mean-CVaR Model Research Articles

Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis

As we know, there is a belief in the finance literature that Value at Risk (VaR) and Conditional Value at Risk (CVaR) are new approaches to manage and control the risk. Regard to, value at risk is not a coherent risk measure and it is not sub-additive and convex, so, we have considered conditional value at risk as a risk measure by different confidence level in the Mean-CVaR and multi objective proportional change Mean-CVaR models and compared these models with our previous mean-VaR models. This paper focuses on the performance evaluation process and portfolios selection by using Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative values for inputs and outputs, but many of data take the negative value. Therefore, we have used our models based on Range Directional Measure (RDM) that can take positive and negative values. Here value at risk is obtained by non-parametric methods such as historical simulation and Monte Carlo simulation. Finally, a numerical example in Iran's market is presented.

Environment and economic risk: An analysis of carbon emission market and portfolio management

Climate change has been one of the biggest and most controversial environmental issues of our times. It affects the global economy, environment and human health. Many researchers find that carbon dioxide (CO2) has contributed the most to climate change between 1750 and 2005. In this study, the orthogonal GARCH (OGARCH) model is applied to examine the time-varying correlations in European CO2 allowance, crude oil and stock markets in US, Europe and China during the Protocol's first commitment period. The results show that the correlations between EUA carbon spot price and the equity markets are higher and more volatile in US and Europe than in China. Then the optimal portfolios consisting these five time series are selected by Mean-Variance and Mean-CVAR models. It shows that the optimal portfolio selected by MV-OGARCH model has the best performance.

Geographical diversification in wheat farming: a copula-based CVaR framework

Purpose – Portfolio theory suggests that geographical diversification of production units could potentially help manage the risks associated with farming, yet little research has been done to evaluate the effectiveness of a geographical diversification strategy in agriculture. The paper aims to discuss this issue. Design/methodology/approach – The paper utilizes several tools from modern finance theory, including Conditional Value-at-Risk (CVaR) and copulas, to construct a model for the evaluation of a diversification strategy. The proposed model – the copula-based mean-CVaR model – is then applied to the producer’s acreage allocation problem to examine the potential benefits of risk reduction from a geographical diversification strategy in US wheat farming. Along with the copula-based model, the multivariate-normal mean-CVaR model is also estimated as a benchmark. Findings – The mean-CVaR optimization results suggest that geographical diversification is a viable risk management strategy from a farm’s profit margin perspective. In addition, the copula-based model appears more appropriate than the traditional multivariate-normal model for conservative agricultural producers who are concerned with the extreme losses of farm profitability in that the later model tends to underestimate the minimum level of risk faced by the producers for a given level of profitability. Originality/value – The effectiveness of geographical diversification in US wheat farming is evaluated. As a methodological contribution, the copula approach is used to model the joint distribution of profit margins and CVaR is employed as a measure of downside risk.

Portfolio Optimization of Global Reits Returns: High-Dimensional Copula-Based Approach

Pacific, Europe, USA, and emerging markets with multivariate t copula based on GARCH model, and to measure portfolio risk with value at risk (VaR) and component VaR (CVaR). The 1,454 REIT price index return observations were collected from 1 Dec 2009 to 29 June 2015 and calculated based on a continuous compound basis. The empirical results showed that the estimated equations of USA, Europe and emerging REIT index returns were ARMA(2,2)-GARCH(1,1), while ASIA-Pacific was ARMA(3,3)-GARCH(1,1). The coefficients of t distribution of these equations were also statistically significant at 1%, meaning the assumption of t distribution for ARMA-GARCH estimation was reasonable. Then, the multivariate t copula was used to construct an optimized portfolio for high dimensional risk management. The Monte Carlo simulation was applied in order to construct the optimized portfolio by using the mean-CVaR model at the given significance level of 5% and to obtain the efficient frontier of the portfolio under different expected returns. Finally, the optimal weights of the portfolio were obtained with the various expected returns in frontier.

A hybrid stock trading system using genetic network programming and mean conditional value-at-risk

This paper describes a hybrid stock trading system based on Genetic Network Programming (GNP) and Mean Conditional Value-at-Risk Model (GNP–CVaR). The proposed method, combining the advantages of evolutionary algorithms and statistical model, has provided useful tools to construct portfolios and generate effective stock trading strategies for investors with different risk-attitudes. Simulation results on five stock indices show that model based on GNP and maximum Sharpe Ratio portfolio performs the best in bull market, and that based on GNP and the global minimum risk portfolio performs the best in bear market. The portfolios constructed by Markowitz’s mean–variance model performs the same as mean-CVaR model. It is clarified that the proposed system significantly improves the function and efficiency of original GNP, which can help investors make profitable decisions.

