Conditional Value at Risk

Open Access Article

1 - Portfolio Optimization Based on Cross Efficiencies By Linear Model of Conditional Value at Risk Minimization
K. Yakideh M.H . Gholizadeh M. Kazmi

Markowitz model is the first modern formulation of portfolio optimization problem. Relyingon historical return of stocks as basic information and using variance as a risk measure aretow drawbacks of this model. Since Markowitz model has been presented, many effortshave More

Markowitz model is the first modern formulation of portfolio optimization problem. Relyingon historical return of stocks as basic information and using variance as a risk measure aretow drawbacks of this model. Since Markowitz model has been presented, many effortshave been done to remove theses drawbacks. On one hand several better risk measures havebeen introduced and proper models have been developed to detect optimized portfolio basedon them. On the other hand the idea of using generated data by data envelopment analysisinstead of historical return of stocks has been presented.In this paper, both improvements are collected by applying a conditional value at riskminimization linear model on cross efficiencies, generated by a proper model of dataenvelopment analysis model, called range adjusted model. Performance of proposedmethod, market portfolio as a benchmark and method of applying Markowitz model oncross efficiencies calculated according to sharp ratio using next year real return of eachportfolio during years of study. Results support proper performance of proposed method. Manuscript profile

Open Access Article

2 - Three steps method for portfolio optimization by using Conditional Value at Risk measure
S. Navidi sh. Banihashemi M. Sanei

Comprehensive methods must be used for portfolio optimization. For this purpose, financial data of stock companies, inputs and outputs variable, the risk measure and investor’s preferences must be considered. By considering these items, we propose a method for por More

Comprehensive methods must be used for portfolio optimization. For this purpose, financial data of stock companies, inputs and outputs variable, the risk measure and investor’s preferences must be considered. By considering these items, we propose a method for portfolio optimization. In this paper, we used financial data of companies for screening the stock companies. We used Conditional Value at Risk (CVaR) as a risk measure, because of its advantages. Data Envelopment Analysis (DEA) can be used to calculate the efficiency of stock companies. Conventional DEA models assume non-negative data. However, many of these data take the negative value, therefore we propose the MeanSharp- CVaR (MSh CV) model and the Multi Objective MeanSharp- CVaR (MOMSh CV) model base on Range Directional Measure (RDM) that can take positive and negative values. By using Multi Objective Decision Making (MODM) model, investors can allocate their capital to the stocks of portfolio as they like. Finally, a numerical example of the purposed method is applied to Iran’s financial market. Manuscript profile

Open Access Article

3 - Estimating Conditional VaR Using Symmetric and Non-Symmetric Autoregressive Models in Old and Oil Markets
Saeid Fallahpour Fatemeh Rezvani Mohammadreza Rahimi

Price volatility on gold and oil market is the top news all the time. Global economy isaffected by those markets volatility. Because of the domestic investor tendency in goldmarket and feasibility of investing on oil by Energy Exchange in Iran, in this paper wefocus on More

Price volatility on gold and oil market is the top news all the time. Global economy isaffected by those markets volatility. Because of the domestic investor tendency in goldmarket and feasibility of investing on oil by Energy Exchange in Iran, in this paper wefocus on the volatility of gold and oil return. The implemented method is one-day aheadout of sample forecast by the conditional value at risk.The goal of this paper is to answer which of the models; GARCH, ECHARCH, andTARCH is best at forecasting the CVaR for gold and oil return. We estimate the value byassuming normal and t-student distribution.The results show that the TGARH(1,1) model specifications are good option forforecasting the CVaR in oil market by t-student distribution Manuscript profile

Open Access Article

4 - A framework for measuring and predicting system risk with the conditional value at risk approach
Ja'far Baba Jani M. Taghi Taghavi Fard Amin Ghazali

In recent years with the increasing integration and innovation in financial markets, concerns about the overall stability of the financial system has increased and the concept of systemic risk has become more important. systemic risk is the risk imposed by interlinkages More

In recent years with the increasing integration and innovation in financial markets, concerns about the overall stability of the financial system has increased and the concept of systemic risk has become more important. systemic risk is the risk imposed by interlinkages and interdependencies in a system or market, where the failure of a single entity or cluster of entities can cause a crisis in the entire system or market. In this study, we presented a framework for measuring and predicting systemic risk in the capital market of Iran using conditional value at risk approach (CoVaR). On this basis, ΔCoVaR as a measure of systematic risk using quintile regression based on a set of state variables that indicates changes in the distribution of asset returns has been estimated. As well as to enhance the accuracy of the estimate, the research variables modeled after the conditional autoregressive value at risk model (CAViaR) has been developed and some Lagged firm specific characteristic has also been added. In order to test the validity of the model back testing methods have been used. On the other hand, The potential for systemic risk increases when volatility decreases (volatility paradox). In this study, we try to predict systemic risk by take advantage of the panel structure of the data and the relationship between ΔCoVaR and firm-specific variables that are available in certain sections. Manuscript profile

Open Access Article

5 - Fuzzy Mean-CVaR Portfolio Selection Based on Credibility Theory
S. Babak Ebrahimi Amirsina Jirofti Matin Abdi

This paper develops a fuzzy portfolio selection problem that minimizes conditional value-at-risk (CVaR) and estimates CVaR by fuzzy credibility theory and also calculates expected return by fuzzy credibility mean. Using fuzzy techniques makes the model more precise and More

This paper develops a fuzzy portfolio selection problem that minimizes conditional value-at-risk (CVaR) and estimates CVaR by fuzzy credibility theory and also calculates expected return by fuzzy credibility mean. Using fuzzy techniques makes the model more precise and accurate due to uncertainty of financial data. The use of CVaR helps investors make better decisions because it indicates the size of loss. This study considers some constraints for model including liquidity, cardinality, minimum and maximum investment proportion. The liquidity constraint is measured by turnover of each asset as a trapezoidal fuzzy number. The liquidity constraint converts to a linear constraint by using fuzzy credibility theory. Using CVaR as a risk measurement and efficient constraints makes the model appropriate and adequate for portfolio selection. Finally, a numerical example is provided by 10 stocks chosen from Tehran Stock Exchange Market in 2015 and it shows the effectiveness and applicability of the proposed model Manuscript profile

