Modeling Portfolio Based on Linear Programming for Bank Business Development Project Plan
Keywords:
productive assets, banking book, constraintAbstract
The bank’s business processes target business plans for the next year. Existing conditions, the business plan is based on the growth asset portfolio every year, so that the purchase of productive assets awaits issuers’ offers. This condition will cause a portfolio not to be measured and the inaccuracy of portfolio selection. Asset Liability Management (ALM) is the management of the structure of assets and liabilities to achieve profit. Banking books and trading books are bank portfolios to earn income. In selecting each portfolio, it contains liquidity risk, market risk and, credit risk. The level of profit is reflected in returns, while returns and risks are a trade-off so that calculations require mathematical and simulation models. Each bank needs an overview of the composition of productive assets, as short-term, medium-term and, long-term assets must be measured risk and target achievement. Linear programming method will allocate productive assets as the bank’s leading source of income, to achieve optimization of profit on the risks received. The problem with this research is that there are 830 variables as banking assets and 19 constraints as indicators of risk. In the seventh iteration of mathematical models, return 1,803 Trillyun from 11 banking book assets.
References
J. Pan and Q. Xiao, “Optimal asset–liability management with liquidity constraints and stochastic interest rates in the expected utility framework,” Journal of Computational and Applied Mathematics, vol. 317, pp. 371–387, 2017.
O. Babat, J. Vera, and L. Zuluaga, “Computing near-optimal value-at-risk portfolios using integer programming techniques,” European Journal of Operational Research, vol. 266, no. 1, pp. 304–315, 2018.
B. Indonesia, “Jakarta: Otoritas jasa keuangan,” 2018.
S. Riyadi, “Banking assets and liability management,” 2003.
T. Bellini, “Integrated bank risk modeling: A bottom-up statistical framework,” European Journal of Operational Research, vol. 230, no. 2, pp. 385–398, 2013.
L. Onyiriuba, Bank risk management in developing economies: Addressing the unique challenges of domestic banks. Academic Press, 2016.
P. Jensen and J. Bard, Operations research models and methods. John Wiley & Sons, 2002.
F. Hillier and G. Lieberman, ISE Introduction to Operations Research. McGraw-Hill Interamericana de Espana S.L., 2021.
