points at the vault of the apse during the restoration process. The present study proposes a statistical methodology for analyzing a subset of RH data recorded by the sensors in 2008 and 2010. This methodology is based on fitting different functions and models to the time series, in order to classify the different sensors.The methodology proposed, computes classification variables and applies a discriminant technique to them. The classification variables correspond to estimates of model parameters of and features such as mean and maximum, among others. These features are computed using values of functions such as spectral density, sample autocorrelation (sample ACF), sample partial autocorrelation (sample PACF), and moving range (MR). The classification variables computed were structured as a matrix. Next, sparse partial least squares discriminant analysis (sPLS-DA) was applied in order to discriminate sensors according to their position in the vault. It was found that the classification of sensors derived from Seasonal ARIMA-TGARCH showed the best performance (i.e., lowest classification error rate). Based on these results, the methodology applied here could be useful for characterizing the differences in RH, measured at different positions in a historical building.

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Section: Methodsmentioning

confidence: 99%

A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings

Ramírez

Zarzo

Perles

et al. 2021

Sensors

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“…2015.; Yaziz et al. 2016). To capture the possibility of this effect, we model a Threshold Generalised Autoregressive Conditional Heteroskedasticity (TGARCH).…”

Section: Data and Research Methodologymentioning

confidence: 99%

Commodity price volatility and stock market returns in an emerging economy

et al. 2022

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The paper examined commodity price volatility and stock returns on the Ghana Stock Exchange (GSE). Utilising Threshold Generalised Autoregressive Conditional Heteroskedasticity (TGARCH) estimation technique, the study also evaluated the likely leverage effect on the stock market and further interrogated if autocorrelation existed in the daily stock returns series, for how many days and on what day does it disappear by specifying an autoregressive model as a mean equation. The study concludes that changes in commodity prices and their volatilities on the international market exert a significant level of diverse impact on daily stock returns and return volatility on the GSE. For instance, whilst coffee price changes result in reduction in daily stock returns, changes in crude and gold prices result in an increment in returns on the GSE. The study also confirms the existence of autocorrelation in the daily stock returns for up to seven days, fading out on the eighth day as well as the existence of the leverage effect on the GSE. This result should influence investment decisions in respect to trading on the GSE. Observance of changes in price of these key commodities could enhance predictions and lead to the formulation of strategies to maximise revenues on the GSE 1

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“…The study concluded that traditional symmetric GARCH model was unable to capture the asymmetry volatility response and negative news induced greater volatility, signifying leverage effect. In the same vein, Yaziz et al (2016) examined the performances of ARIMA-TGARCH models of gold price with five innovations in estimation comprising Gaussian, student's-t, skewed student's-t, generalised error distribution and skewed generalised error distribution. Using a total of 2,845 daily gold series data from the years of 2003 to 2014, the study found that a hybrid ARIMA(0, 1, 0) -TGARCH(1, 1) embedded with t-innovation was the best model in forecasting international gold prices.…”

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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Modelling gold price using ARIMA–TGARCH

Cited by 8 publications

References 16 publications

A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings

A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings

Commodity price volatility and stock market returns in an emerging economy

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

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