Market Risk Analysis with Value at Risk Models using Machine Learning in BIST-30 Banking Index

Yıl 2024, Cilt: 14 Sayı: 1, 63 - 89, 30.06.2024

Yavuz Demirdöğen

https://doi.org/10.31679/adamakademi.1387201

Öz

Market risk is one of the most critical risks for banks and portfolio managers. According to Basel criteria, Value at Risk (VaR) calculations should be conducted at regular intervals. VaR calculations can be performed using various methods, and the approaches and variables added to the model can vary significantly. Developments in machine learning and deep learning methods have increased the diversity of VaR calculations, enabling the construction of more accurate and complex models.
In this study, a portfolio was created using the stocks of 4 major banks in BIST30 (AKBNK, GARAN, ISCTR, YKBNK) with the help of Monte Carlo simulation and Random Forest. Calculations were made for 126 periods with a 10-day interval using 5 years of daily data. Predictions were made for the last 4 periods using 3 different Value at Risk (VaR) methods (historical, parametric, and Monte Carlo). Independent variables such as VIX (fear index), USD/TL, Gold/TL, and Brent/TL were used. The suitability of the variables was tested with machine learning regularization methods, including Ridge, Lasso, and Elastic Net regression models. Random Forest was again used to measure the impact of independent variables on stocks' weights. For each VaR model, stock weight distributions were determined for the last 4 periods, and the realized VaR results were compared.
As a result of the findings, the parametric VaR method provided the best result for the first period, while the historical VaR method provided the closest result for the other three periods. When comparing the findings with the actual results, it was observed that the findings were more optimistic, and even the closest results did not come within 30% of the actual value. The reason for the difference being greater than expected could be attributed to the fact that the value of bank stocks has been below their value in the last two years and the sharp movements in the stock market in the selected last 4 periods, independent of individual stocks.

Anahtar Kelimeler

Market Risk, Value at Risk, Machine Learning, BIST-30, Banking Index

Etik Beyan

The study does not require an ethical statement.

Teşekkür

I would like to thank our graduate student Mehmet ÇELİK for his contributions.

BIST-30 Bankacılık Endeksi'nde Makine Öğrenmesi Kullanılarak Riske Maruz Değer Modelleri ile Piyasa Riski Analizi

Öz

Market riski bankaların ve portföy yöneticilerinin en dikkat ettiği risklerden birisidir. Basel kriterleri gereği Riske Maruz Değer (RMD) hesaplaması sık aralıklarla yapılmalıdır. RMD hesabı birkaç farklı yöntemle yapılabilirken yaklaşımlar ve modele eklenen değişkenler çok farklı olabilir. Makine öğrenmesi ve derin öğrenme yöntemlerindeki gelişmeler RMD hesaplamalarındaki çeşitliliği artırarak daha hassas ve karmaşık modellerin kurgulanmasına yardımcı olmuştur.
Bu çalışmada BIST30’da yer alan 4 büyük bankanın (AKBNK, GARAN, ISCTR, YKBNK) hisselerinden Monte Carlo simülasyonu ve Random Forest yardımıyla portföy oluşturulmuştur. 5 yıllık ve günlük verilerle 10-ar günlük 126 periyot hesaplanmıştır. Son 4 periyot için 3 farklı RMD yöntemiyle (tarihi, parametrik ve Monte Carlo) tahminlemeler yapılmıştır. Değişken olarak VIX (korku endeksi), USD/TL, Gold/TL ve Brent/TL alınmıştır. Değişkenlerin uygunluğu makine öğrenmesi düzenlileştirme metotları olan Ridge, Lasso ve Elastic Net regresyon modelleriyle test edilmiştir. Bağımsız değişkenlerin hisselere etkisini ağırlıklandırılmasını ölçmek amacıyla yine Random Forest kullanılmıştır. Her bir RMD modeli için son 4 periyotta hisse ağırlık dağılımı çıkarılarak tekrar RMD sonuçları karşılaştırılmıştır.
Bulgular sonucunda ilk periyot için parametrik RMD en iyi sonucu verirken diğer üç periyot için tarihi RMD en yakın sonucu vermiştir. Bulgular ile gerçekleşen sonuçlar kıyaslandığında bulguların daha iyimser olduğu, gerçekleşen değere en yakın sonuçların dahi %30’dan daha az oranda yaklaşmadığı gözlemlenmiştir. Farkın beklenenden fazla olmasının sebebi olarak banka hisselerinin son iki yılda değerinin altında olması ve seçilen son 4 dönemdeki borsa hareketlerinin -hisse özelinden bağımsız olarak- sert olması gösterilebilir.

Anahtar Kelimeler

Piyasa Riski, Riske Maruz Değer, Makine Öğrenmesi, Bist-30, Bankacılık Endeksi

Kaynakça

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