Value at Risk

It seems that paying attention to social responsibility by companies can lead to a better stakeholder’s view toward the company, thereby increasing their loyalty and trust. Having the ability to obtain more financial resources in times of crisis, due to the greater loyalty of investors, will result in reducing the company risk. In contrast, being overconfident about the loyalty of individuals to the company can lead to keeping a short-term debt structure, thereby increasing the risk of obtaining financial resources. Recently, the negative impacts of petrochemical companies on the environment have made social and environmental groups focus more on this industry, and this focus has pushed companies into involving in more social activities. Considering the potential impact of CSR on the company’s risk, this study examines the relationship between corporate social responsibility and value at risk in petrochemical industry using a sample of 27 companies listed on the Tehran Stock Exchange during the 2010-2017 period. Eviews 10 is used for computing and analyzing the data, and the generalized auto regressive conditional heteroskedasticity (GARCH) model is employed to estimate value at risk. The results indicate a negative and statistically significant relationship between corporate social responsibilities and company value at risk. <strong> </strong>  

This paper concentrates on the modelling of optimal stock portfolio selection based on Risk Assessment and Behavioral Financial Approach Mental Accounting and 28 expert’s opinion. In this approach developing the model approved by the opinion of academic and practical experts using quantitative and qualitative methods.  Using quarterly return data of industrial indices for ten years in form of eight training and two test years indicates that the performance of DMSS and MVO based portfolios is equal however by regarding the value at risk and liquidity constraints in modeling, DMSS based portfolios perform higher than MVO portfolios.

This paper presents an optimal portfolio selection approach based on value at risk (VaR), conditional value at risk (CVaR), worst-case value at risk (WVaR) and partitioned value at risk (PVaR) measures as well as calculating these risk measures. Mathematical solution methods for solving these optimization problems are inadequate and very complex for a portfolio with high number of assets. For these reasons, a combination of particle swarm optimization (PSO) and genetic algorithm (GA) is used to determine optimized weights of assets. Stocks’ Optimized weight results show that proposed algorithm gives more accurate outcomes in comparison with GA algorithm. According to back-testing analysis, PVaR and WVaR overestimate risk value while VaR and CVaR give a rather accurate estimation. A set of companies in Tehran Stock Exchange are considered as a case study for empirical analysis. JEL Classification: G10, G11, G19

One of the most important methods employed to measure the market risk is value at risk calculation method. In this study, the value at risk of banks listed on the Tehran Stock Exchange and Over-the-counter (OTC) are calculated using parametric model, Monte Carlo simulation, historical simulation and Two-Sided Power (TSP) Distribution. The sample includes all listed banks in Iran. The results showed that the value at risk estimated by TSP and historical models is more accurate than the VaR estimated by Monte Carlo and GARCH models. TSP model and then historical model are more accurate than the other ones. Moreover, GARCH is the least accurate model. So far, no research has been conducted to investigate all four models of value at risk assessment. JEL Classification: E5, E58, J21

This study examines the criterion of value at risk from another perspective and presents a new type of mean-value at Risk model. To solve the portfolio optimization problem in Tehran Stock Exchange, we use NSGA II, MOACO, and MOABC algorithms and then compare the mean-pVaR model with the mean-SV model. Given that, finding the best answer is very important in meta-heuristic methods, we use the concept of dominance in the discussion of multi-objective optimization to find the best answers and show that, at low iterations, the performance of the NSGA II algorithm is better than the MOABC and MOACO algorithms in solving the portfolio optimization problem. As the iteration increases, the performance of the algorithms improves, but the rate of improvement is not the same, in a way, the performance of the MOABC algorithm is better than that of the NSGA II and MOACO algorithms. Then, to compare the performance of the “mean-percentage of Value at Risk” model and the “mean-semi variance” model, we examine both models in the standard mean-variance model and show that the mean-pVaR model, compared to the mean-SV model, Has better performance in stock portfolio optimization.

Price return and P/E are two interesting factors for a lot of investors; The Bohmian quantum mechanics used referring to the time correlation of return and P/E of the stock market under consideration. In this study, we extend the quantum potential concept to determine the behaviour of P/E and also price return in two different industry of Tehran stock market during a time interval of April. 2008 to march 2019. The obtained results show that the quantum potential behaves in the same manner for P/E and price return, also confines the variations of the P/E and price return into a specific domain. Furthermore, a joint quantum potential as a function of return and P/E is derived by the probability distribution function (PDF) constructed by the real data of a given market. It serves as a suitable instrument to investigate the relationship between these variables. The resultant PDF and the corresponding joint quantum potential illustrate that where we have light points in joint quantum potential chart, the probability of those amount of P/E and price return are more than other points. In addition, because of the rectangular shape of the joint quantum potential chart we can say that these two variables behave as two independent variables in the Market.