The Impact of the Investment Horizon in Optimizing Portfolio using Wavelet and GARCH-COPULA

Document Type : applied


1 Assistant Prof., Financial Engineering,Tarbiat Modares University, Tehran, Iran

2 MSc. Student in financial engineering, Faculty of Management, University of Khatam, Tehran, Iran


The purpose of our paper is to show how investors can used the multi-scale nature of assets into their portfolio decisions. We decompose weekly return series of 3stocks listed in the Tehran stock exchange Index (TSE) from 2011 to 2014 into different time scales to separate short-term noise from long-run trends. We decompose data by applying wavelet Transform techniques. Then, we apply ARIMA(p,d.q)_GARCH(1,1)_Copula to determine return and Value at Risk (VaR). The process first extracts the filtered residuals and variance from each return series with an ARIMA and asymmetric GARCH model, then constructs the sample marginal cumulative distribution function (CDF) of each asset using a Gaussian kernel estimate for the interior and a generalized Pareto distribution (GPD) estimate for the upper and lower tails. A Student's t copula is then fit to the data and used to induce correlation between the simulated residuals of each asset. Finally, the simulation assesses the Value-at-Risk (VaR) of the equity portfolio over a different horizon. We to get the best weight of each stock in the portfolio. We have identified the best VAR based ratio.In this study, we had predicted a portfolio for each time horizon according to risk and return portfolio. Our results provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective.
JEL: G32, G11
How to cite this paper: Rastegar, M. A., & Okeinezhad, M. (2017). The Impact of the Investment Horizon in Optimizing Portfolio using Wavelet and GARCH-COPULA. Journal of Risk Modeling and Financial Engineering, 2(3), 340–361. (In Persian)


Main Subjects

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