scholarly journals Portfolio theory in the selection of oil investment projects

2012 ◽  
Vol 19 (2) ◽  
pp. 265-272
Author(s):  
Jalimar Guimarães Simplício ◽  
Celso Funcia Lemme ◽  
Ricardo Pereira Câmara Leal
Keyword(s):  
Efficient Frontier ◽  
Investment Project ◽  
Optimization Procedure ◽  
Financial Assets ◽  
Investment Projects ◽  
The Mean ◽  
Project Portfolios ◽  
Investment Portfolios ◽  
Mean Variance ◽  
Selection Of

The objective of this article is to compare investment project selection using the efficient frontier in the mean-variance space based on optimization models introduced by Markowitz (1952) with the project ranking method according to the profitability index (PI). The selection of real assets by companies did not incorporate the mean-variance optimization procedure in the same way the selection of financial assets in investment portfolios did. The process of selection and formation of portfolios of investment projects for the oil area of a company in the energy industry was analyzed. Project portfolios formed according to the usual company practice of ranking by their PI were compared with those that result from applying mean-variance optimization through Monte Carlo simulation, which allows the computation of mean returns, variances, and covariances for the set of projects considered. The inefficiency of project portfolios obtained by ranking according to the PI compared to those obtained by the method of Markowitz suggests that there are opportunities to improve the process of selecting the set of projects to be implemented by companies.

scholarly journals A Random Parameter Model for Continuous-Time Mean-Variance Asset-Liability Management

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Hui-qiang Ma ◽  
Meng Wu ◽  
Nan-jing Huang
Keyword(s):  
Continuous Time ◽  
Efficient Frontier ◽  
Random Parameter ◽  
Management Problem ◽  
Random Parameters ◽  
Asset Liability Management ◽  
Linear Quadratic ◽  
Liability Management ◽  
The Mean ◽  
Mean Variance

We consider a continuous-time mean-variance asset-liability management problem in a market with random market parameters; that is, interest rate, appreciation rates, and volatility rates are considered to be stochastic processes. By using the theories of stochastic linear-quadratic (LQ) optimal control and backward stochastic differential equations (BSDEs), we tackle this problem and derive optimal investment strategies as well as the mean-variance efficient frontier analytically in terms of the solution of BSDEs. We find that the efficient frontier is still a parabola in a market with random parameters. Comparing with the existing results, we also find that the liability does not affect the feasibility of the mean-variance portfolio selection problem. However, in an incomplete market with random parameters, the liability can not be fully hedged.

scholarly journals Mean-Variance Portfolio Selection with Tracking Error Penalization

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1915
Author(s):  
William Lefebvre ◽  
Grégoire Loeper ◽  
Huyên Pham
Keyword(s):  
Portfolio Selection ◽  
Tracking Error ◽  
Efficient Frontier ◽  
Minimum Variance ◽  
Sharpe Ratio ◽  
Portfolio Strategy ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Variance Criterion

This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in the case of misspecified parameters, by “fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean–Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.

Application of the Mean-Variance Theory and Resampling Technique for the Italian Energy Portfolio Settlement

2013 ◽  
Vol 869-870 ◽  
pp. 581-592
Author(s):  
Mauro Arnesano ◽  
Antonio Paolo Carlucci ◽  
Giovanni D'Oria ◽  
Alessio Guadalupi ◽  
Domenico Laforgia
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Estimation Error ◽  
Efficient Frontier ◽  
Energy Generation ◽  
Energy System ◽  
Estimation Errors ◽  
Energy Mix ◽  
The Mean ◽  
Mean Variance

The energy planning based on Mean - Variance theory, guides the investors in investment decisions, trying to maximize the return and minimize the risk of investment. However, this theory is based on strong hypotheses and, in addition, input data are often affected by estimation errors. Moreover, this theory determines poor diversification increasing return and risk of the portfolio, and strong variability of the outputs when inputs are varied.In the first part of the paper, the Mean - Variance theory was applied to the energy generation in Italy; in particular, the analysis was on the actual energy mix, but also assuming the use of nuclear technology and taking into account verisimilar improvement, of technologies in the future.On the other hand, in the second part of the paper, a methodology has been applied in order to limit the problems of Mean-Variance theory applied to the energy mix settlement. In particular, the input variables have been calculated using Monte Carlo simulation, in order to reduce the estimation error, and the Resampled EfficiencyTMtechnique has been applied in order to calculate the resulting new “average” efficient frontier. This methodology has been applied either not limiting or limiting the minimum and maximum percentage for every energy generation technology, in order to simulate constraints due, for example, to the technological characteristics of the plant, the availability of the sources and eventually to norms, to the territorial characteristics and to the socio-political choices. The application of Mean - Variance theory allowed to obtain energy portfolio, alternative to the actual, characterized by higher values of expected returns an lower values of risk.It was also shown that the application of the Resampled EfficiencyTMtechnique with data originated with the Monte Carlo simulation effectively tackles the problems of Mean - Variance theory; in this way, the decision maker is helped in making decisions in the energy system policy and development.Thanks to this approach, applied in particular to the Italian energy contest, it was also possible to evaluate the effectiveness of the introduced modifications to the Italian actual energy mix to achieve the 2020 European Energy Directive targets in particular concerning the reduction of CO2levels.

