FORECASTING VOLATILITY IN OIL PRICES WITH ARCH/GARCH MODELS AND ARTIFICIAL NEURAL NETWORK ALGORTIHMS

Year 2017, ICMEB17 Özel Sayısı, 588 - 597, 01.12.2017

Salih Çam Esra Ballı Çiler Sigeze

Abstract

In this study, we analyze volatility in crude oil prices using ARCH-GARCH models and Artificial Neural Network ANN over the time periods from January 02, 2008 to October 23, 2017. To investigate the relationship, financial variables included in the model such as the DJIA and FTSE stock market indexes, EUR/USD and Yen/USD exchange rates. According to the artificial neural network results, the most important effect on oil price comes from volatility of DJIA and FTSE stock market indexes. Artificial Neural network evidence shows that the R-square coefficient is 87% for the sample period

Keywords

volatility, oil prices, artificial neural networks, GARCH models

PETROL FİYATLARINDAKİ OYNAKLIĞIN ARCH/GARCH MODELLERİ VE YAPAY SİNİR AĞLARI ALGORİTMASI İLE TAHMİNİ

Abstract

Bu çalışmada Otoregresif Koşullu Değişen Varyans ARCH modeli, Genelleştirilmiş Otoregresif Koşullu Değişen Varyans GARCH modeli ve yapay sinir ağları algoritması kullanılarak petrol fiyatlarındaki oynaklık 2 Ocak 2008 ve 23 Ekim 2017 dönemi esas alınarak tahmin edilmiştir. Modelde finansal değişkenler olarak Dow Jones endeksi, FTSE endeksi, EUR/USD, Yen/USD döviz kurları kullanılmıştır. Yapay sinir algoritması ile petrol fiyatları getiri serisinin oynaklık değerleri tahmin edilmesinin yanı sıra hangi değişkenin bu oynaklık değerleri üzerinde en çok etkiye sahip olduğu önem analizi yardımıyla belirlenmiştir. Tahmin edilen yapay sinir ağları sonuçlarına göre R2 değeri %87 bulunurken, petrol fiyatına en fazla etki eden değişkenler Dow Jones ve FTSE endeksleri olmuştur.

Keywords

oynaklık, petrol fiyatları, yapay sinir ağları, GARCH modelleri

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