A New Portfolio Rebalancing Model with Transaction Costs

A portfolio rebalancing model with self-finance strategy and consideration of V-shaped transaction cost is presented in this paper. Our main contribution is that a new constraint is introduced to confirm that the rebalance necessity of the existing portfolio needs to be adjusted. The constraint is constructed by considering both the transaction amount and transaction cost without any additional supply to the investment amount. The V-shaped transaction cost function is used to calculate the transaction cost of the portfolio, and conditional value at risk (CVaR) is used to measure the risk of the portfolios. Computational tests on practical financial data show that the proposed model is effective and the rebalanced portfolio increases the expected return of the portfolio and reduces the CVaR risk of the portfolio.

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Developing an Algorithm for Urban Flood Management With the Aim of Reducing Damage and Costs Using the Concept of Conditional Value at Risk

10.21203/rs.3.rs-266864/v1 ◽

2021 ◽

Author(s):

Pedram Eshaghieh Firoozabadi ◽

sara nazif ◽

Seyed Abbas Hosseini ◽

Jafar Yazdi

Keyword(s):

At Risk ◽

Hybrid Model ◽

Best Management Practices ◽

Value At Risk ◽

Management Practices ◽

Flood Management ◽

Surface Flow ◽

Conditional Value At Risk ◽

Urban Flood ◽

Proposed Model

Abstract Flooding in urban area affects the lives of people and could cause huge damages. In this study, a model is proposed for urban flood management with the aim of reducing the total costs. For this purpose, a hybrid model has been developed using SWMM and a quasi-two-dimensional model based on the cellular automata (CA) capable of considering surface flow infiltration. Based on the hybrid model outputs, the best management practices (BMPs) scenarios are proposed. In the next step, a damage estimation model has been developed using depth-damage curves. The amount of damage has been estimated for the scenarios in different rainfall return periods to obtain the damage and cost- probability functions. The conditional value at risk (CVaR) are estimated based on these functions which is the basis of decision making about the scenarios. The proposed model is examined in an urban catchment located in Tehran, Iran. In this study, five scenarios have been designed on the basis of different BMPs. It has been found that the scenario of permeable pavements has the lowest risk. The proposed model enables the decision makers to choose the best scenario with the minimum cost taking into account the risk associated with each scenario.

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EFFECTS OF MULTIPLE CRITERIA ON PORTFOLIO OPTIMIZATION

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622014500047 ◽

2014 ◽

Vol 13 (01) ◽

pp. 77-99 ◽

Cited By ~ 6

Author(s):

TUNCER ŞAKAR CEREN ◽

MURAT KÖKSALAN

Keyword(s):

At Risk ◽

Portfolio Optimization ◽

Decision Maker ◽

Value At Risk ◽

Efficient Frontier ◽

Conditional Value At Risk ◽

Computational Studies ◽

Expected Return ◽

Risk Criteria ◽

Return Variance

We study the effects of considering different criteria simultaneously on portfolio optimization. Using a single-period optimization setting, we use various combinations of expected return, variance, liquidity and Conditional Value at Risk criteria. With stocks from Borsa Istanbul, we make computational studies to show the effects of these criteria on objective and decision spaces. We also consider cardinality and weight constraints and study their effects on the results. In general, we observe that considering alternative criteria results in enlarged regions in the efficient frontier that may be of interest to the decision maker. We discuss the results of our experiments and provide insights.

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Mean-Conditional Value-at-Risk Optimal Energy Storage Operation in the Presence of Transaction Costs

IEEE Transactions on Power Systems ◽

10.1109/tpwrs.2014.2341642 ◽

2015 ◽

Vol 30 (3) ◽

pp. 1222-1232 ◽

Cited By ~ 43

Author(s):

Somayeh Moazeni ◽

Warren B. Powell ◽

Amir H. Hajimiragha

Keyword(s):

At Risk ◽

Energy Storage ◽

Transaction Costs ◽

Value At Risk ◽

Conditional Value At Risk ◽

Optimal Energy

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Wholesale Price for Supply Chain Coordination via Conditional Value-at-Risk Minimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.88 ◽

2010 ◽

Vol 20-23 ◽

pp. 88-93 ◽

Cited By ~ 1

Author(s):

Chuan Xu Wang

Keyword(s):

At Risk ◽

Supply Chain ◽

Value At Risk ◽

Supply Chain Coordination ◽

Wholesale Price ◽

Conditional Value At Risk ◽

Optimal Order ◽

Individual Member ◽

Risk Minimization ◽

Proposed Model

The theory of the conditional value-at-risk (CVaR) in financial risk management is considered in this paper to develop a model of supply chain coordination with a wholesale pricing policy. The proposed model solves the drawbacks of objective function in current supply chain coordination model. A numerical example is given to demonstrate the effectiveness of the proposed model. The following helpful conclusions are drawn from the paper: with the increase of the degree of risk averting for supply chain individual member, the optimal order quantity of supply chain is decreasing, while the optimal profit is decreasing; If supplier’s risk averting degree increases, supplier has to increase wholesale price to achieve supply chain coordination; If retailer’s risk averting degree increases, supplier has to decrease wholesale price to achieve supply chain coordination.

