فهرست مطالب محمدعلی رستگار

We investigate the effects of the implementation of reverse factoring on the credit risk level of financially constrained suppliers within a supply chain. To simulate the desired supply chain finance environment, an agent-based framework is developed. Short-term bank financing and reverse factoring are available financial instruments for suppliers. The estimations regarding the default probability of agents are calculated using formulations of the KMV (Kealhofer Merton Vasicek) model. It incorporates market-based information and company-specific financial data to estimate the likelihood of default and potential losses based on the estimation of the market value and volatility of the firm’s asset and calculation of the distance to default. Results suggest that the adoption of reverse factoring significantly alleviates the credit risk levels of financially constrained members of a certain layer within a supply chain.

Finding solutions for price prediction, forming an optimal portfolio and achieving more profit are the basic goals of financial market activists. The purpose of this research is to predict the price of financial assets such as several stocks, gold, coin and a number of digital currencies using the LSTM neural network model and then form an optimal portfolio by calculating the rate of return, risk and the Sharpe ratio. The data used is from the archives of the Tehran Stock Exchange website, the website of the gold, coin and currency information network, as well as the website of buying and selling digital currencies. The time series of the prices of the investigated assets is between 2017 and 2020. Also, we used Python programming language and Gephi software to build the model and analyze the data. In the end, it was found that the LSTM neural network model is capable of predicting the price of financial assets with a very low error rate in each asset, and according to the Sharpe ratio obtained for each financial asset and the correlation matrix, Vebank stock, Khbahman 1 stock, and Digital currencies TRON, Tether and Bitcoin allocate more shares in the proposed portfolio.

Knowing the real costs of a project, correctly estimating them and identifying the risks involved in resource allocation for projects such as the construction of a petrochemical unit whose resources are expensive or difficult to access, is of particular importance. By using costing methods in conditions of risk and uncertainty, more reliable cost information can be obtained. By examining past studies and researches in the field of costing, it was found that their focus was mostly on carrying out activities, and less attention was paid to the risk factor. In this article, the use of the ABC approach, which focuses on identifying activities and cost centers, has been discussed in the form of a mathematical model whose purpose is to minimize the risk of resource allocation to activity centers based on the desired limitations in costs and resources. Due to the fact that the numerical values of the parameters in the general model, such as cost and time, have uncertainty, risk-based resource allocation modeling has been considered. To test the proposed model, the main activities of the construction and installation stage of a petrochemical unit were considered, and after collecting the necessary data, the steps of the compiled model for resource allocation and the steps of the activity-based costing approach have been implemented. The results show that there is a difference between the costs calculated based on the allocation of resources of the proposed model for the activity centers and the costs estimated according to the past data.

Supply chain finance includes a set of innovative solutions to improve working capital in a supply chain. The purpose of this study is to provide a comprehensive framework for designing supply chain financial strategies by combining a finance-oriented perspective and a supply chain-oriented perspective, simultaneously. To achieve the research objective, the seven-step model proposed by Sandelowski and Barroso was used to analyze the content of the articles. The statistical population of this research includes international publications in the field of supply chain finance between 2000 and 2021. After collecting the related articles, the relevant codes were extracted and finally the reliability of the coding was confirmed by calculating the Cohen's kappa coefficient. According to the classification of codes extracted from selected articles, the components affecting the design and implementation of supply chain finance strategies were identified. This framework consists of 5 main categories including supply chain, financing instruments, actors, enablers and inhibitors and modeling. The reliability of this coding was also confirmed at the 95% confidence level by calculating the kappa index. The results of this study show that although several supply chain finance instruments are designed based on accounts receivable and accounts payable techniques, internal financing strategies through the use of surplus funds within the supply chain are not well developed. Also, based on the country's banking industry's need to increase income, in the modeling of external supply chain finance solutions, commercial banks can maximize their profits along with supply chain members at a rate higher than the risk-free interest rate.

