Mean-Variance Portfolio Optimization under Asset-Liability based on Time Series Approaches

Sukono; Eman Lesman; Herlina Napitupulu; Yuyun Hidayat

doi:https://doi.org/10.14445/22315373/IJMTT-V49P519

International Journal of Mathematics Trends and Technology

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Volume 49 | Number 2 | Year 2017 | Article Id. IJMTT-V49P519 | DOI : https://doi.org/10.14445/22315373/IJMTT-V49P519

Mean-Variance Portfolio Optimization under Asset-Liability based on Time Series Approaches

Sukono, Eman Lesman, Herlina Napitupulu, Yuyun Hidayat

Abstract

In this paper, we analyze about Optimization of Mean-Variance portfolio under assets-liability based on time series approach. It is assumed that the asset return follows the time series pattern, where the asset return has non-constants of mean and volatility. Non-constant mean is estimated using the model of autoregressive moving average (ARMA), and non-constant volatility is estimated using the generally autoregressive conditional heteroscedastic (GARCH) model. While the mean and variance of return liabilities are estimated using the return of bonds. The surplus return is estimated using the asset liability model. The predictive value of the mean and volatility model is non-constant, then used to determine the mean and variance of surplus return following the asset liability models. Next, the value of the mean and variance of returns surplus is used for the investment portfolio optimization process. Portfolio optimization of the surplus return is done using the Mean-Variance model from Markowitz. As a numerical illustration is analyzed several assets traded on the capital market in Indonesia. Optimization of this portfolio produces a combination of optimum weight, which can be used as consideration for investors in making investment decisions on assets analyzed.

Keywords

Optimization, mean-variance, asset-liability, ARMA, GARCH.

References

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Citation :

Sukono, Eman Lesman, Herlina Napitupulu, Yuyun Hidayat, "Mean-Variance Portfolio Optimization under Asset-Liability based on Time Series Approaches," International Journal of Mathematics Trends and Technology (IJMTT), vol. 49, no. 2, pp. 146-151, 2017. Crossref, https://doi.org/10.14445/22315373/IJMTT-V49P519