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Cryptocurrency Portfolio Selection—A Multicriteria Approach

Mathematics ◽

10.3390/math9141677 ◽

2021 ◽

Vol 9 (14) ◽

pp. 1677

Author(s):

Zdravka Aljinović ◽

Branka Marasović ◽

Tea Šestanović

Keyword(s):

At Risk ◽

Standard Deviation ◽

Portfolio Selection ◽

Value At Risk ◽

Selection Process ◽

Efficient Frontier ◽

Conditional Value At Risk ◽

Optimal Portfolios ◽

Promethee Ii ◽

Proposed Model

This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.

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Multi-stage stochastic model in portfolio selection problem

Filomat ◽

10.2298/fil1803991b ◽

2018 ◽

Vol 32 (3) ◽

pp. 991-1001

Author(s):

Shokoofeh Banihashemi ◽

Ali Azarpour ◽

Marziye Kaveh

Keyword(s):

At Risk ◽

Portfolio Selection ◽

Value At Risk ◽

Risk Measures ◽

Risk Measure ◽

Selection Problem ◽

Conditional Value At Risk ◽

Expected Return ◽

Portfolio Selection Problem ◽

Multi Stage

This paper is a novel work of portfolio-selection problem solving using multi objective model considering four parameters, Expected return, downside beta coefficient, semivariance and conditional value at risk at a specified confidence level. Multi-period models can be defined as stochastic models. Early studies on portfolio selection developed using variance as a risk measure; although, theories and practices revealed that variance, considering its downsides, is not a desirable risk measure. To increase accuracy and overcoming negative aspects of variance, downside risk measures like semivarinace, downside beta covariance, value at risk and conditional value at risk was other risk measures that replaced in models. These risk measures all have advantages over variance and previous works using these parameters have shown improvements in the best portfolio selection. Purposed models are solved using genetic algorithm and for the topic completion, numerical example and plots to measure the performance of model in four dimensions are provided.

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Conditional Value-at-Risk-Based Portfolio Optimization

Metaheuristic Approaches to Portfolio Optimization - Advances in Information Quality and Management ◽

10.4018/978-1-5225-8103-1.ch004 ◽

2019 ◽

pp. 82-108

Author(s):

Jhuma Ray ◽

Siddhartha Bhattacharyya ◽

N. Bhupendro Singh

Keyword(s):

At Risk ◽

Portfolio Optimization ◽

Value At Risk ◽

Risk Measures ◽

Real Life ◽

Conditional Value At Risk ◽

Market Theory ◽

Market Conditions ◽

Expected Return ◽

Multi Objective

Over the past few decades, an extensive research on the multi-objective decision making and combinatorial optimization of real world's financial transactions has taken place. The modern capital market theory problem of portfolio optimization stands to be a multi-objective problem aiming at the maximization of the expected return of the portfolio in turn minimizing portfolio risk. The conditional value-at-risk (CVaR) is a widely used measure for determining the risk measures of a portfolio in volatile market conditions. A heuristic approach to portfolio optimization problem using ant colony optimization (ACO) technique centering on optimizing the conditional value-at-risk (CVaR) measure in different market conditions based on several objectives and constraints has been reported in this paper. The proposed ACO approach is proved to be reliable on a collection of several real-life financial instruments as compared to its value-at-risk (VaR) counterpart. The results obtained show encouraging avenues in determining optimal portfolio returns.

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Value-at-Risk Efficient Portfolio Selection Using Goal Programming

Review of Pacific Basin Financial Markets and Policies ◽

10.1142/s0219091508001313 ◽

2008 ◽

Vol 11 (02) ◽

pp. 187-200 ◽

Cited By ~ 9

Author(s):

Hsin-Hung Chen

Keyword(s):

At Risk ◽

Portfolio Selection ◽

Goal Programming ◽

Asset Allocation ◽

Value At Risk ◽

Expected Return ◽

Allocation Model ◽

Proposed Model ◽

Portfolio Selection Model ◽

The Empirical Analysis

The purpose of this study is to apply polynomial goal programming to establish a new portfolio selection model that considers the tradeoffs between expected return and Value-at-Risk (VaR) of portfolios and the flexibility of incorporating investor's preferences. The historical data of 10 international stock markets of Pacific Rim countries were used in the empirical analysis. The results showed that the proposed model demonstrated the ability to resolve the problems of a traditional asset allocation model. The validity and fitness of the proposed model were confirmed.

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