One of the main problems of Iranian banks is the lack of risk management process with a forward-looking approach, and one of the most important risks in banks is liquidity risk. Therefore, predicting liquidity risk has become an important issue for banks. Conventional methods of measuring liquidity risk are complex, time-consuming and expensive, which makes its prediction far from possible. Predicting liquidity risk at the right time can prevent serious problems or crises in the bank. In this study, it has been tried to provide an innovative solution for predicting bank liquidity risk and leading scenarios by using the approach of news sentiment analysis. The news sentiment analysis approach about one of the Iranian banks has been used in order to identify dynamic and effective qualitative factors in liquidity risk to provide a simpler and more efficient method for predicting the liquidity risk trend. The proposed method provides practical scenarios for real-world banking risk decision makers. The obtained liquidity risk scenarios are evaluated in comparison with the scenarios occurring in the bank according to the guidelines of the Basel Committee and the opinion of banking experts to ensure the correctness of the predictions and its alignment. The result of periodically evaluating the studied scenarios indicates a relatively high accuracy. The accuracy of prediction in possible scenarios derived from the Basel Committee is 95.5% and in scenarios derived from experts' opinions, 75%.

Liquidity risk is caused by the inability of a bank to pay debts on time, fulfil obligations, or the inability to expand the portfolio of high-yielding assets at a conventional cost. In other words, when a bank does not have sufficient liquidity, it is not able to obtain sufficient funds quickly and at a reasonable cost by increasing debts or converting assets, which will affect the bank's profitability. It can be said that the main cause of liquidity risk in banks is the mismatch between the amount and maturity of debts and assets, resulting in a negative liquidity gap. In this regard, institutions and banks used different approaches, including the approaches of the Wing Committee, to assess liquidity risk. This research aims to review liquidity risk management in the banking industry. In this research, content analysis of previous research has been done using the meta- Synthesis method and Sandelowski and Barroso's seven-stage model. Using the meta-Synthesis method, 242 related research between 2000 and 2023 were extracted from reliable scientific databases. For this purpose, after analyzing the research, 41 researchers have been selected. This research has provided a comprehensive framework for better risk management in the banking industry. This framework includes 5 main categories, which are liquidity risk data, liquidity risk factors, liquidity risk assessment, liquidity risk guidelines and liquidity risk management, 12 subcategories, 104 concepts and 175 codes. The results of this research can be a basis for better use of liquidity risk management in the banking industry. Also, the results were confirmed based on the opinion of experts with a Kappa index of 0.738

In recent years, after the economic crises, the value of operational risk assessment has been observed in the financial industry, while the biggest impact of operational risk has been on the banking industry. As a result, more attention has been paid to operational risk assessment in the banking industry after the financial crisis. Operational risk is defined as the risk of loss caused by the inadequacy or inefficiency of internal processes, people, systems, or external events. In this regard, institutions and banks have been looking for operational risk assessment by taking different approaches, including the approaches of the Basel Committee. The current research aimed to review operational risk management in the banking industry. Using the meta-synthesis method, 643 related research documents between 2000 and 2022 were gathered from among reliable scientific databases. By using this method, 43 final documents made the basis for extracting the findings. Finally, this method identified 5 main categories, including operational risk, risk assessment, risk quantification methods, risk analysis, and risk management, as well as 10 subcategories consisting of 43 concepts and 169 codes. The results were confirmed based on the experts’ opinions with a kappa index of 0.756.

Financing of infrastructures and huge projects such as utility-scale solar power plants is imperative to economic development. From the perspective of investors, financing of infrastructure projects divides into two general methods including lending and partnership. One of the methods based on lending is project finance that is the focus of this study. In loan-based investing, the lower the lender's estimate of the uncertainty (risk) of future project cash flow, the more willing lender would be to invest. The method is a limited recourse; thus, the lender tendency to partnership in the finance deal depends on the reliability on future cash flow of a project. Here, we gather the results of different studies and investigate the uncertainties led to fluctuation of the expected future income of utility-scale solar power plants. Using Monte-Carlo simulations and Maximum Likelihood Estimation, the annual probability distribution of debt service coverage ratio for a 10 MW solar power plant project has been estimated during the loan term, and we use that to calculate the probability of default for each year. Then, using sensitivity analysis of financial indices to various leverages, the appropriate ratio of debt and equity in the structure of project financing are obtained. The presented method of estimating solar power plant project income can be used for other utility-scale solar power plants using specific uncertainties for each location. Our work paves the way to a more reliable decision making for lender(s) and facilitates the process of attraction of investor(s) for project company.

