Hisse Senedine Dayalı Vadeli İşlem Sözleşmelerinin Fiyat Hareketlerinin Zaman Serileri ile Modellenmesi: Tüpraş Örneği

Abstract

Bu çalışma, hisse senedine dayalı vadeli işlem sözleşmelerinin fiyat hareketlerini modellemek amacıyla gelişmiş zaman serisi analiz tekniklerini kullanmakta ve Tüpraş vadeli işlemlerini örnek olay olarak ele almaktadır. Finansal piyasaların karmaşıklığını tam olarak yansıtamayan geleneksel ARIMA modellerinin sınırlılıkları dikkate alınarak, bu araştırmada hisse senedi fiyatları, piyasa endeksleri, döviz kurları, faiz oranları ve enflasyon gibi temel dışsal değişkenleri içeren genişletilmiş bir ARIMAX çerçevesi geliştirilmiştir. Çalışmada Ocak 2017 ile Ağustos 2023 arasındaki aylık veriler kullanılmıştır. Sağlam ve güvenilir tahminler elde edebilmek için veri ön işleme, keşifsel analiz, durağanlık testi adımları uygulanmıştır. Ampirik sonuçlar, RMSE, MSE, R², AIC ve BIC gibi doğruluk ölçütleri açısından değerlendirildiğinde, ARIMAX modelinin hem temel hem de optimize edilmiş ARIMA modellerinden belirgin şekilde daha iyi performans gösterdiğini ortaya koymaktadır. Bu araştırma, dışsal değişkenlere dayalı modellerin enerji türevleri gibi volatil ve yapısal olarak karmaşık piyasalarda sunduğu açıklayıcılığı ortaya koyarak finansal ekonometri alanına katkı sağlamaktadır.

Keywords

• ARIMAX Modeli
• Vadeli İşlem Sözleşmeleri Tahmini
• Zaman Serisi Analizi
• Dışsal Değişkenler
• Ekonomik Göstergeler

Modelling Price Movements of Stock-Based Futures Contracts with Time Series: Tupraş Example

Abstract

This study investigates the modelling of price movements in stock-based futures contracts by applying advanced time series techniques, using the Tupraş futures as a case study. Recognizing the limitations of traditional ARIMA models in capturing the complexities of financial markets, the research develops an extended ARIMAX framework that incorporates key exogenous variables such as stock prices, market indices, exchange rates, interest rates, and inflation. The dataset spans from January 2017 to August 2023 with monthly observations. Data preprocessing, exploratory analysis, and stationarity test steps were applied to obtain robust and reliable estimates. Empirical results reveal that the ARIMAX model significantly outperforms the baseline and optimized ARIMA models, as indicated by improved accuracy metrics, including RMSE, MSE, R², AIC, and BIC. This research contributes to financial econometrics by demonstrating the explanatory power of exogenous-driven models in volatile and structurally complex markets such as energy derivatives.

Keywords

• ARIMAX Model
• Futures Contracts Forecasting
• Time Series Analysis
• Exogenous Variables
• Economic Indicators

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