Modelling Malaysian gold using symmetric and asymmetric GARCH models

Abstract

The purpose of the current study is to model Malaysian gold prices, known as Kijang Emas, using a popular class of generalized econometric models called Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and three of its variants. The variant models selected are GARCH in the mean (GARCH-M), Threshold GARCH (TGARCH) and Exponential GARCH (EGARCH). While the standard GARCH and GARCH-M are symmetric models, TGARCH and EGARCH are asymmetric. Using Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) as model selection criteria, the best fit model for modelling Malaysian gold is TGARCH.

Modelling the Time-Varying Gold Price Volatility in Malaysia (2005 – 2018)

“…Our finding concludes that the notion of stable gold price movement is no longer valid for short term investment as gold is highly sensitive to financial crisis and external economic turbulence (Ahmad & Ping, 2014). Our result also suggests that volatility clustering is evident during the middle of financial crisis.…”

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

“…A hybrid model was considered an effective way to improve forecast accuracy [1]. In the study of symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models for forecasting Malaysian gold prices, a variant of GARCH, called TGARCH was shown to outperform GARCH, GARCH-M and EGARCH models [2]. The TGARCH model is a GARCH variant that includes leverage terms for modeling asymmetric volatility clustering.…”

“…ARIMA-GARCH model was developed and it outperformed ARIMA model. However, in the study of symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models for forecasting Malaysian gold prices, a variant of GARCH, called TGARCH was shown to outperform GARCH, GARCH-M and EGARCH models [2]. This paper proposes a hybrid of linear autoregressive moving average (ARIMA) and a variant of non-linear generalized autoregressive conditional heteroscedasticity (GARCH) called GJR-GARCH in modeling and forecasting Malaysian gold price.…”

Modelling and forecasting US Dollar/Malaysian Ringgit exchange rate

“…In developing hybrid ARIMA-GARCH, seven hybrid models were considered. Hybrid ARIMA (2,1,2)-GARCH (1,3) is the most appropriate model because it has the smallest value of AIC of -7.978035. The residuals of ARIMA(2,1,2)-GARCH(1,3) are tested for ARCH effects using ARCH-LM test.…”

“…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]. The current study aims to compare modelling and forecasting performances of hybrid ARIMA and symmetric GARCH, specifically ARIMA-GARCH with hybrid ARIMA and asymmetric GARCH, specifically ARIMA-EGARCH in modeling and forecasting United States Dollar to Malaysian Ringgit rate.…”