In this study, in order to measure the liquidity risk in interbank payment systems, the time series of daily data balances of an Iranian bank's payment systems from 01/01/94 to 31/5/98 and then We examined stationary time series with Dickey Fuller and Philips Peron tests and compared the expected value and risk value of payment systems data with the historical method and compared with the Pareto method. The results of the Kopik and Christofferson tests showed that Pareto's generalized approach to better manage banks' liquidity risk is better than historical method based on daily data of payment systems. The bank can then provide liquidity management operations to manage the liquidity risk in the payment system

Nowadays, the measurement of the risk of the marketplace has a significant effect on investments; however, the inadequate evaluation of this risk will cause a financial crisis and possible bankruptcy. One of the typical approaches to measure this risk is the probability-based risk measurement method, known as Value-at-Risk (VaR), for estimating and backtesting of which there are various methods. The purpose of this paper is to put forward a comprehensive test for backtesting and analyzing the sensitivity of VaR based on the number of samples (n) and confidence levels (N).

First, the VaR of Tehran Stock Exchange data was estimated by applying GARCH-Copula, DCC, and EVT. Next, by using the multinomial backtesting in two steps the accuracy of VaR estimation and ranked the models were tested. Thereafter, considering the number of samples (n) and the confidence levels (N), the sensitive analysis of the backtesting result demonstrated the accuracy of the estimated VaR by selecting the most appropriate parameters.

Sensitive analysis findings indicated that in all three models, increasing the parameter "N" will result in an increase in the error rate. On the other hand, sensitive analysis of parameter "n" proved that its value depends on the technique used to estimate VaR, but generally, any increase in it leads to validation of VaR estimation models. The results also showed that according to the EVT method, at least 29% of the data is required to be used as a test sample in VaR estimation; however, the amount is equal to 22% in the DCC and GARCH-Copula methods.

The result of the sensitivity analysis indicated that the reliability of different estimating VaR techniques relies on "n" and "N" parameters and different amounts of these two parameters can generate inaccurate and uncertain outcomes for each model. In addition, ranking these methods by using the loss function, GARCH-Copula, EVT and DCC methods ranked first to third, respectively.

Considering that the existence of a performance appraisal system in different dimensions of organizations and in decision-making units in general, is very important and the lack of a proper performance appraisal system to evaluate the performance of decision-making units in the use of available resources and facilities will have negative consequences for them, it is necessary for different organizations to develop an efficient evaluation system in order to evaluate and improve their performance. Also, considering that in general, efficiency is a criterion for evaluating the performance of a decision-making unit and generally expresses how a decision-making unit uses the resources at its disposal to produce the highest level of output, the performance of different decision-making units can be evaluated by calculating theirefficiency. Therefore, considering the vital role of banking system in the economic development of ourcountry and the effect ofbanks’ branches performance on the overall performance of banks, in this study, considering the nature of banks' branches perfomance, the operational process in the investigated branches have been divided to three stages.Then  the performance of each branch has been evaluated based oni these three stages. The first part of bank branches work process is related to how they use resources such as manpower, equipment and space of the branch to collect deposits, and the efficiency calculated at this stage is called operational efficiency. In the second part, their performance in converting the collected deposits into bank facilities has been evaluated, and the performance calculated in this section has been named as accreditation efficiency. In the third part, the efficiency related to how theymake profit from the granted facilities is calculated, which is named as the profitability efficiency. Because banks' products and their branches include multiple inputs and outputs, many studies on bank performance have used data envelopment analysis to evaluate the performance of these units (Kong et al., 2017). The main application of data envelopment analysis method is based on its ability to identify inefficient units.This enables the bank management to take action to eliminate inefficiency by reallocating resources at its disposal. But there is one major drawback to this method, and that is full-performance units that are actually on the performance frontier are only more efficient than the other units present in the sample under study. In other words, there may be branches outside the sample under study that perform better than branches on the performance frontier and are able to produce a certain level of output using less input. This structural flaw may lead to bias in the results of data envelopment analysis method (Aggelopoulos & Georgopoulos., 2017). In this research, an attempt has been made to eliminate this shortcoming by using bootstrap method and to correct the bias of the results obtained by applying envelope analysis method, which has been used to calculate the efficiency of bank branches in this research. The results of this study shows that the average efficiency of the studied branches in stage one is more than stages two and three and the lowest level of efficiency is related to profitability efficiency. Also, none of the studied branches have been able to have full performance in all three stages. It is necessary to differentiate between the performance of branches that are on the edge of efficiency in terms of standard data envelopment analysis method, but their levels of performance are different.  