Multi-Type of Generators Scheduling Optimization Model Based on LHS Method and the Mean-CVaR Model

In order to study the multi-type of generators scheduling optimization model,this paper is based on the LHS method and Mean - CVaR model for research. LHS method is informed as an improved method which can overcome the disadvantage of wind power simulation method of the most commonly used - the monte carlo method which needs a lot of statistical data. Scheme - CVaR model set the minimum electric abandon the wind as the goal, then select the minimum system energy consumption of power generation cost and the minimum pollutant emissions from power generation as the objective function of system combined power generation performance of scheduling ,then under certain constraints, and finally reach the purpose of optimized we expected. Key words: LHS method, Mean - CVaR model, Kantorovich distance

CVaR Robust Mean-CVaR Portfolio Optimization

One of the most important problems faced by every investor is asset allocation. An investor during making investment decisions has to search for equilibrium between risk and returns. Risk and return are uncertain parameters in the suggested portfolio optimization models and should be estimated to solve the problem. However, the estimation might lead to large error in the final decision. One of the widely used and effective approaches for optimization with data uncertainty is robust optimization. In this paper, we present a new robust portfolio optimization technique for mean-CVaR portfolio selection problem under the estimation risk in mean return. We additionally use CVaR as risk measure, to measure the estimation risk in mean return. To solve the model efficiently, we use the smoothing technique of Alexander et al. (2006). We compare the performance of the CVaR robust mean-CVaR model with robust mean-CVaR models using interval and ellipsoidal uncertainty sets. It is observed that the CVaR robust mean-CVaR portfolios are more diversified. Moreover, we study the impact of the value of confidence level on the conservatism level of a portfolio and also on the value of the maximum expected return of the portfolio.

A214 北海道における風力発電所の地理的分散配置による発電量変動リスクの抑制効果解析(OS2 再生可能エネルギー(3))

To spread the wind power technology, the power fluctuation risk of wind power plants, i.e.a risk of frequent and large reduction in the output caused by time-varying wind speeds, need to be decreased. Among measures against the risk, the geographical distribution of wind power plants needs to be examined first because additional facilities, such as backup thermal power plants or electricity storages, are not required. This study analyzes the risk reduction effect of geographically distributing wind power plants among the regions within Hokkaido. The mean-CVaR (Conditional Value at Risk) model is adopted to estimate optimum geographical distribution of the plants. We found that the geographical distribution can largely decrease the power fluctuation risk. Such a result indicates that the Hokkaido has enough variety of climates to largely decrease the power fluctuation risk of wind power plants by domestic geographical distribution.

Joint optimal ordering and weather hedging decisions: mean-CVaR model

This paper considers the problem of hedging inventory risk for a seasonal product whose demand is sensitive to weather conditions, such as the average seasonal temperature. The newsvendor not only decides the order quantity, but also adopts a weather hedging strategy. A typical hedging strategy is to use an option (weather derivative) that is constructed on a weather index before the season begins, which will compensate the buyer of the option if the actual seasonal weather index is above (or below) a given strike level. We adopt the risk measure of Conditional-Value at Risk (\(\hbox{CVaR}\)) and explore the joint decision problem in mean-CVaR criterion. We find that the weather derivative hedging can increase order quantity. Furthermore, it can help the risk-averse newsvendor improve both expected overall and downside profits.

Portfolio Optimization via Pair Copula-GARCH-EVT-CVaR Model

This paper uses CVaR as the risk measure and applies EVT to model the tails of the return series so as to estimate risk of assets more accurately. This paper also applies pair Copula to capture the inter-dependence structure between assets and constructs pair Copula-GARCH-EVT model; then, we combine it with Monte Carlo Simulation and Mean-CVaR model to optimize portfolio. Finally, an empirical study of four Indexes from Chinese Stock Market is performed and the result suggests that pair Copula can better characterize the inter-dependence structure between assets and the performance of pair Copula-GARCH-EVT-CVaR model is better than that of multivariate t Copula-GARCH-EVT-CVaR model in portfolio optimization.

Mean-risk models using two risk measures: a multi-objective approach

This paper proposes a model for portfolio optimization, in which distributions are characterized and compared on the basis of three statistics: the expected value, the variance and the CVaR at a specified confidence level. The problem is multi-objective and transformed into a single objective problem in which variance is minimized while constraints are imposed on the expected value and CVaR. In the case of discrete random variables, the problem is a quadratic program. The mean-variance (mean-CVaR) efficient solutions that are not dominated with respect to CVaR (variance) are particular efficient solutions of the proposed model. In addition, the model has efficient solutions that are discarded by both mean-variance and mean-CVaR models, although they may improve the return distribution. The model is tested on real data drawn from the FTSE 100 index. An analysis of the return distribution of the chosen portfolios is presented.