Modeling and forecasting crude oil markets using ARCH-type models

Cheong, Chin Wen

doi:10.1016/j.enpol.2009.02.026

Cited by 187 publications

(119 citation statements)

References 54 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…In the literature there is no reconciliation of a best fit model to explore conditional volatility, some studies provide strong support for component GARCH to capture the long memory volatility far more superior than the other GARCH models. However, some other studies argue that the simple non-parametric GARCH model outperforms the parametric models (Cheong, 2009;Vivian and Wohar, 2012). We aim to set a baseline to measure volatility persistence and asymmetry by conducting the linear specification models, while our effort shifts to long memory testing together with the asymmetry features in pertinence to the non-linear models.…”

Section: Garch Modelmentioning

confidence: 99%

Volatility Modeling and Forecasting of Istanbul Gold Exchange (IGE)

Gencer¹,

Musoglu²

2014

International Journal of Financial Research

View full text Add to dashboard Cite

In this study, we model the volatility dynamics of Istanbul Gold Exchange with several GARCH models, which incorporate asymmetry and long-range dependence in the conditional volatility. We use daily spot prices of the gold exchange from January 4, 2006 to November 20, 2013. In addition, forecasting performances of the models are evaluated based on three commonly used measures. We examine the out-of-sample predictions for 1, 5 and 20-days ahead as the portfolio managers usually focus on longer horizons (20-days) as well as relatively short horizons (1 and 5-days ahead). The forecasting results point out the superior performance of EGARCH and CGARCH models. Hence our results provide useful information for investors and portfolio managers to consider asymmetric reactions to the news and long-range dependence property of the gold market in Turkey.

show abstract

Section: Garch Modelmentioning

confidence: 99%

Volatility Modeling and Forecasting of Istanbul Gold Exchange (IGE)

Gencer¹,

Musoglu²

2014

International Journal of Financial Research

View full text Add to dashboard Cite

show abstract

“…Furthermore, Haas et al (2004), Agnolucci (2009), Cheong (2009) and Jo (2012 devoted effort to analyzing the volatility of the oil prices. Several studies forecasted the oil prices from standard econometric techniques and intelligent computing models such as artificial neural networks and fuzzy expert systems (see for example, Abramson and Finizza, 1991;Pan et al, 2009;and Azadeh et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Constructing a Composite Leading Indicator for the Global Crude Oil Price

Chong¹,

Puah²,

Mansor³

2018

IBR

View full text Add to dashboard Cite

Crude oil, as the most traded commodity in the world, exhibits prices with a clear influence on other commodities in the worldwide market. It also poses implications regarding the economic growth of oil-exporting and oil-importing nations. This study provides an unprecedented method of employing the indicator approach as proposed by the Conference Board, National Bureau of Economic Research, to construct a leading indicator for the global crude oil price. The results reveal that the constructed oil price indicator can predict the cyclical movement of the oil price by moving in advance of 3.5 months on average. This finding could provide better signaling to oil-related nations as well as other commodities that consider crude oil to be a leader in the market.

show abstract

“…ACF becomes smaller after lag 3, so the moving average process is probably 2 or 3. In this case, we consider 4 forms for this model: ARIMA(2,2,2), ARIMA(2,2,3), ARIMA(3,2,2), ARIMA (3,2,3).…”

Section: B Model Building and Parameter Estimationmentioning

confidence: 99%

“…Short-term wind speed forecasting was studied in [2]. The oil market was also object of the study in [3]. There are many approaches for time series forecasting, Kalman filter, Regression analysis, neural network.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Environment Changes with the ARMA Model for Application to an Oil Tank Fire Hazard

Yuanzhi¹,

Zhang²,

Liu³

et al. 2015

Advances in Computer Science Research

View full text Add to dashboard Cite

Abstract-In oil tank farm, the fire hazard causes a great loss to both people and property. Once there is a fire, to avoid more loss, a reliable, secure and practical method for putting out a fire is needed. And under this circumstance, the accurate forecasting of the changes of the environment, such as temperature or humidity, is of great value. We employed time series analysis method to forecast the temperature and humidity changes when there was a fire accident in the oil tank farm. According to the record, we modelled a suitable ARMA model. And the results show that ARMA is an effective method to predict time varying series in the oil tank farm. Our results in this paper also provide an important way of studying the fire spreading in the oil tank farm.

show abstract

Modeling and forecasting crude oil markets using ARCH-type models

Cited by 187 publications

References 54 publications

Volatility Modeling and Forecasting of Istanbul Gold Exchange (IGE)

Volatility Modeling and Forecasting of Istanbul Gold Exchange (IGE)

Constructing a Composite Leading Indicator for the Global Crude Oil Price

Forecasting the Environment Changes with the ARMA Model for Application to an Oil Tank Fire Hazard

Contact Info

Product

Resources

About