Open Access Article

6 - Using intelligent methods in Solving Constrained Portfolio in Tehran Stock Exchange
Esmat Jamshidi Eyni Hamid Khaloozadeh

The optimal portfolio selection problem to find an optimal way to allocate a fixed amount of capital to a set of available asset swhich aims to maximize expected returns and minimize risk at the same time, to take place. In this Study is shown that an investor with n ri More

The optimal portfolio selection problem to find an optimal way to allocate a fixed amount of capital to a set of available asset swhich aims to maximize expected returns and minimize risk at the same time, to take place. In this Study is shown that an investor with n risky share, how to reach certain profits with minimal risk. Such a portfolio, efficient portfolio is called. For this purpose, the study of evolutionary algorithms, Genetic Algorithm, Imperialist Competitive Algorithm and Particle Swarm Optimization algorithm, also with regard to the basic constraints on the investment, we use these practical methods to solve the portfolio optimization problem. Practical results for the portfolio optimization problem in the Tehran Stock Exchange, of the30 company' sactivein the industry with the selection of20companies along with their validation, is obtained. Aims to help investors better and more practical to select different stocks and thus is an effective investment. Manuscript profile

Open Access Article

7 - Using intelligent methods in Solving Constrained Portfolio in Tehran Stock Exchange
Esmat Jamshdi Eyni Hamid Khaloozadeh

The optimal portfolio selection problem to find an optimal way to allocate a fixed amount of capital to a set of available assets which aims to maximize expected returns and minimize risk at the same time, to take place. In this Study is shown that an investor with n ri More

The optimal portfolio selection problem to find an optimal way to allocate a fixed amount of capital to a set of available assets which aims to maximize expected returns and minimize risk at the same time, to take place. In this Study is shown that an investor with n risky share, how to reach certain profits with minimal risk. Such a portfolio, efficient portfolio is called. For this purpose, the study of evolutionary algorithms, Genetic Algorithm, Imperialist Competitive Algorithm and Particle Swarm Optimization algorithm, also with regard to the basic constraints on the investment, we use these practical methods to solve the portfolio optimization problem. Practical results for the portfolio optimization problem in the Tehran Stock Exchange, of the30 company's active in the industry with the selection of20companies along with their validation, is obtained. Aims to help investors better and more practical to select different stocks and thus is an effective investment. Manuscript profile

Open Access Article

8 - Comparing the Frechet Distribution and the Generalized Pareto Distribution in Estimating Value at Risk and Conditional Value at Risk in Tehran Stock Exchange
Azadeh Meharani Ali Najafi moghadam Ali Baghani

Selecting the most accurate method of risk measurement is the main challenge in risk estimation. This study aims to measure value at risk (VaR) and conditional value at risk (CoVar) in Tehran Stock Exchange (TSE) using the Frechet distribution (FD) and the generalized P More

Selecting the most accurate method of risk measurement is the main challenge in risk estimation. This study aims to measure value at risk (VaR) and conditional value at risk (CoVar) in Tehran Stock Exchange (TSE) using the Frechet distribution (FD) and the generalized Pareto distribution (GPD). It used the data from 21 and 63-day time series of TEPIX, free-float, and the indices of the top 50 TSE companies between 2012-3-20 and 2020-3-19. It used COPIC post-test and Christoffersen’s conditional coverage test for models statistical confirmation. It applied Lopez and Blanco-Ihle’s second loss functions for model comparison. The CoVaR models were ranked by two loss functions, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results indicated according to the statistics measured in Lopez’s second loss function, GPD performed better than FD in measuring VaR of TEPIX and the return index of the selected 50 companies, however, FD performed better for the free-float index. Blanco-Ihle’s loss function results contradicted the ones derived from Lopez’s second loss function. MAE and RMSE results indicated FD is better in estimating CoVaR.      Manuscript profile

Open Access Article

9 - Comparative Analysis of Stock Portfolio Optimization in Fireworks and Genetic Algorithms Using Conditional Value at Risk
Ali Asghar Shahriari saeed Daei-Karimzadeh Reza Behmanesh

10.30495/jfksa.2021.19255

Devaluation of assets in the future is one of the most important investment concerns that has led investors to choose the set of assets that have the lowest risk and highest return. The present study deals with the problem of stock portfolio optimization according to th More

Devaluation of assets in the future is one of the most important investment concerns that has led investors to choose the set of assets that have the lowest risk and highest return. The present study deals with the problem of stock portfolio optimization according to the Conditional Value at Risk based on the new and intelligent fireworks algorithm and compares it with genetic algorithm with the historical simulation method using MATLAB software. The parameters of meta-heuristic algorithms were adjusted by Taguchi method using MINITAB software. Not suspended, used. For reliability of the study, generalized Dickey-Fuller test and Phillips-Prone test were used. To evaluate the accuracy of the Conditional Value at Risk model, the kupiec proportion of failure test, Christoffersen independence test and Conditional coverage test are used.  A comparison was also made between the models by Lopez test. Findings showed that at %95 and %99  confidence levels, the conditional risk value model using the fireworks algorithm has a suitable and reliable validity for measuring market risk and optimizing the stock portfolio. Manuscript profile

Open Access Article

10 - Presenting of nonlinear hybrid model based on Extreme Value Theory for forecasting the Conditional Value at Risk (CVaR)
Ehsan Mohammadian Amiri Ehan Atefi Seyed Babak Ebrahimi

The political and economic instability in recent years and followed by rapid changes in the realm of financial markets, has increased the risk of most financial institutions. So that risk managers at these institutions are worried about the decline in their asset value More

