[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
اصول اخلاق نشریه::
اصول اخلاق نشریه::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 12, Issue 50 (Autumn 1395 2016) ::
QEER 2016, 12(50): 1-24 Back to browse issues page
The Impact of Different Data Frequency on Prediction Powers of Various Short- and Long Memory Models: an application to Oil Market Volatility
Ali Kiani * 1, Karim Eslamloueyan
1- , alikiani2000@gmail.com
Abstract:   (4966 Views)

Many researchers have used different methods to forecast the volatility of goods and capital markets. However, only few have taken into account the importance of data frequency on their predictions. However, none of them have considered the possibility of long run memory in predicting the volatility of oil market. In order to fill this gap in the literature, we have estimated a class of ARFIMA and GARCH models (long- and short-run memory models) with different data frequency to predict oil market volatility. Based on Root Mean Square Error (RMSE) criterion, irrespective of models' type, all models with high frequency data outperform the low frequency data models. The result also shows that at each frequency level, the prediction powers of both ARFIMA and GARCH models are the same. To sum up, we suggest the use of short-run memory model with high frequency data to forecast volatility of oil market. Hence, it seems a proper GARCH model can do the job and there is no need to use ARFIMA model for this purpose.

Keywords: Data frequency, long vs short memory models, GARCH, ARFIMA, Prediction of Oil Price Volatility
Full-Text [PDF 566 kb]   (2046 Downloads)    
Type of Study: Research | Subject: Energy Economic
Received: 2014/10/24 | Accepted: 2016/05/16 | Published: 2017/02/27 | ePublished: 2017/02/27
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

kiani A, Eslamloueyan K. The Impact of Different Data Frequency on Prediction Powers of Various Short- and Long Memory Models: an application to Oil Market Volatility. QEER 2016; 12 (50) :1-24
URL: http://iiesj.ir/article-1-426-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 12, Issue 50 (Autumn 1395 2016) Back to browse issues page
فصلنامه مطالعات اقتصاد انرژی Quarterly Energy Economics Review
Persian site map - English site map - Created in 0.07 seconds with 37 queries by YEKTAWEB 4645