Forecasting Method for Optimal Diversification

  • Jayeola Dare Department of Mathematical Sciences, Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria
  • Aye O. Patrick Department of Mathematical Sciences, Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria
  • Akpodamure Oghenefejiro Federal Polytechnic Orogun, Delta State, Nigeria
  • Thomas O. Mary Department of Mathematical Sciences, Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria
Keywords: GARCH, EGARCH, Black-Litterman model, expected returns, risk


Forecasting is a technique that uses historical data as inputs to make estimates that are predictive in determining the direction of future trends. The goal of investors is to make optimal choice that leads to minimization of risk and maximization of returns, but the method that leads to these objectives has been a challenge for investor. In this study, Black-Litterman model (BLM) is adopted and two forecasting methods; EGARCH and GARCH methods are used for two parameters of BLM; investor views and level of uncertainty. The aim of this paper is to investigate the best forecasting method to estimate BLM that would lead to minimum risk and maximum returns. The analysis of this paper shows that EGARCH method gives maximum expected returns and minimum risk.


Candelon, B., Fuerst, F., & Hasse, J. (2021). Diversification potential in real estate portfolio. Journal of International Economics, 166, 126-139.

Fernandes, B., Street, A., Fernandes, C., & Valladao, D. (2018). On an adaptive Black- Litterman investment strategy using conditional fundamentalist information: A Brazilian case study. Finance Research Letters, 27, 201-207.

Jayeola, D., & Ismail, Z. (2018). Impacts of riskless assets on diversification. Advance Science Letters, 24(6), 4286-4289.

Davis, M. H., & Lleo, S. (2016). A simple procedure for combining expert opinion with statistical estimates to achieve superior portfolio performance. The Journal of Portfolio Management, 42(4), 49-58.

Platanakis, E., & Urquhart, A. (2019). Portfolio management with cryptocurrencies: The role of estimating risk. Economics Letters, 177(1), 76-80.

Oikonomou, I., Platanakis, E., & Sutaliffe, C. (2018). Socially responsible investment portfolio: Does the optimization process matter? The British Accounting Review, 50(4), 139-401.

Ince, H., & Trafalis, T. B. (2017). A hybrid forecasting model for stock market prediction. Economic Computation and Economic Cybernetics Studies and Research, 21, 263-280.

Bayram, K., Abdullah, A., & Meera, A. K. (2018). Identifying the optimal level of gold as a reserve asset using Black-Litterman model: The case for Malaysia, Turkey, KSA and Pakistan. International Journal of Islamic and Middle Eastern Finance and Management, 11(3), 334-356.

Kara, M., Ulucan, A., & Atici, K. B. (2019). A hybrid approach for generating investor views in Black-Litterman model. Expert Systems with Applications, 128, 256-270.

Harris, R. D., Stoja, E., & Tan, L. (2017). The dynamic Black-Litterman approach to asset allocation. European Journal of Operational Research, 259(3), 1085-1096.

Takapoui, R., Moehle, N., Boyd, S., & Bemporad, A. (2017). A simple effective heuristic for embedded mixed-integer quadratic programming. International Journal of Control, 79(13), 1-11.

Yanagihara, H., Kamo, K., Imori, S., & Yamamura, M. (2020). A study on the bias-correction effect of the AIC for selecting variables in normal multivariate linear regression models under model misspecification. REVSTAT-Statistical Journal, 15(3), 299-332.

Jayeola, D., Aye, O. P., & Oyewola, D. O. (2022). Comparison of stationarity on Ljung-Box test statistics for forecasting. Earthline Journal of Mathematical Sciences, 8(2), 325-336.

How to Cite
Dare, J., Patrick, A. O., Oghenefejiro, A., & Mary, T. O. (2024). Forecasting Method for Optimal Diversification. Earthline Journal of Mathematical Sciences, 14(2), 283-291.