The political and economic instability in recent years and followed by rapid changes in the realm of financial markets, has increased the risk of most financial institutions. So that risk managers at these institutions are worried about the decline in their asset value over the coming days. In recent studies, generally the Conditional Value at Risk is used to measure and forecast the risks existing in financial markets. Therefore, in this research, it has been attempted to introduce, calculate and implement a nonlinear hybrid model for forecasting the Conditional Value at Risk. For this purpose, the new hybrid model based on the Extreme Value Theory and the Holt-Winters exponential smoothing (HWES-EVT) that, in addition to dynamics, cluster characteristics and broad data sequence, also takes into account the forecast Conditional Value at Risk of the industry and Tehran Stock Exchange Indices. For evaluating the accuracy the performance of proposed hybrid model, this modek is compared with the GARCH-EVT model. The results of backtesting show that the proposed hybrid approach provides a more accurate answer to the forecasting of Conditional Value at Risk for these indicators Indices. Manuscript profile

Open Access Article

11 - Financial Risk Modeling with Markova Chain
Fraydoon Rahnamay Roodposhti Hamid Vaezi Ashtiani Bahman Esmaeili

Manuscript profile

Open Access Article

12 - Robust Portfolio Optimization with risk measure CVAR under MGH distribution in DEA models
Morteza Robatjazi Shokoofeh Banihashemi Navideh Modarresi

Manuscript profile

Open Access Article

13 - Systemic risk assessment models: a better approach in Iranian financial institutions
majid noroozi Hamid Reza Kordlouei Reza gholamijamkarani hossein Jahangirnia

ریسک سیستمیک به خطر شکست سیستم مالی یا شکست کل بازار اطلاق می‌شود. این ریسک می‌تواند از بی‌ثباتی یا بحران در مؤسسات مالی نشأت بگیرد و در اثر سرایت به کل نظام مالی انتقال یابد. به‌عبارتی ریسک سیستمیک به میزان به‌ هم‌پیوستگی در یک سیستم مالی اشاره دارد جایی‌که شکست در یک More

ریسک سیستمیک به خطر شکست سیستم مالی یا شکست کل بازار اطلاق می‌شود. این ریسک می‌تواند از بی‌ثباتی یا بحران در مؤسسات مالی نشأت بگیرد و در اثر سرایت به کل نظام مالی انتقال یابد. به‌عبارتی ریسک سیستمیک به میزان به‌ هم‌پیوستگی در یک سیستم مالی اشاره دارد جایی‌که شکست در یک نهاد مالی می‌تواند به بحران کل سیستم منجر شود. این تحقیق با توجه به رویکردهای مختلف جهت اندازه&rlm;گیری ریسک سیستمیک به دنبال انتخاب رویکرد بهتر برای اندازه&rlm;گیری ریسک سیستمیک است. انتخاب رویکرد بهتر با توجه به خطای پیش&rlm;بینی ارائه شده توسط هریک از مدل&rlm;ها است. مدل‌های به کار گرفته شده اعم از مدل&rlm;های گارچی چند متغیره، مدل ارائه شده توسط برانلس و انگل به نام VCT، مدل&rlm;های عاملی&rlm;، مدل&rlm;های آماری دومتغیره است. نتایج تحقیق نشان می&rlm;دهد که مدل پیشنهادی برانلس و انگل (VCT) خطای کمتری را نسبت به سایر مدل&rlm;ها از خود نشان داده است. Manuscript profile

Open Access Article

14 - Relationship between risk and risk - aversion utility Based on Multi-Period prospect theory
RAZIEH Ahmadi Adel Azar gholam reza zomorodianS

The purpose of this study is to investigate the effect of loss-aversion behavior on multi-period investment decisions. For this purpose, two models of portfolio optimization have been designed. Instead of a single-period portfolio model, a three-period model has been us More

The purpose of this study is to investigate the effect of loss-aversion behavior on multi-period investment decisions. For this purpose, two models of portfolio optimization have been designed. Instead of a single-period portfolio model, a three-period model has been used. In order to bring the optimization models closer to the real world, in addition to the CVaR as one of the main constraints, the transaction cost and the lower bound and upper bound investment in each asset are also considered. two models of loss aversion and mean-CVaR optimization were solved using PSO algorithm. Also, some important criteria such as initial loss aversion coefficient and reference point are used to test the robustness of model. The results based on the optimal wealth and Sharp ratio showed that loss-averse investors tend to concentrate most of their wealth and have a better performance than rational investors. The impact of CVaR on investment performance was identified. When the market is falling, investors with higher risk aversion avoid extreme losses and obtain more gains. Manuscript profile

Open Access Article

15 - Investment Portfolio Optimization of Insurance Companies with Copulas and Extreme Value Approach
arash goodarzi reza Tehrani Ali souri

This study determines the optimal investment portfolio of insurance companies by considering underwriting activities. investment decisions in insurance companies are affected by underwriting activities. In this paper, the investment optimization problem of insurers is m More

This study determines the optimal investment portfolio of insurance companies by considering underwriting activities. investment decisions in insurance companies are affected by underwriting activities. In this paper, the investment optimization problem of insurers is modeled using the copula-based conditional risk value, taking into account the results of insurance activities. Also, since the emphasis is on tails of distribution, the probability distribution of variables in tails is estimated using Pareto distribution and in other parts of the distribution using the Empirical probability distribution. Data collected on monthly basis covers two periods in-sample, between 2006 to 2015 and out-of-sample, between 2016 to 2019.The findings show that the optimum portfolio includes eighty percent of risky assets (stock and real estate) and only twenty percent of risk-free assets (bank deposits) and it is outside the legal constraints set by Central Insurance Therefore, legal constraints prevent insurance companies from the optimal selection of investment portfolio. Also, the comparison of out-of-sample performance with in-sample performance of portfolios shows that portfolios based on copula functions have better and more robust performance than traditional models. Manuscript profile

Open Access Article

16 - Application of Copula and Simulated Returns in the Portfolio Optimization with Conditional Value-at-Risk (CVaR) in Tehran Stock Exchange (TSE)
Esmaeil Lalegani Mostafa Zehtabian

