scholarly journals Forecasting Efficient Risk/Return Frontier for Equity Risk with a KTAP Approach—A Case Study in Milan Stock Exchange

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1055 ◽  
Author(s):  
Marina Dolfin ◽  
Leone Leonida ◽  
Eleonora Muzzupappa
Keyword(s):  
Value At Risk ◽  
Stock Exchange ◽  
Stochastic Game ◽  
Optimization Theory ◽  
Efficient Frontier ◽  
Conditional Value At Risk ◽  
Risk Return ◽  
Equity Risk ◽  
Research Perspectives

We introduce and discuss a dynamics of interaction of risky assets in a portfolio by resorting to methods of statistical mechanics developed to model the evolution of systems whose microscopic state may be augmented by variables which are not mechanical. Statistical methods are applied in the present paper in order to forecast the dynamics of risk/return efficient frontier for equity risk. Specifically, we adopt the methodologies of the kinetic theory for active particles (KTAP) with stochastic game-type interactions and apply the proposed model to a case study analyzing a subset of stocks traded in Milan Stock Exchange. In particular, we evaluate the efficient risk/return frontier within the mean/variance portfolio optimization theory for 13 principal components of the Milan Stock Exchange and apply the proposed kinetic model to forecast its short-term evolution (within one year). The model has the aim to pave the way to many different research perspectives and applications discussed eventually in the paper. In particular, the case of efficient frontier obtained by minimizing the Conditional Value-at-Risk (CVaR) is introduced and a preliminary result is proposed.

scholarly journals Risk-return analysis of the investment portfolio of two stocks, under the Markowitz model. Case study: Two multinational companies listed on the Mexican Stock Exchange

2020 ◽  
pp. 21-27
Author(s):  
María Trinidad ALVAREZ-MEDINA
Keyword(s):  
Stock Exchange ◽  
Industrial Sector ◽  
Financial Assets ◽  
Investment Portfolio ◽  
Model Case ◽  
Risk Return ◽  
Economic Entity ◽  
Food And Beverage ◽  
And Performance

Investment in productive and financial assets are a decision made as an alternative to direct resources to bring greater value and higher performance to an economic entity. The objective of this article is to analyze the return risk of the stocks of two companies listed on the Mexican Stock Exchange (BMV), presenting the case of the companies Grupo Bimbo, SAB de CV, and GRUMA, SAB de CV, both companies listed on the Mexican Stock Exchange, belonging to the industrial sector specifically the food and beverage sub-sector, being the most representative companies of this sector. The return on the portfolio is 0.27256% and the risk is 0.0121862, with an investment of 50% in each of them. The period analyzed was from 2015 to 2018. It is important to base decision-making by considering the risk analysis and performance of financial assets in where you wish to invest, in addition to relying on other analyzes such as fundamental and technical analysis, among others.

scholarly journals CVaR Regression Based on the Relation between CVaR and Mixed-Quantile Quadrangles

2019 ◽  
Vol 12 (3) ◽  
pp. 107 ◽  
Author(s):  
Golodnikov ◽  
Kuzmenko ◽  
Uryasev
Keyword(s):  
Linear Regression ◽  
Value At Risk ◽  
Risk Measure ◽  
Error Function ◽  
Decomposition Theorem ◽  
Discrete Distributions ◽  
Conditional Value At Risk ◽  
Suggested Approach ◽  
Risk Functions

A popular risk measure, conditional value-at-risk (CVaR), is called expected shortfall (ES) in financial applications. The research presented involved developing algorithms for the implementation of linear regression for estimating CVaR as a function of some factors. Such regression is called CVaR (superquantile) regression. The main statement of this paper is: CVaR linear regression can be reduced to minimizing the Rockafellar error function with linear programming. The theoretical basis for the analysis is established with the quadrangle theory of risk functions. We derived relationships between elements of CVaR quadrangle and mixed-quantile quadrangle for discrete distributions with equally probable atoms. The deviation in the CVaR quadrangle is an integral. We present two equivalent variants of discretization of this integral, which resulted in two sets of parameters for the mixed-quantile quadrangle. For the first set of parameters, the minimization of error from the CVaR quadrangle is equivalent to the minimization of the Rockafellar error from the mixed-quantile quadrangle. Alternatively, a two-stage procedure based on the decomposition theorem can be used for CVaR linear regression with both sets of parameters. This procedure is valid because the deviation in the mixed-quantile quadrangle (called mixed CVaR deviation) coincides with the deviation in the CVaR quadrangle for both sets of parameters. We illustrated theoretical results with a case study demonstrating the numerical efficiency of the suggested approach. The case study codes, data, and results are posted on the website. The case study was done with the Portfolio Safeguard (PSG) optimization package, which has precoded risk, deviation, and error functions for the considered quadrangles.

EFFECTS OF MULTIPLE CRITERIA ON PORTFOLIO OPTIMIZATION

2014 ◽  
Vol 13 (01) ◽  
pp. 77-99 ◽  
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.

