İki Dinamik Koşullu Korelasyon (DCC) Modeli Arasındaki Tahmin Doğruluğunun Karşılaştırılması

Year 2024, Volume: 9 Issue: 23, 1 - 11, 29.02.2024

Metin İlbasmış

https://doi.org/10.25204/iktisad.1388428

Abstract

Bu çalışma, genel bir minimum varyans portföyü içinde ABD hisse senedi ve GYO (Gayrimenkul Yatırım Ortaklığı) piyasaları arasındaki bağlantıyı tahmin etmek için yaygın olarak kullanılan iki Dinamik Koşullu Korelasyon (DCC) modelini karşılaştırmaktadır. Hisse senedi ve GYO portföyleri, varyans-kovaryans matrisleri ile örneklem dışı olarak oluşturulmuş, ex-ante tahminle ileriye dönük kovaryans bilgisini temsil etmektedir. Çalışmanın amacı, her iki modelin tahmin hassasiyetini değerlendirip hangisinin daha düşük tahmin hataları ürettiğini ve ekonomik olarak daha iyi performans gösterdiğini belirlemektir. İstatistiksel karşılaştırmaya göre Asimetrik DCC modele dayanan ex-ante korelasyon tahminleri, standart DCC modele göre daha küçük hatalar üretmektedir. Bu iki modelin ekonomik performansı, dinamik bir portföy tahsisi çerçevesinde ampirik olarak karşılaştırıldığında, Asimetrik DCC modelinin standart DCC modeline benzer ekonomik performans özellikleri sergilediği ortaya konulmuştur. Asimetrik DCC modelinin ekonomik performansına rağmen portföylerini haftalık olarak yeniden kalibre eden yatırımcılar, daha az tahmin hatasından ve daha verimli portföyler oluşturma becerisinden faydalanabilirler.

Keywords

Tahmin Hatası, Asimetrik DCC-GARCH, Dinamik Korelasyon

A Comparison of Forecasting Accuracy between Two Dynamic Conditional Correlation (DCC) Models

Abstract

This study compares two commonly used DCC-family models to predict the linkage between the US equity and REIT markets within a global minimum-variance portfolio. Equity and REIT portfolios are constructed using variance-covariance matrices, which represent forward-looking covariance information. These matrices are constructed out-of-sample with ex-ante forecasting. By assessing the predictive precision of each model, the study aims to determine which one produces the lowest forecasting errors and performs better economically. According to a statistical comparison, ex-ante correlation forecasts based on the Asymmetric DCC model were more accurate than those based on the standard DCC model. An empirical comparison of the economic performance of these two models in a dynamic portfolio allocation framework reveals that, despite its complexity, the Asymmetric DCC model exhibits similar economic performance characteristics to the standard DCC model. Despite the lack of emphasis on the economic overperformance of the Asymmetric DCC model, investors who recalibrate their portfolios weekly will benefit from reduced forecast errors and the ability to create more efficient portfolios by using an asymmetric model instead of a standard model.

Keywords

Forecasting Error, Asymmetric DCC-GARCH, Dynamic Correlation

References

• Case, B., Yang, Y., and Yildirim, Y. (2012). Dynamic correlations among asset classes: REIT and stock returns. The Journal of Real Estate Finance and Economics, 44(3), 298-318. https://doi.org/10.1007/s11146-010-9239-2
• Diebold, F. X., and Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3), 253-263. https://www.jstor.org/stable/1392155
• Elton, E. J., and Gruber, M. J. (1973). Estimating the dependence structure of share prices--implications for portfolio selection. The Journal of Finance, 28(5), 1203-1232. https://doi.org/10.2307/2978758
• Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. https://doi.org/10.1198/073500102288618487
• Engle, R., and Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. National Bureau of Economic Research. https://doi.org/10.3386/w8554
• Granger, C. W. J., and Newbold, P. (1977). Identification of Two-way Causal Systems. Frontiers in Quantitative Economics Vol. IIIA (Intriligator, HD, ed.) Amsterdam: North-Holland.
• Hansen, P. R., and Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of Applied Econometrics, 20(7), 873-889. https://doi.org/10.1002/jae.800
• Harvey, D., Leybourne, S., and Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281-291. https://doi.org/10.1016/S0169-2070(96)00719-4
• Huang, J. Z., and Zhong, Z. (2013). Time variation in diversification benefits of commodity, REITs, and TIPS. The Journal of Real Estate Finance and Economics, 46(1), 152-192. https://doi.org/10.1007/s11146-011-9311-6
• Jensen, M. C. (1968). The performance of mutual funds in the period 1945-1964. The Journal of finance, 23(2), 389-416. https://doi.org/10.2307/2325404
• Kalotychou, E., Staikouras, S. K., and Zhao, G. (2014). The role of correlation dynamics in sector allocation. Journal of Banking and Finance, 48, 1-12. https://doi.org/10.1016/j.jbankfin.2014.06.025
• Meese, R., and Rogoff, K. (1988). Was it real? The exchange rate interest rate relation, 1973-1984. Journal of Finance, 43(4), 933-948. https://doi.org/10.2307/2328144
• Orskaug, E. (2009). Multivariate DCC-GARCH model:-with various error distributions (Master's thesis). Institutt for Matematiske Fag, Norwegian University of Science and Technology. http://hdl.handle.net/11250/259296
• Peng, L., and Schulz, R. (2013). Does the diversification potential of securitized real estate vary over time and should investors care?. The Journal of Real Estate Finance and Economics, 47(2), 310-340. https://doi.org/10.1007/s11146-011-9357-5
• Schnaars, S. P. (1986). A comparison of extrapolation models on yearly sales forecasts. International Journal of Forecasting, 2(1), 71-85. https://doi.org/10.1016/0169-2070(86)90031-2
• Sharpe, W. F. (1966). Mutual fund performance. The Journal of Business, 39(1), 119-138. https://www.jstor.org/stable/2351741
• Treynor, J. L. (1965). How to rate management of investment funds. Harvard Business Review, 43(1), 63-75.
• West, K. D. (1996). Asymptotic inference about predictive ability. Econometrica: Journal of the Econometric Society, 64(5),1067-1084. https://doi.org/10.2307/2171956
• White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097-1126. https://doi.org/10.1111/1468-0262.00152