Several studies have confirmed with using criteria appropriate to the structure and characteristics of the data, the performance of the models significantly improved. The Copula function is one of the models in determining the relationship between variables has attracte More

Several studies have confirmed with using criteria appropriate to the structure and characteristics of the data, the performance of the models significantly improved. The Copula function is one of the models in determining the relationship between variables has attracted a lot of attention. In this study, we investigated the portfolio of TSE industry indexes optimized to minimize the Conditional Value at Risk, simulated data based on Copula function and generalized Pareto distribution as input. The statistical analyzes implies portfolio performance improves significantly. Since the Copula functions are diverse, Compare the impact of any one risk reduction is recommended.     Manuscript profile

Open Access Article

17 - An Investigation of methods to reduce transaction costs in Tehran Stock Exchange
Romina Atrchi Shahin Ramtinnia

Among 37 methods to reduce transaction costs introduced by Cha (2007), we recursively choose the best method for next period's investment in each of three portfolio strategies: Mean-Variance Optimization, Mean-CVaR Optimization, and the equally-weighted market. We ident More

Among 37 methods to reduce transaction costs introduced by Cha (2007), we recursively choose the best method for next period's investment in each of three portfolio strategies: Mean-Variance Optimization, Mean-CVaR Optimization, and the equally-weighted market. We identify a few of the best methods and offer a framework by which additional methods can be considered. Within our framework, the best methods recapture a substantial amount of wealth and significantly improve risk-adjusted performance, both economically and statistically. We used prices and returns of the 10 most active firms of Tehran Stock Exchange market, from 1391 to 1394 on a monthly basis in this research. The transactions costs reduction methods will be applied on them and the best methods will be identified. Also, a framework will be offered for comparison and investigation of new methods.     Manuscript profile

Open Access Article

18 - Algorithmic Trading System for future contract of gold coin based on intra-day data
Mohammad Ali Rastegar Amin Sedaghatipour

Today, with the prevalence of online trading and algorithmic trading, it is required that the trading data of financial markets be analyzed faster and become profitable decision. The purpose of this paper is to develop an automated and algorithmic trading system on gold More

Today, with the prevalence of online trading and algorithmic trading, it is required that the trading data of financial markets be analyzed faster and become profitable decision. The purpose of this paper is to develop an automated and algorithmic trading system on gold coin future contracts in Iran Mercantile Exchange. According to the suitableness of technical analysis for two-sided markets (long and short position), 8 technical tool signals has been used for trading system. In order to develop the trading system, MOPSO algorithm is used with the aim of optimizing the efficiency function and Conditional Value at Risk (CVaR). Besides for completing the risk management system, optimized take profit and stop loss has been specified for future contract. The results show that the designed trading system has a more favorable ratio of return to risk than other competitor strategies such as buy & hold and sell & hold. Also the time frame of 30 minutes seems appropriate for designing a trading system based on gold futures contract.   Manuscript profile

Open Access Article

19 - Selection of optimal portfolio by using improved Non-Dominated Sorting Genetic Algorithm and Evolutionary Algorithm Strength Pareto By taking risk on the basis of conditional value at risk
Mojtaba Moradi Maryam Ghavidel

Portfolio selection problem is one of the most important economic issues. The right combination of stock or other asset portfolio is that an investor pays to buy it. Selection of an optimal portfolio is based on the principle that the investor decides to accept one or s More

Portfolio selection problem is one of the most important economic issues. The right combination of stock or other asset portfolio is that an investor pays to buy it. Selection of an optimal portfolio is based on the principle that the investor decides to accept one or several investments among different investment depending on the tolerance of risk and expected a reasonable amount of stock returns. In this study, improved Non-Dominated Sorting multi-objective genetic algorithms and Evolutionary Algorithm Strength Pareto are used to create an optimum portfolio. These algorithms are improved version of their previous versions and have a better solution than its previous versions. The value of the portfolio and its risk, as optimization purposes and conditional value at risk as the basis risk, have been used. Two applied conditions consider to Portfolio and shown that the Evolutionary Algorithm Strength Pareto‌ has better results than the Non-Dominated Sorting Genetic Algorithm II.       Manuscript profile

Open Access Article

20 - Estimating Conditional Value at Risk (CVaR) with consideration the robust of the measure based on robust Cipra method
Ehsan Mohammadian Amiri Ehsan Mohammadian Amiri Seyed Babak Ebrahimi

Since the atmosphere of financial markets is uncertain and ambiguous, Conditional Value at Risk measurement has been of great importance in recent years for financial companies and micro and macro investors. In this paper, we estimate the CVaR of the Tehran Stock Exchan More

Since the atmosphere of financial markets is uncertain and ambiguous, Conditional Value at Risk measurement has been of great importance in recent years for financial companies and micro and macro investors. In this paper, we estimate the CVaR of the Tehran Stock Exchange Index for distribution of the Trial Student at confidence levels of 95%  and 99%  based on the Cipra method, which is proposed as a new approach for the estimation of the CVaR. In order to evaluate the performance of this approach, the comparison between the said approach and the conventional methods of GARCH, EGARCH and TGARCH was performed using four backtesting of unconditional coverage  test, conditional coverage  test, joint test and Lopez loss function test. The results show that robust Cipra method has a better and more reliable performance than the other methods in estimating the CVaR.   Manuscript profile

Open Access Article

21 - Expression and design a model to forecast the exchange rate shocks and stress testing of the currency in Iran
Abdollah Rajabi Khanghah Hashem Nikomaram Mehdi Taghavi Fereydoon Rahnamay Roodposhti Mirfiyaz Fallah Shams

This paper attempts to Expression  and design a model to forecast  the exchange rate shocks and stress testing of the currency in Iran . In other  words, what factors influence the currency shock and is there the shock predictability in the currency marke More

