Akaike, H. (1974). A new look at the statistical model identification. IEEE
Transactions on Automatic Control, 19(6), 716-723.
Alexander, C. (2009). Market risk analysis, value at risk models. (Vol. 4). John
Wiley & Sons.
Anagnostopoulos, K. P., & Mamanis, G. (2010). A portfolio optimization model
with three objectives and discrete variables. Computers & Operations
Research, 37(7), 1285-1297.
Banihashemi, S., & Navidi, S. (2017). Portfolio performance evaluation in MeanCVaR framework: A comparison with non-parametric methods value at risk
in Mean-VaR analysis. Operations Research Perspectives, 4, 21-28.
Böckenhauer, H. -J., Komm, D., Královič, R., & Rossmanith, P. (2012). On the
advice complexity of the knapsack problem. Latin American Symposium on
Theoretical Informatics, 61-72.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity.
Journal of Econometrics, 31(3), 307-327.
Bonami, P., & Lejeune, M. A. (2009). An exact solution approach for portfolio
optimization problems under stochastic and integer constraints. Operations
Research, 57(3), 650-670.
Castro, F., Gago, J., Hartillo, I., Puerto, J., & Ucha, J. M. (2011). An algebraic
approach to integer portfolio problems. European Journal of Operational
Research, 210(3), 647-659.
Christoffersen, P. (2011). Elements of financial risk management. Academic
Press.
Dana, A. -N. (2016). Modelling and estimation of volatility using ARCH/GARCH
models in Jordan’s stock market. Asian Journal of Finance & Accounting,
8(1).
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist nondominated sorting genetic algorithm for multi-objective optimization:
NSGA-II. International Conference on Parallel Problem Solving From
Nature, 849-858.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist
multiobjective genetic algorithm: NSGA-II. IEEE Transactions on
Evolutionary Computation, 6(2), 182-197.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for
autoregressive time series with a unit root. Journal of the American
Statistical Association, 74(366a), 427-431.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates
of the variance of United Kingdom inflation. Econometrica: Journal of the
Econometric Society, 987-1007.
Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional
variances. Econometric Reviews, 5(1), 1-50.
592 Vaezi et al., Iranian Journal of Economic Studies, 9(2) 2020, 569-594
Fonseca, C. M., & Fleming, P. J. (1995). An overview of evolutionary algorithms
in multiobjective optimization. Evolutionary Computation, 3(1), 1-16.
Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multiobjective
optimization: Formulation discussion and generalization. Icga, 93, 416-423.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between
the expected value and the volatility of the nominal excess return on stocks.
The Journal of Finance, 48(5), 1779-1801.
Gökbulut, R. I., & Pekkaya, M. (2014). Estimating and forecasting volatility of
financial markets using asymmetric GARCH models: An application on
Turkish financial markets. International Journal of Economics and Finance,
6(4), 23-35.
Guidolin, M., & Pedio, M. (2018). Essentials of time series for financial
applications. Academic Press.
Guo, X., Chan, R. H., Wong, W. -K., & Zhu, L. (2019). Mean–variance, mean–
VaR, and mean–CVaR models for portfolio selection with background risk.
Risk Management, 21(2), 73-98.
Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an
autoregression. Journal of the Royal Statistical Society. Series B
(Methodological), 190-195.
Holland, J. H. (1992). Adaptation in natural and artificial systems: An
introductory analysis with applications to biology, control, and artificial
intelligence. MIT press.
Huang, J. -J., Lee, K. -J., Liang, H., & Lin, W. -F. (2009). Estimating value at risk
of portfolio by conditional copula-GARCH method. Insurance: Mathematics
and Economics, 45(3), 315-324.
Jorion, P. (1997). Value at risk: The new benchmark for controlling market risk.
Irwin Professional Pub.
Kellerer, H., Pferschy, U., & Pisinger, D. (2004). The bounded knapsack problem.
In Knapsack Problems (pp. 185-209). Springer.
Li, H. -L., & Tsai, J. -F. (2008). A distributed computation algorithm for solving
portfolio problems with integer variables. European Journal of Operational
Research, 186(2), 882-891.
Linsmeier, T. J., & Pearson, N. D. (2000). Value at risk. Financial Analysts
Journal, 56(2), 47-67.
Lwin, K. T., Qu, R., & MacCarthy, B. L. (2017). Mean-VaR portfolio
optimization: A nonparametric approach. European Journal of Operational
Research, 260(2), 751-766.
Mamipour, S., & Vaezi Jezeie, F. (2015). Non-linear relationships among oil
price, gold price and stock market returns in Iran: A multivariate regimeswitching approach. Iranian Journal of Economic Studies, 4(1), 101-126.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of
investment. 344 p. John Wiley & Sons, New York, USA.
Vaezi et al., Iranian Journal of Economic Studies, 9(2) 2020, 569-594 593
Meghwani, S. S., & Thakur, M. (2018). Multi-objective heuristic algorithms for
practical portfolio optimization and rebalancing with transaction cost.
Applied Soft Computing, 67, 865-894.
Morelli, D. (2002). The relationship between conditional stock market volatility
and conditional macroeconomic volatility: Empirical evidence based on UK
data. International Review of Financial Analysis, 11(1), 101-110.
Morgan, J. P. (1996). JP Morgan/reuters riskmetrics–Technical document. JP
Morgan, New York. JP Morgan, New York.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new
approach. Econometrica: Journal of the Econometric Society, 347-370.
Pagan, A. R., & Schwert, G. W. (1990). Alternative models for conditional stock
volatility. Journal of Econometrics, 45(1-2), 267-290.
Pritsker, M. (2006). The hidden dangers of historical simulation. Journal of
Banking & Finance, 30(2), 561-582.
Ranković, V., Drenovak, M., Urosevic, B., & Jelic, R. (2016). Mean-univariate
GARCH VaR portfolio optimization: Actual portfolio approach. Computers
& Operations Research, 72, 83-92.
Rey Horn, J., Nafpliotis, N., & Goldberg, D. E. (1993). Multiobjective
optimization using the niched pareto genetic algorithm. IlliGAL Report,
93005, 61801-2296.
Sahni, S. (1975). Approximate algorithms for the 0/1 knapsack problem. Journal
of the ACM (JACM), 22(1), 115-124.
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of
Statistics, 6(2), 461-464.
So, M. K., & Philip, L. H. (2006). Empirical analysis of GARCH models in value
at risk estimation. Journal of International Financial Markets, Institutions
and Money, 16(2), 180-197.
Spears, W. M., & De Jong, K. A. (1991). An analysis of multi-point crossover. In
Foundations of genetic algorithms, (1), 301-315. Elsevier.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated
sorting in genetic algorithms. Evolutionary Computation, 2(3), 221-248.
Vaezi, F., Sadjadi, S. J., & Makui, A. (2019). A portfolio selection model based
on the knapsack problem under uncertainty. PloS One, 14(5), e0213652.
Vaezi, F., Sadjadi, S. J., & Makui, A. (2020). A robust knapsack based constrained
portfolio optimization. International Journal of Engineering, 33(5), 841-851.
Yiu, K. -F. C., Liu, J., Siu, T. K., & Ching, W. -K. (2010). Optimal portfolios with
regime switching and value-at-risk constraint. Automatica, 46(6), 979-989.
Zhu, D. -M., Xie, Y., Ching, W. -K., & Siu, T. -K. (2016). Optimal portfolios with
maximum Value-at-Risk constraint under a hidden Markovian regimeswitching model. Automatica, 74, 194-205.
