Evaluating the Performance of Forecasting Models for Portfolio Allocation Purposes with Generalized GRACH Method
محورهای موضوعی : Financial MathematicsAdel Azar 1 , Mohsen Hamidian 2 , Maryam Saberi 3 , Mohammad Norozi 4
1 - Faculty of Management & Economics , University of Tarbiat Modares , Tehran, Iran
2 - Faculty of Economics & Accounting , University of Islamic Azad south Tehran, Tehran, Iran
3 - Faculty of Management & Economics , University of Tarbiat Modares , Tehran, Iran
4 - Faculty of Economics & Accounting , University of Islamic Azad south Tehran, Tehran, Iran
کلید واژه: Loss functions, Portfolio allocation, Evaluating forecasts,
چکیده مقاله :
Portfolio theory assumes that investors accept risk. This means thatin the equal rate of return on the two assets, the assets were chosenthat have a lower risk level. Modern portfolio theory is accepted byinvestors who believe that they are not cope with the market. Sothey keep many different types of securities in order to access theoptimum efficiency rate that is close to the rate of return on market.One way to control investment risk is establishing the portfolioshares. There are many ways to choose the optimal portfolioshares. Among these methods in this study we use loss functions.For this, we choose all firms from the year2011to the end of 2015that had been a member in the Tehran Stock Exchange. The resultsof this research show that the likelihood functions have the bestperformance in Forecasting the optimal portfolio allocationprob-lem.
[1]. Andersen, T.G., Bollerslev, T., Encyclopedia of Statistical Sciences, John Wiley & Sons, Ltd, 1998.
[2]. Andersen, T. G., Bollerslev, T., Christoffersen, P., & Diebold, F. X., Practical volatility and correlation modeling for financial market risk management The risks of financial institutions, University of Chicago Press, 2007.
[3]. Alexander, C. and Chibumba, A., Multivariate orthogonal factor GARCH, Working Paper, Mathematics Department, University of Sussex, 1996.
[4]. Becker, R., Clements, A.E., Doolan, M.B., Hurn, A.S., Selecting volatility forecasting models for portfolio allocation purposes, International Journal of Forecasting, 2015, 31, (3), P. 849-861.
[5]. Clark, T. and McCracken, M., Tests of equal forecast accuracy and encompassing for nested models, Journal of Econometrics, 2001, 105, (1), P. 85-110.
[6]. Danielsson, J., Financial Risk Forecasting, Wiley 2011.
[7]. Diebold, F. and Mariano, R., Comparing Predictive Accuracy, Journal of Business & Economic Statistics, 1995, 13, (3), P. 253-63.
[8]. Engle, R, and Colacito, R., Testing and Valuing Dynamic Correlations for Asset Allocation, Journal of Business & Economic Statistics, 2006, 24, P. 238-253.
[9]. Elliott, G. and Timmermann, A., Economic Forecasting, Princeton University Press, 2008.
[10]. Fleming, J., (2001), The Economic Value of Volatility Timing, Journal of Finance, 2001, 56, (1), P. 329-352.
[11]. Fleming, J., Kirby, C. and Ostdiek, B., The economic value of volatility timing using "realized" volatility, Journal of Financial Economics, 2003, 67, (3), P. 473-509.
[12]. Hansen, P. R., Lunde, A., & Nason, J. M., The model confidence set. Econometrica, 2011, 79, (2), P. 453-497.
[13]. Hansen, PR., Lunde, A., Realized variance and market microstructure noise, Journal of Business and Economic Statistics, 2006, 24 (2), P. 127-161.
[14]. Laurent SFJA, Rombouts JVK, Violante F., On loss functions and ranking forecasting performances of multivariate volatility models. Journal of Econometrics. 2013, 173 (1), P. 1-10.
[15]. Patton, AJ, Sheppard, K., Evaluating volatility and correlation forecasts, Handbook of financial time series, 2009.
[16]. Patton, AJ, Volatility forecast comparison using imperfect volatility proxies, Journal of Econometrics, 2011, 160, P. 246–256.