This paper attempts to Expression  and design a model to forecast  the exchange rate shocks and stress testing of the currency in Iran . In other  words, what factors influence the currency shock and is there the shock predictability in the currency market. On the other hand, are there in critical condition (shock) the ability to predict currency risk through stress tests? The research method is descriptive-analytic and data collection library using econometric regression model using econometric software Eviews is carried out. The research is method in this study (time series modeling) . The results suggest that based on estimations made clear  that the exchange rate shock  predictable feature of the model is capable MGARCH. . In other words, using multivariate GARCH model and Conditional value at risk predictability of the exchange rate shock and influence the variable of  Macroeconomic variables has been clarified. Finally, using the Back Testing the validity and effectiveness model was estimated and validation tests with stress tests to estimate the shock has been in critical condition. Manuscript profile

Open Access Article

22 - تاثیر شاخص متا مالمکوئیست روی بهینه سازی سبد دارایی
زهره طائب شکوفه بنی هاشمی

10.30495/ijim.2022.61818.1532

از آنجائیکه تغییر ارزش در معرض خطر شرطی (CVaR) در سطوح مختلف اطمینان برای بهینه سازی سبد بسیار موثر است، شاخص متا مالمکوئیست (MMI) دراین پژوهش استفاده شده است. برای این هدف، مدلهای میانگین- ارزش در معرض خطر شرطی با شاخص متا مالمکوئیست در حضور داده منفی معرفی گردیده است. More

از آنجائیکه تغییر ارزش در معرض خطر شرطی (CVaR) در سطوح مختلف اطمینان برای بهینه سازی سبد بسیار موثر است، شاخص متا مالمکوئیست (MMI) دراین پژوهش استفاده شده است. برای این هدف، مدلهای میانگین- ارزش در معرض خطر شرطی با شاخص متا مالمکوئیست در حضور داده منفی معرفی گردیده است. مشابه تئوری مارکوویتزدر چارچوب میانگین- واریانس، ارزش در معرض خطر شرطی به عنوان سنجه ریسک بکار رفته و مدلها بدون در نظر گرفتن چولگی و کشیدگی بازده مطرح شده است. در این مطالعه تعدادی داده منفی وجود دارد، بنابراین مدلهای برمبنای مدل اندازه جهت دار مبنایی (RDM) است که مقادیر مثبت و منفی را می پذیرد. در این مقاله، کارائیها در همه سطوح اطمینان در مدل های میانگین- ارزش در معرض خطر شرطی و شاخص متا مالمکوئیست روی سطوح اطمینان به عنوان دوره ها در حضور داده منفی محاسبه شده است. این روش به سرمایه گذاران کمک می کند که سبدهای سودآورشان را با شاخص متا مالمکوئیست بسازند. همچنین یک مطالعه عملی روی بازار بورس ایران انجام گرفته است. Manuscript profile

Open Access Article

23 - Using MODEA and MODM with Different Risk Measures for Portfolio Optimization
Sarah Navidi Mohsen Rostamy-Malkhalifeh Shokoofeh Banihashemi

10.22034/amfa.2019.1864620.1200

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Open Access Article

24 - The Estimation of Systematic Risk in Iranian Financial Sectors (ΔCoVaR Approach)
samad hekmati farid Ali Rezazadeh ali malek

Abstract The occurrence of last crisis has led to the consideration of systematic risk and it's transmission in theoretical and empirical point view. Hence, the main aim of this paper is to estimate and localize of systematic risk in financial sectors of Iran such as St More

Abstract The occurrence of last crisis has led to the consideration of systematic risk and it's transmission in theoretical and empirical point view. Hence, the main aim of this paper is to estimate and localize of systematic risk in financial sectors of Iran such as Stock, Insurance and Bank sectors during the period of 1995-2015.  The quintile regression econometric approach has been used for estimating the difference conditional value at risk in these sectors. The main empirical findings of post estimation indicated that there is significant difference between Stock, Insurance and Bank sectors as main financial sectors. Moreover, the results of Fridman test as a method for ordering of variable status showed that, the systematic risk of insurance is high and risk of bank is low during the period of study. So, there is significant difference between orders of financial sectors in Iran over the period of study. Manuscript profile

Open Access Article

25 - Fuzzy Portfolio Optimization Using Credibility Theory: Multi-Objective Evolutionary Optimization Algorithms
MariehAlsadat MirAboalhassani Farzad Movahedi Sobhani Emran Mohammadi

10.30495/fomj.2022.1949727.1054

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26 - Designing and explaining the dynamic model of comprehensive risk transfer of cryptocurrency in the financial markets of the world
Reza Karimi Mirfeiz Falahshams Shadi Shahverdiani Gholamreza zomorodian

10.30495/ecomag.2023.1979337.1057

The purpose of this article was to provide a dynamic and dynamic model to explain how to transfer the pervasive risk of cryptocurrencies in the world markets. In this regard, the statistical information of the cryptocurrency market index and the data of the Nasdaq, New More

The purpose of this article was to provide a dynamic and dynamic model to explain how to transfer the pervasive risk of cryptocurrencies in the world markets. In this regard, the statistical information of the cryptocurrency market index and the data of the Nasdaq, New York, Toronto, London, Frankfort, Madrid, Shanghai, Hong Kong, Tokyo, and Mumbai stock market indices were used. In this research, the data related to the cryptocurrency market and financial markets from July 2012 to July 2022 have been used. In the first part of this study, using the information of the period 2012-2022, based on the frequency of monthly data for the financial markets, the comprehensive risk criterion has been calculated using the method of value at risk, conditional interval and expected loss. In the second part, using multivariate conditional heteroscedastic variance autocorrelation method (MGARCH), the external effects related to pervasive risk related to cryptocurrency were estimated on financial markets. The obtained results indicate that there are spillover effects between financial markets and an increase in pervasive risk in each of the financial markets leads to an increase in pervasive risk in other financial markets. In the second part, using multivariate conditional heteroscedastic variance autocorrelation method (MGARCH), the external effects related to pervasive risk related to cryptocurrency were estimated on financial markets. The obtained results indicate that there are spillover effects between financial markets and an increase in pervasive risk in each of the financial markets leads to an increase in pervasive risk in other financial Manuscript profile