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.

scholarly journals Two-Stage Robust Optimization Model for Uncertainty Investment Portfolio Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Dongqing Luan ◽  
Chuming Wang ◽  
Zhong Wu ◽  
Zhijie Xia
Keyword(s):  
Robust Optimization ◽  
Financial Management ◽  
Value At Risk ◽  
Optimization Theory ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Equal Weight ◽  
Investment Portfolio ◽  
Two Stage ◽  
Mip Model

Investment portfolio can provide investors with a more robust financial management plan, but the uncertainty of its parameters is a key factor affecting performance. This paper conducts research on investment portfolios and constructs a two-stage mixed integer programming (TS-MIP) model, which comprehensively considers the five dimensions of profit, diversity, skewness, information entropy, and conditional value at risk. But the deterministic TS-MIP model cannot cope with the uncertainty. Therefore, this paper constructs a two-stage robust optimization (TS-RO) model by introducing robust optimization theory. In case experiments, data crawler technology is used to obtain actual data from real websites, and a variety of methods are used to verify the effectiveness of the proposed model in dealing with uncertainty. The comparison of models found that, compared with the traditional equal weight model, the investment benefits of the TS-MIP model and the TS-RO model proposed have been improved. Among them, the Sharpe ratio, Sortino ratio, and Treynor ratio have the largest increase of 19.30%, 8.25%, and 7.34%, respectively.

scholarly journals Local Likelihood Density Estimation and Value-at-Risk

2010 ◽  
Vol 2010 ◽  
pp. 1-26 ◽  
Author(s):  
Christian Gourieroux ◽  
Joann Jasiak
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Local Approximation ◽  
Conditional Density ◽  
Conditional Value At Risk ◽  
Computation Method ◽  
Toronto Stock Exchange ◽  
Historical Simulation ◽  
Portfolio Returns

This paper presents a new nonparametric method for computing the conditional Value-at-Risk, based on a local approximation of the conditional density function in a neighborhood of a predetermined extreme value for univariate and multivariate series of portfolio returns. For illustration, the method is applied to intraday VaR estimation on portfolios of two stocks traded on the Toronto Stock Exchange. The performance of the new VaR computation method is compared to the historical simulation, variance-covariance, and J. P. Morgan methods.

Application of Portfolio Theory Based on CVaR in Determining Optimal Spinning Reserve with Consideration of Load and Wind Power Uncertainties

2013 ◽  
Vol 724-725 ◽  
pp. 649-654
Author(s):  
Jun Li Wu ◽  
Bu Han Zhang ◽  
Zhen Yin Xiao ◽  
Kui Wang
Keyword(s):  
Power System ◽  
Wind Power ◽  
Value At Risk ◽  
Efficient Frontier ◽  
Portfolio Theory ◽  
Conditional Value At Risk ◽  
Power Prediction ◽  
Spinning Reserve ◽  
The Cost ◽  
Installed Capacity

With the increased installed capacity of wind power in power system, determining optimal spinning reserve capacity is one of the most important problems in operation of electricity power system. CVaR (conditional value at risk) is introduced to calculate the risk of the cost associated with load shed and abandoning wind power with the consideration of load and wind power prediction uncertainties. Portfolio theory based on CVaR is used to build the Cost-CVaR model. Efficient frontier, which can support the system operators (SO) with the decision of optimal spinning reserve, can be obtained by solving the Cost-CVaR model. The analysis of RTS example can demonstrate the usefulness and efficiency of the model.

scholarly journals Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2249 ◽  
Author(s):  
Emanuel Canelas ◽  
Tânia Pinto-Varela ◽  
Bartosz Sawik
Keyword(s):  
Portfolio Optimization ◽  
Electricity Market ◽  
Value At Risk ◽  
Programming Model ◽  
Spot Market ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Expected Cost ◽  
Seasonal Behavior

Electricity markets are nowadays flooded with uncertainties that rise from renewable energy applications, technological development, and fossil fuel prices fluctuation, among others. These aspects result in a lumpy electricity prices for consumers, making it necessary to come up with risk management tools to help them hedge this associated risk. In this work a portfolio optimization applied to electricity sector, is proposed. A mixed integer programming model is presented to characterize the electricity portfolio of large consumers. The energy sources available for the portfolio characterization are the day-ahead spot market, forward contracts, and self-generation. The study novelty highlights the energy portfolio characterization for players denoted as large consumers, which has been overlooked by the scientific community and, focuses on the Iberian electricity market as a real case study. A multi-objective methodology is explored, using a weighted-sum approach. The expected cost and the conditional value-at-risk (CVaR) minimization are used as objective function. Three case studies illustrate the model applicability through the characterization of how the portfolio evolves with different demand profiles and how to take advantage from seasonality characteristic in the spot market. A scenario analysis is explored to reflect the uncertainty on the price of the spot market. The expected cost and CVaR are optimized for each case study and the portfolio analysis for each risk posture is characterized. The results illustrate the advantage to reduce costs and risk if the prices seasonality is considered, triggering to an adaptive seasonal behavior, which support the decision-maker decision towards its goals.

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