Author :
Abstract
In this study, the best method was tried to be found in order to apply model on volatility of crude oil via using daily price of crude oil between 2015 and 2016 years. This study consists of crude oil price since crude oil is the very volatile commodity. Findings about study is that there is an arch effect on crude oil prices and the best model for modelling is GARCH ( 1,1). After determining the model, ARCH LM test was applied for GARCH (1,1) and results indicate that there is no arch effect among error terms. Furthermore, when crude oil prices are controlled graphically, crude oil has sharp volatility since Rusia, Ukrania, Greece, Iran and Iraq are seem as geopolitical risk. A deal which OPEC members and decision which play an essential role on crude oil also constitute market price for crude oil. Especially, commodity such as crude oil has trend in direction of FED and China. Any news or progress about macroeconomic variables or decision lead to volatility. For this reason, crude oil price has fluctuation by climbing peak and decreasing the deepest point from begining of 2015 to end of 2016 years.
Keywords
Abstract
In this study, the best method was tried to be found in order to apply model on volatility of crude oil via using daily price of crude oil between 2015 and 2016 years. This study consists of crude oil price since crude oil is the very volatile commodity. Findings about study is that there is an arch effect on crude oil prices and the best model for modelling is GARCH ( 1,1). After determining the model, ARCH LM test was applied for GARCH (1,1) and results indicate that there is no arch effect among error terms. Furthermore, when crude oil prices are controlled graphically, crude oil has sharp volatility since Rusia, Ukrania, Greece, Iran and Iraq are seem as geopolitical risk. A deal which OPEC members and decision which play an essential role on crude oil also constitute market price for crude oil. Especially, commodity such as crude oil has trend in direction of FED and China. Any news or progress about macroeconomic variables or decision lead to volatility. For this reason, crude oil price has fluctuation by climbing peak and decreasing the deepest point from begining of 2015 to end of 2016 years.
Keywords
- Ahmad, M. Hura ve Ping P. Yean. 2014. Modelling Malaysian Gold Using Symmetric and
- Ahmad, M. Hura ve Ping P. Yean. 2014. Modelling Malaysian Gold Using Symmetric andAsymmetric GARCH Models. Department of Mathematical Sciences, Faculty of Science UniversitiTeknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia, Applied Mathematical Sciences. Vol.7,No.17, 2879 - 2884. Internet Adress:file:///C:/Users/EAKDMK247/Downloads/Forecasting%20malaysian%20gold%20using%20GARC H%20model.pdf
- Akar, C. (2007). Volatilite Modellerinin Öngörü Performansları: ARCH, GARCH ve SWARCH Karşılaştırması, İşletme Fakültesi Dergisi, Cilt 8, Sayı 2, ss.201- 217.
- Balaban, E. (2004). Comparative Forecasting Performance of Symmetric and Asymmetric Conditional Volatility Models of an Exchange Rate. Economics Letters, 83, 99-105.
- Du, Y. 2012. Modelling and Forecasting Volatility of Gold Price with Other Precious Metals Prices by Univeriate GARCH Models. Uppsala Universitet, Department of Statistics Master’s Thesis, June,
- Er, Ş. ve Fidan, N. (2013). Modelıng Istanbul Stock Exchange-100 Daıly Stock Returns: ANonparametrıc Garch Approach, Journal of Business, Economics & Finance (2013), Vol.2 (1), 36-Mittal, A. K., D. D. Arora, and N. Goyal. 2012. ‘Modelling the Volatility of Indian Stock Market.’ gitam Journal of Management 10 (1): 224–43.
- Pan, Hongyu and Zhıchao ZHANG; (2006), “ Forecasting Financial Volatility: Evidence From Chinese Stock Market”, Working Paper In Economics and Finance, No:06/02, pp. 1-29.
- Peters, Jean-Philippe ; (2001),” Estimating And Forecasting Volatility of Stock Indices UsingAsymmetric GARCH Models And (Skewed) Student-t Densities” Internet Address:http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=AEE3119AF4DCDA0F66DFED86D9AA 6874?doi=10.1.1.465.87&rep=rep1&type=pdf, Date of Access: 29.06.2017
- Siourounis, Gregorios D.; (2002), “Modelling Volatility and Testing for Efficiency in EmergingCapital Markets: The Case of The Athens Stock Exchange”, Applied Financial Economics,12, pp.47-55.
- West, K. D. and Cho, D. (1995). The Predictive Ability of Several Models of Exchange Rate Volatility. Journal of Econometrics, 69, 367-391.