Open Access Article

27 - Analyzing and measuring the systemic risk between cryptocurrencies and real currencies using the value-at-risk and the marginal expected shortfall
Zohre Rahimi Gholamreza Zomorodian Azita Jahanshad Mehdi Madanchizaj

The purpose of this paper is to analyze and measure the systemic risk between the cryptocurrency and real currencies using the conditional risk exposure value approach and the expected marginal loss. In this study, statistical data of real and virtual currencies during More

The purpose of this paper is to analyze and measure the systemic risk between the cryptocurrency and real currencies using the conditional risk exposure value approach and the expected marginal loss. In this study, statistical data of real and virtual currencies during the years 2015-2021 have been used. For this purpose, systemic risk indices have been calculated using CoVaR and MES indices and then the correlation between systemic risk of currencies has been evaluated. In this study, the statistical data of the currencies of the exchange rate of the pound to the dollar, the exchange rate of the yuan to the dollar, the exchange rate of the lira to the dollar, the exchange rate of the euro to the dollar, bitcoin, atrium, ripple, litcoin and atrium based on daily price returns Currencies and real currencies were used. The results showed that there was a correlation between systemic risk indicators for the studied currencies and virtual currencies had a lower systemic risk index than real currencies. The purpose of this paper is to analyze and measure the systemic risk between the cryptocurrency and real currencies using the conditional risk exposure value approach and the expected marginal loss. In this study, statistical data of real and virtual currencies during the years 2015-2021 have been used. For this purpose, systemic risk indices have been calculated using CoVaR and MES indices and then the correlation between systemic risk of currencies has been evaluated. Manuscript profile

Open Access Article

28 - Examining the Efficiency Models, Genetic Algorithm under MSV Risk and Particle Swarm Optimization Algorithm under CVAR Risk Criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange
Dariush Adinevand Ebrahim Ali Razini Mahmoud Khodam Fereydoun Ohadi Elham Elsadat Hashemizadeh

10.30495/fed.2023.707996

Abstract Choosing the optimal stock portfolio is one of the main goals of capital management. Today, There are several tools and techniques for measuring portfolio risk and selecting the optimal stock portfolio. In this article, using data of 15 shares selected by purp More

Abstract Choosing the optimal stock portfolio is one of the main goals of capital management. Today, There are several tools and techniques for measuring portfolio risk and selecting the optimal stock portfolio. In this article, using data of 15 shares selected by purposeful sampling method from the top companies of Tehran Stock Exchange Organization including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days and then using MATLAB software models The Metaheuristic Optimization of the Genetic Algorithm under the MSV Risk Criterion and the Particle Swarm Algorithm under the CVaR risk Criterion are Compared. The results show that the genetic algorithm model under MSV risk criterion is more efficient and less risky, therefore the genetic algorithm model under MSV risk criterion is more efficient than the particle swarm algorithm model under CVaR risk criterion. Manuscript profile

Open Access Article

29 - Stock portfolio optimization using Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) under Conditional Value at Risk (CVaR)
Arezou Karimi sara goodarzi dahrizi

The choice of stock portfolio is a special issue in the field of investment. Given the wide range of options in the stock market, one of the major concerns of investment groups is the optimal allocation of assets. Therefore, most of these collections use portfolio selec More

The choice of stock portfolio is a special issue in the field of investment. Given the wide range of options in the stock market, one of the major concerns of investment groups is the optimal allocation of assets. Therefore, most of these collections use portfolio selection models. The conditional value at Risk, which is one of the models of portfolio selection, follows the Quadratic Programming. Given that Quadratic Programming requires extensive computations, the use of metaheuristic algorithms in solving these problems increases the speed and accuracy of computations. The aim of this study is to minimize the Conditional Value at Risk by using two algorithms of Imperialist Competitive Algorithm and Particle Swarm Optimization. Therefore, using 800 days of data from 12 companies listed on the Tehran Stock Exchange in the period of 2/5/92 to 1/28/98, portfolio has been formed, and the weight of each stock in the optimal portfolio and the risk and return of the portfolio has been calculated using MATLAB2018 software. Then, using SPSS software, the average difference between risk and return of the two algorithms was tested.The results showed that the risk and return of the two algorithms were not statistically significant,. Manuscript profile

Open Access Article

30 - Stock Portfolio Optimization with MAD and CVaR Criteria by Comparing Classical and Metaheuristic Methods
Mohammad reza Haddadi Younes Nademi Fateme Tafi

Choosing the optimal stock portfolio is one of the main goals of capital management. There are several criteria for choosing the optimal portfolio. In this paper, using data of 10 stocks which randomly selected from the Tehran Stock Exchange including Vanovin, Vakharazm More

Choosing the optimal stock portfolio is one of the main goals of capital management. There are several criteria for choosing the optimal portfolio. In this paper, using data of 10 stocks which randomly selected from the Tehran Stock Exchange including Vanovin, Vakharazm, Seghrab, Shepna, Vapetro, Dana, Khasapa, Shekarbon, Shadous and Khahen, first the returns of these stocks are calculated and their portfolio risk is calculated using the models of absolute deviation risk and risk value, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the optimization with the risk criterion of absolute deviation, the Dana has the highest weight and in the optimization with the value at risk criterion, the stocks of Segharb, Shepna and Shekarbon have the most weight. In the following, the deviation-absolute risk model and value at risk model of metaheuristic method are compared. The results show that the NSGA2 model of metaheuristic method compared to the classical method in solving portfolio optimization problem showed more risk in both MAD and CVaR criteria and therefore it is a better method to solve such portfolio optimization problems. Manuscript profile

Open Access Article

31 - Stock portfolio optimization using multi-objective genetic algorithm (NSGA II) and maximum Sharp ratio
Arezou Karimi

