Mean-conditional Value Research Articles

Risk-averse distributionally robust optimization for construction waste reverse logistics with a joint chance constraint

The uncertainty of the amount of construction and demolition waste (CDW) generation affects the CDW reverse logistics network service level. We investigate a CDW reverse logistics network location-routing problem considering uncertainties. To minimize the total social cost, a two-stage risk-averse distributionally robust optimization model is developed, which aims to optimize the location and number of CDW disposal facilities and the CDW transportation scheme. We introduce the mean-conditional value at risk measure and a joint chance constraint into our model to consider the government’s risk aversion. The above model is approximated as a standard second-order cone programming model (SOCP) considering a special case. To exactly solve the SOCP, we design an outer approximation algorithm. Several performance tests and a case study are proposed, and sensitivity analysis provides helpful managerial insights.

Distributionally Robust Optimization Approaches for a Stochastic Mobile Facility Fleet Sizing, Routing, and Scheduling Problem

We propose two distributionally robust optimization (DRO) models for a mobile facility (MF) fleet-sizing, routing, and scheduling problem (MFRSP) with time-dependent and random demand as well as methodologies for solving these models. Specifically, given a set of MFs, a planning horizon, and a service region, our models aim to find the number of MFs to use (i.e., fleet size) within the planning horizon and a route and time schedule for each MF in the fleet. The objective is to minimize the fixed cost of establishing the MF fleet plus a risk measure (expectation or mean conditional value at risk) of the operational cost over all demand distributions defined by an ambiguity set. In the first model, we use an ambiguity set based on the demand’s mean, support, and mean absolute deviation. In the second model, we use an ambiguity set that incorporates all distributions within a 1-Wasserstein distance from a reference distribution. To solve the proposed DRO models, we propose a decomposition-based algorithm. In addition, we derive valid lower bound inequalities that efficiently strengthen the master problem in the decomposition algorithm, thus improving convergence. We also derive two families of symmetry-breaking constraints that improve the solvability of the proposed models. Finally, we present extensive computational experiments comparing the operational and computational performance of the proposed models and a stochastic programming model, demonstrating when significant performance improvements could be gained, and derive insights into the MFRSP. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1153 .

Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach

Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their clients’ portfolios. The advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating macroeconomic conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical tests on the resulting portfolio are conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against three benchmark portfolios over a period of almost twelve years (01/2007–11/2019). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, volatility, Sharpe ratio, maximum drawdown, and 99% CVaR.

Mean-conditional value at risk model for the stochastic project scheduling problem

Every project faces different opportunities and risks during its lifecycle. Risks are the factors that can disrupt the successful implementation of projects and cause failure in achieving project goals. Advancing the project while considering its risks is one of the most essential aspects of project management. Planning and scheduling can be applied in a way that reduces the risks in the management of projects. In this paper, a new scenario-based mean-conditional value-at-risk (CVaR) model is developed to minimize the risk of the project’s net present value (NPV). Moreover, the trade-off between expected NPV and the risk of NPV is considered in this study. Start time policies are also used to specify the start times of project activities. Two multi-objective optimization algorithms including Non-dominated Sorting Genetic Algorithms (NSGA-II), and Multi-Objective Vibration Damping Optimization (MOVDO) are applied to identify the Pareto optimal solution. The efficiency of the algorithms is assessed based on some performance criteria. The results of the computational experiments show that at identical run time MOVDO functions better in terms of hypervolume indicator, while NSGA- II better results in other performance metrics.

Multiperiod Mean Conditional Value at Risk Asset Allocation: Is It Advantageous to Be Time Consistent?

We formulate the multiperiod, time consistent mean-CVAR (conditional value at risk) asset allocation problem in a form amenable to numerical computation. Our numerical algorithm can impose realisti...

Fusing Economic Indicators for Portfolio Optimization - A Simulation-Based Approach

Portfolio optimization and quantitative risk management have been extensively studied since the 1990s, and attracted even more attention after the financial crisis in 2008. Such a disastrous event required portfolio managers to better manage the risk and return trade-off when building their clients' portfolios. With that said, the advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating the macro-economy conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing eleven macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical test on the achieved portfolio is conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against the three benchmark portfolios over a twelve-year period (01/2007 - 12/2018). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, 99% VaR, and Sharpe ratio.

