Volatilite Tahmini: iskandinav Hisse Senedi Piyasalarından Bulgular

Year 2023, Volume: 13 Issue: 2, 1 - 12, 28.12.2023

Burak Korkusuz

Abstract

Bu çalışma, İskandinav borsaları için en etkin volatilite tahmin modelini belirlemeyi amaçlamaktadır. Bu bağlamda, HAR-(RV, RSV ve PS) modellerinin tahmin gücü, 2010-2019 yılları arasında 7 İskandinav borsa endeksi için yüksek frekanslı veriler kullanılarak ARFIMA-RV modeli ile karşılaştırılmıştır. Özyinelemeli pencere mekanizması kullanılarak bir gün sonra gerçekleşen örneklem dışı volatilite tahminleri üretilmektedir. Örneklem dışı tahmin kayıpları, MSE ve QLIKE kriterleri ile ölçülür. Sonuçlar birkaç önemli noktaya işaret etmektedir. İlk olarak, HAR-RV (PS ve RSV) modellerinin, ARFIMA-RV modeline göre daha iyi performans gösteren model grubu olduğu öne sürülmektedir. İkincisi, varyansın pozitif ve negatif yarı varyanslara veya diğer bir deyişle iyi ve kötü varyanslara ayrıştırılması, bazı durumlarda, gelecekteki varyansın tahminine yardım eden faydalı finansal bilgiler sunabilir. Son olarak, sonuçlar ve bulgular pazara, veri sıklığına, zaman ufkuna ve verilerin bazı karakteristik özelliklerine özgüdür ve bulguların yorumlanmasında bu faktörlerin önemi vurgulanmaktadır.

Keywords

Volatilite, Tahmin, HAR-RV ve ARFIMA-RV, Iskandinav hisse senedi piyasalari

Forecasting Realized Volatility: Evidence From Nordic Stock Markets

Abstract

This study aims to determine the most effective model for forecasting volatility within the Nordic stock markets. In this regard, the forecasting power of HAR-RV, RSV, and PS models is compared to the ARFIMA-RV model using high frequency data for 7 Nordic stock market indices spanning from 2010 to 2019. One-day-ahead out-of-sample realized volatility forecasts are produced using a recursive window mechanism. The out-of-sample forecast losses are measured by the MSE and QLIKE criteria. The results indicate several noteworthy points. Firstly, the HAR-RV (PS and RSV) models are suggested to be best performing realized volatility models over the ARFIMA-RV model. Secondly, the separation of realized variance into positive and negative realized semivariances, which is known as good and bad volatilities, might offer valuable financial insights in certain situations, aiding the prediction of future realized volatility. Lastly, the results and findings are specific to market, data frequency, time horizon, and some characteristics of data, emphasizing the importance of these factors in interpreting the findings.

Keywords

Volatility, Forecasting, HAR-RV and ARFIMA-RV, Nordic stock markets

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