Portfolio selection and matching: a synthesis

1992 ◽  
Vol 119 (1) ◽  
pp. 87-105 ◽  
Author(s):  
M. Sherris
Keyword(s):  
Portfolio Selection ◽  
Life Insurance ◽  
General Framework ◽  
Pension Fund ◽  
Central Value ◽  
Efficient Portfolios ◽  
The Mean ◽  
Mean Variance ◽  
Special Case ◽  
Selection Of

AbstractThis paper considers a general framework for the selection of assets to meet the liabilities of a life insurance or pension fund. This general framework contains the mean-variance efficient portfolios of modern portfolio theory as a special case. The paper also demonstrates how the portfolio selection and matching approach of Wise (1984a, 1984b, 1987a, 1987b) and Wilkie (1985) fits into this general framework. The matching portfolio is derived as a special case, and is also shown to have implications for determining the central value of the liabilities.

Prescriptive portfolio selection: a compromise between fast and slow thinking

2017 ◽  
Vol 9 (2) ◽  
pp. 98-116 ◽  
Author(s):  
Omid Momen ◽  
Akbar Esfahanipour ◽  
Abbas Seifi
Keyword(s):  
Portfolio Selection ◽  
Stock Exchange ◽  
Efficient Frontier ◽  
Fast System ◽  
Content Type ◽  
The Mean ◽  
Selection For ◽  
Mean Variance ◽  
Prescriptive Analysis

PurposeThe purpose of this paper is to develop a prescriptive portfolio selection (PPS) model based on a compromise between the idea of “fast” and “slow” thinking proposed by Kahneman. Design/methodology/approach“Fast” thinking is effortless and comfortable for investors, while “slow” thinking may result in better performance. These two systems are related to the first two types of analysis in the decision theory: descriptive, normative and prescriptive analysis. However, to compromise between “fast” and “slow” thinking, “overconfidence” is used as a weighting parameter. A case study including a sample of 161 active investors in Tehran Stock Exchange (TSE) is provided. Moreover, the feasibility and optimality of the model are discussed. FindingsResults show that the PPS recommendations are efficient with a shift from the mean-variance efficient frontier; investors prefer PPS portfolios over the advisor recommendations; and investors have no significant preference between PPS and their own expectations. Research limitations/implicationsTwo assumptions of this study include: first, investors follow their “fast” system of thinking by themselves. Second, the investors’ “slow” system of thinking is represented by advisor recommendations which are simple expected value of risk and return. Therefore, considering these two assumptions for any application is the main limitation of this study. Moreover, the authors did not have access to more investors in TSE or other financial markets. Originality/valueThis is the first study that includes overconfidence in modeling portfolio selection for the purpose of achieving a portfolio that has a reasonable performance and one that investors are comfortable with.

On the Endogeneity of the Mean-Variance Efficient Frontier

2002 ◽  
Vol 33 (4) ◽  
pp. 357-366
Author(s):  
R. A. Somerville ◽  
Paul G. J. O'connell
Keyword(s):  
Efficient Frontier ◽  
The Mean ◽  
Mean Variance

scholarly journals Continuous-Time Mean-Variance Portfolio Selection under the CEV Process

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Hui-qiang Ma
Keyword(s):  
Portfolio Selection ◽  
Continuous Time ◽  
Stock Price ◽  
Efficient Frontier ◽  
Optimal Portfolio ◽  
Portfolio Strategy ◽  
Cev Process ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance

We consider a continuous-time mean-variance portfolio selection model when stock price follows the constant elasticity of variance (CEV) process. The aim of this paper is to derive an optimal portfolio strategy and the efficient frontier. The mean-variance portfolio selection problem is formulated as a linearly constrained convex program problem. By employing the Lagrange multiplier method and stochastic optimal control theory, we obtain the optimal portfolio strategy and mean-variance efficient frontier analytically. The results show that the mean-variance efficient frontier is still a parabola in the mean-variance plane, and the optimal strategies depend not only on the total wealth but also on the stock price. Moreover, some numerical examples are given to analyze the sensitivity of the efficient frontier with respect to the elasticity parameter and to illustrate the results presented in this paper. The numerical results show that the price of risk decreases as the elasticity coefficient increases.

scholarly journals Households' Portfolio Diversification

2010 ◽  
pp. 117-143
Author(s):  
Tullio Jappelli ◽  
Christian Julliard ◽  
Marco Pagano
Keyword(s):  
Representative Sample ◽  
Null Hypothesis ◽  
Efficient Frontier ◽  
Efficiency Analysis ◽  
Short Selling ◽  
Portfolio Diversification ◽  
Italian Population ◽  
Bank Account ◽  
The Mean ◽  
Mean Variance

This paper performs an efficiency analysis of households portfolios based on the comparison of observed portfolios with the mean-variance frontier of assets returns. Data on household portfolios are drawn from a representative sample of the Italian population with at least a bank account. We find that most households' portfolios are extremely close to the efficient frontier once we explicitly take into account no short-selling constraints, while the null hypothesis of efficiency is rejected for all portfolios if we don't consider these constraints.

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