Financial Crisis and Financialization Acuity on the Diversification Benefits of Commodities: A Stochastic Asset Allocation

This research investigates the portfolio diversification benefits of commodities in the backdrop of uncertainty caused by the financial crisis, increased Financialization and speculation in commodity markets. Portfolios are formed out of varied asset classes comprise of equity, bond, infra structure, commodity spot & futures indices and sectoral indices such as agri, metals and energy sectors over a period 2005-2013. It employed stochastic mean-conditional value at risk (CVaR) optimization model. CVaR quantifies downside risk and helps to minimize extreme losses. The ex-post stability of the results and the robustness of the model are validated through back testing. Different performance measures such as Sharpe ratio, modified Sharpe ratio with conditional value at risk, opportunity cost and maximum draw down are employed to compare the results of multi asset portfolios. The results support the evidence of the diversification benefit in commodity futures indices than in spot indices. It also highlighted the significance of Agri commodities in offering portfolio diversification than energy and metal commodities. The diversification benefit of later are found to be reduced with the advent of financial crisis. It also provides empirical evidence that the diversification benefits of energy and metal commodities were reduced during the financial crisis and this can be attributed to the observed increase in Financialization and cross-asset market integration during the crisis period.

Optimal Portfolio with Structured Product in a Framework of Mean Conditional Value at Risk

In the recent recession market, we are in the objective to invest into secured product in a time horizon of one or two years. The objective is to maintain at least the capital invested if the market still goes down and to be able to benefit part of the market return when the market goes up. That’s why we choose the simple structured product named guaranteed capital protection as part of the assets for our optimal portfolio. The aim of this document is to provide an answer to the question: what is the proportion of our portfolio we should invest into the capital protection? The document is organized in as follows. We first describe the standard assets proposed to be used in the portfolio; we then describe capital protection product. We describe how to obtain the result. We interpret the results for the optimal portfolio with structured product. Comparison with the portfolio which has only assets is also provided.

A mean-CVaR-skewness portfolio optimization model based on asymmetric Laplace distribution

In the presence of uncertainty of asset returns, choosing an appropriate risk measure and determining the optimal weights of assets in a portfolio remain formidable and challenging problems. In this paper, we propose and study a mean-conditional value at risk-skewness portfolio optimization model based on the asymmetric Laplace distribution, which is suitable for describing the leptokurtosis, fat-tail, and skewness characteristics of financial assets. In addition, skewness is added into the portfolio optimization model to meet the diverse needs of investors. To solve this multi-objective problem, we suggest a simplified model with exactly the same solution. This modified model greatly reduces the complexity of the problem. Therefore, the mean-conditional value at risk-skewness model can be correspondingly solved. In order to illustrate the method, we provide an application concerning the portfolio allocation of 19 constituent stocks of S&P 500 index using our model. We show that this model could make important contributions to research on investment decision making.

Assessing the role of U.S. timberland assets in a mixed portfolio under the mean-conditional value at risk framework

This study examines the role of U.S. timberland assets in a mixed portfolio from the risk perspective. Under the mean-conditional value at risk (M-CVaR) optimization framework, the efficient frontier of the mixed portfolio is dramatically improved after adding timberland assets in comparison of the mean-variance (M-V) efficient frontier. The asset allocation strategies formulated by the static and dynamic optimizations indicate that timberland assets maintain a significant allocation in the mixed portfolio. Moreover, risk decomposition is used to identify the risk sources under four different scenarios. It is found that large-cap stocks and small-cap stocks are generally risk intensifiers, whereas treasury bonds, treasury bills, and timberland assets are risk diversifiers.

The Impact of Skewness and Fat Tails on the Asset Allocation Decision

The authors modeled the non-normal returns of multiple asset classes by using a multivariate truncated Lévy flight distribution and incorporating non-normal returns into the mean-conditional value at risk (M-CVaR) optimization framework. In a series of controlled optimizations, they found that both skewness and kurtosis affect the M-CVaR optimization and lead to substantially different allocations than do the traditional mean–variance optimizations. They also found that the M-CVaR optimization would have been beneficial during the 2008 financial crisis.

Optimal Mortgage Loan Diversification

Homebuyers in several countries may finance the purchase of their properties using different variants of either adjustable-rate mortgages (ARMs) or fixed-rate mortgages (FRMs). The variety and complexity of these loan products poses a risk management task for mortgage bank advisors to recommend the right mortgage loan strategy for the individual mortgagor; almost all mortgage banks advise their customers to take a single loan product. This argument is often justified by the fact that trade frictions make it unattractive to hold a portfolio of loans as a private home owner. Even with transaction costs, however, we show in this paper that most mortgagors with some degree of risk aversion benefit from holding a mortgage portfolio. To do so we develop a multistage Mean-Conditional Value at Risk (MCVaR) model to consider the risk of the mortgage payment frequency function explicitly using a coherent risk measure. In addition to the diversification benefits we also show that the multistage model produces superior results as compared to single period models and that the solutions are robust with regards to changes in uncertainty parameters in particular for risk averse mortgagors. Finally, we show how the model can be used to calculate fair premia for adjustable rate mortgages with interest rate guarantees (caps) which are becoming increasingly popular as a hybrid product between the existing ARM and FRM mortgages.