One of the most important issues in finance is how to choose an investment portfolio. Activists in this field are seeking to select a portfolio that controls risk with high return. Due to the increasing limitations of the capital market, the efficiency of classical meth More

One of the most important issues in finance is how to choose an investment portfolio. Activists in this field are seeking to select a portfolio that controls risk with high return. Due to the increasing limitations of the capital market, the efficiency of classical methods has been discussed. Hence, researchers have turned their attention to metaheuristic algorithms. The aim of this study is to determine the optimal portfolio of pharmaceutical companies accepted in the Tehran Stock Exchange by two methods of multi-objective genetic algorithm (NSGA-II) and maximum Sharp ratio. In this study, the multi-objective genetic algorithm (NSGA-II) is under Conditional Value at Risk criterion. Also, the data of 13 companies in the period of 90-97 were used to form the portfolio. The results show that in the multi-objective genetic algorithm (NSGA-II) method, the stock with the lowest Value at Risk gains the most weight in the optimal portfolio. Also, the optimized portfolio by multi-objective genetic algorithm is more return and at the same time less risky. Manuscript profile

Open Access Article

32 - Estimation value at risk (VAR) and conditional value at risk (CoVaR) at Tehran Stock Exchange by approach to using Fréchet distribution (FD)
Azadeh Meharani Ali Najafi moghadam Ali baghani

Risk estimation cannot deliver appropriate reliable predictions by focusing on one or two models and considering the irrelevant factors. This study aims to estimate the value at risk (VAR) and the conditional value at risk (CoVaR) in the Tehran Stock Exchange using Fr&e More

Risk estimation cannot deliver appropriate reliable predictions by focusing on one or two models and considering the irrelevant factors. This study aims to estimate the value at risk (VAR) and the conditional value at risk (CoVaR) in the Tehran Stock Exchange using Fréchet distribution (FD). In doing so, generalized extreme value (GEV) is used with the help of Friche distribution approach. In this study, the return of 21-day and 63-day data of the time series of the total index, free-float-stock index, and index of 50 active companies of Tehran Stock Exchange during 01/01/2012 to 12/29/1398 have been used. The obtained results through the estimation of three parameters of GEV, including location, scale, and shape, have shown that the parameter of distribution shape within all 21 and 63-day periods of each indicator is positive, and the distribution for indexes studied follows FD as the second type of generalized distribution of GEV. By using the same pattern, the measurement of CoVaR and VaR has presented that it is possible to estimate CoVar and VaR through the use of FD and CoVaR is higher than VaR within the whole range of alpha. Manuscript profile

Open Access Article

33 - Examining the Efficiency Models, Conditional Value at Risk and Mean Absolute Deviation and Particle Swarm Optimization Algorithm under CVAR and MAD risk criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange
dariuosh adinehvand Ebrahim ali Razini Rahmani Mahmod khoddam Fereydon Ohadi alhamsadat hashemizadeh

Choosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including More

Choosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days. Then and their portfolio risk is calculated using the models of absolute deviation risk and conditional value at risk, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the following, the deviation - absolute risk model and conditional value at risk model of metaheuristic method using MATLAB (R2019) software are compared. The results show that the PSO model of metaheuristic method compared to the classical method in solving portfolio optimization problem showed more return in PSO-MAD criteria and therefore it is a better method to solve such portfolio optimization problems. Manuscript profile

Open Access Article

34 - Multi-Asset Portfolio Optimization based on Conditional Value at Risk using Artificial Bee Colony Algorithm
Somayeh Mousavi Abbasali Jafari-Nodoushan Marzieh Kazemi-Rashnani Mahsa Mohammadtaheri

Multi-asset portfolio management and optimization have always been of interest to investors. Due to the inflation in Iran market, different performance of the asset classes in different market conditions and the ability to earn more profit along with less risk by divers More

Multi-asset portfolio management and optimization have always been of interest to investors. Due to the inflation in Iran market, different performance of the asset classes in different market conditions and the ability to earn more profit along with less risk by diversifying the types of assets, it seems necessary to select a portfolio consisting of stocks, foreign currency and commodities. In this paper, assets of the above categories, including Emami coins, American dollar, and 11 sector indices, are considered in the portfolio composition. Due to the importance of the risk measure in multi-asset portfolio optimization, a model with conditional value at risk, the historical simulation approach has been extended and its efficiency has been compared with the mean-variance model. The models have been solved using the artificial bee colony and imperialist competitive algorithms. The daily asset prices in the period 2013 to 2020 have been used to evaluate the models in Iran market. Results show that the mean-conditional value at risk model performs better than the mean-variance in the training and testing periods. Furthermore, optimized portfolios with the artificial bee colony algorithm could outperform the imperialist competitive algorithm based on the Sharpe ratio, conditional Sharpe ratio, and return on risk. Manuscript profile

Open Access Article

35 - The evaluation of Systemic Risk in the Iran Banking System by Delta Conditional Value at Risk ( CoVaR) Criterion
asadollah farzinvash naser elahi javad gilanipour Ghadir Mahdavi

The Banking Crisis is previous decades caused the discussion of Systemic Risk in the financial market, including the Banks, has been taken into Consideration by Policy- makers. Based on this in this research using Delta Conditional Value at Risk (CoVaR), the Systemic ri More

The Banking Crisis is previous decades caused the discussion of Systemic Risk in the financial market, including the Banks, has been taken into Consideration by Policy- makers. Based on this in this research using Delta Conditional Value at Risk (CoVaR), the Systemic risk in Iran Banking Section has been evaluated. For this reason, seventeen banks out of all ones which have been listed in Tehran Stock Exchange and the equity of their Owners from 1389 to 1395 was available, have been chosen. The results Show that CoVaR for Khavarmianeh Bank Was the most (15.61) and for Sarmayeh Bank was the least (0.32). These results indicate that the crisis or disturbance in Khavarmianeh Bank more than the other Banks, affects the Financial System and Sarmayeh Bank has the least effect. In other words, any crisis in khavarmianeh Bank will give a rise of about 15.61 Percent to the Financial System risk, while the corresponded value for the Sarmayeh Bank is only 0.32 percent. Manuscript profile

Open Access Article

36 - Measurement conditional value at risk based on FIGARCH-EVT method at Tehran stock Exchange
mohammadreza Lotfalipour Mahdiyeh Nosrati abolfazl Ghadiri Moghaddam Mahdi Filsaraei

An important factor in risk management is optimized conditional value at risk (CVaR) of the portfolio. Choose a model which calculates time depended to variance rather than the model with constant variance lead to improve data modeling. Using an appropriated method for More

An important factor in risk management is optimized conditional value at risk (CVaR) of the portfolio. Choose a model which calculates time depended to variance rather than the model with constant variance lead to improve data modeling. Using an appropriated method for measuring risk in financial asset returns distribution has a great utility. The main purpose of this study is implementing a hybrid procedure to calculate CVaR which, models, volatility and dynamics in clusters, and calculates CVaR value based on fat tail feature. In this case, using Extreme value theory (EVT) leads to calculate CVaR more precisely. In addition to, using some ARCH (autoregressive conditional heteroskedasticity) family models result to dynamic feature in estimating CVaR. Data were used in this study related to TEDPIX during 2001-2015. Total 2781 data were derived from Rahavard Novinand & TseCline softwares as daily. For analysis this TEDPIX data, MATLAB software and EXCELL were used. This result represented, return data distribution has fat tail. The historical simulation (HS) at 95% confidence level isn’t accurate, while the accuracy Generalized Auto-Regressive Conditional Heteroskedasticity-EVT (GARCH-EVT) model at 95% is more suitable. Using (Fractionally integrated generalized autoregressive conditional heteroskedasticity -EVT) FIGARCH-EVT method leads accurate estimates of CVaR in comparison with HS procedure. Calculating CVaR by FIGARCH-EVT-CVaR was more accurate than the GARCH-EVT-CVaR. This model has considered to both GARCH-EVT features and long memory property. The FIGARCH-EVT-CVaR model had acceptable accuracy and its exceptions are independent. In General, models which considered heteroscedastic, had an acceptable accuracy in comparing HS Manuscript profile

Open Access Article

37 - Examining the efficiency of optimization models of multi objective genetic algorithm and particle swarm algorithm under the risk criteria of conditional value at risk and mean smai variance in determining the optimal stock portfolio
Dariush Adinehvand Ebrahim Ali Razini Rahmani Mahmoud Khoddam Fereydoun Ohadi Elham Sadat Hashemizadeh

10.30495/afi.2023.1988982.1233

Objective: The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.Research Methodology: Attaini More

Objective: The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.Research Methodology: Attaining an efficient portfolio involves solving an optimization problem. There are numerous techniques and tools available to solve this issue. In this study, 15 stocks from companies listed on the Tehran Stock Exchange, including symbols such as Khapars, Khazamiya, Vepasar, Foulad, Akhabar, Kegel, Femli, Tapiko, Sepaha, Fazer, Fakhas, Shohbaran, Shefan, Qamro and Qathabat, were selected using cluster sampling. First, the daily returns of these stocks were calculated over a 5-year period from 2015 to 2020 (1183 days). The risk of the optimal investment portfolio was then calculated using the Mean-Semi Variance and Conditional Value at Risk models. These two criteria were compared using a classic solution method. Subsequently, the output data obtained from these calculations were compared using MATLAB software, employing the Particle Swarm Optimization algorithm under the Mean-Semi Variance risk criterion and the Genetic Algorithm under the Conditional Value at Risk criterion.Findings: The results of this study indicate that the meta-heuristic Particle Swarm Optimization method yields a higher portfolio return ratio compared to the Genetic Algorithm in the Mean-Semi Variance risk criterion.Originality / Value: This research utilizes multi-objective genetic algorithms and Particle Swarm Optimization, which are intelligent and novel algorithms, to minimize the objective function value using Conditional Value at Risk and Mean-Semi Variance criteria. These algorithms optimize the return and risk ratios of the stocks in the investment portfolio with the highest possible accuracy. Additionally, the efficiency comparison of these models using MATLAB software contributes an innovative aspect to this study. Manuscript profile

Open Access Article

38 - The use of support vector machine and Naive Bayes algorithms and its combination with risk measure and fuzzy theory in the selection of stock portfolio
Danial Mohammadi Emran Mohammadi Naeim Shokri Nima Heidari

10.30495/afi.2023.1995691.1257

Purpose: The purpose of the current research is to create an optimal portfolio using machine learning algorithms and fuzzy theory, which has a better return than the average return of the market (total index of the stock exchange).Research Methodology:In this article, t More

Purpose: The purpose of the current research is to create an optimal portfolio using machine learning algorithms and fuzzy theory, which has a better return than the average return of the market (total index of the stock exchange).Research Methodology:In this article, the stocks of the selected companies are classified in the first stage using the two introduced algorithms. In the next step, stocks that entered the positive class are predicted for the next trading day with the help of random forest algorithm. For each company, three predictions are made, which are the inputs of fuzzy method optimization. Optimization is done with the aim of minimizing the risk with risk measures of value at risk and value at conditional risk. Shares information is five years old (daily) and its time period is from the beginning of 2017 to the end of 2021.Findings: In the end, each of the algorithms and the risk measure used were measured and compared with the actual market return. Based on the obtained results, the CVAR risk measure has a better capability and result than the VAR risk measure, and the support vector machine algorithm has also achieved a better performance in choosing the investment portfolio.Originality/ value: This research is optimized in the form of a capital sample by integrating machine learning methods and risk measures. Adding VaR and CVaR risk metrics enhances the decision-making process regarding risk reduction. Forecasting with the help of random forest and using an approach based on fuzzy theory for risk and value analysis gives the research an innovative perspective in portfolio formation. The findings provide investors and researchers with valuable insights in their search for better investment strategies. Manuscript profile

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