In this study, a slack-based measure model, which is one of the main models of data envelopment analysis method, has been used to calculate the efficiency of the studied bank branches. This also has the ability to consider the negative impact of adverse inputs and outputs. . This model is one of the data envelopment analysis models that uses auxiliary variables (surplus inputs and lack of outputs) and provides a scalar size for performance score. Also, because the working process of bank branches include different sub-processes, it is necessary to separate the operational process of branches into several stages and evaluate the efficiency of each stage and the overall efficiency of the branches. Therefore, in this research, branch operations have been divided into three levels, under the headings of "operational efficiency", "accreditation efficiency" and "profitability efficiency". In the first stage, the ability of the studied branches to attract deposits using the resources at their disposal has been measured; Therefore, variables "personnel and administrative costs", "depreciation costs" and "rent costs" of the branches are considered as input in the first stage and variable "volume of deposits" as output. In the second stage, the performance of the studied branches in terms of accreditation is evaluated, in which variable "Volume of deposits" as input and variables "Deferred loans" and "Non-deferred loans" as output are used. In the third stage, in order to evaluate the profitability of the studied branches, in addition to output variables of the second stage, variable "Volume of guarantees" is used as an input variable and variables "Profit and obligation of facilities" and "Fee of guarantees"are used as output variable. A noteworthy point in the development of the three-stage model is that in the process of determining appropriate inputs and outputs to calculate efficiency and applying the optimization modelfor calculating efficiency, considering the negative impact of undesirable variables such as "deferred loans" on a branch performance is really vital. It is obvious that not separating favorable variables from unfavorable ones will cause an increase in the production of desirable outputs and the efficiency of the studied branches will be calculated more than the actual value. Therefore, in this research, a measurement model based on auxiliary variables has been used to calculate the efficiency.It has been optimized to consider the negative impact of undesirable input and output variables. Finally, after calculating the efficiency of branches in each stage, the overall efficiency of each branch is calculated using simultaneous optimization method. But  a more important flaw in standard envelopment analysis method is how to determine the efficiency boundary . As a limited sample of a large community is selected and the performance boundary is determined according to this sample,  there might be a branch outside the sample that has more output with the same number of inputs as branches on the performance boundary, and this calls into question the validity of the performance boundary determined by standard envelopment analysis method. Therefore, in this research, to solve these problems, using Bootstrap method and SW algorithm, 2000 simulated branches have been generated for each main branch, and by comparing the average experimental distribution of the simulated branches with the main branch, The amount of skewness of results obtained from the standard model is calculated.  

The results of this study show that at the third stage ("profitability efficiency"), due to large volume of non-performing loans (which are considered as undesirable inputs) or due to low volume of interest received on facilities and/ Or for both reasons, the efficiency of branches under review is lower than the other two stages. Also, none of the studied branches have been able to be efficient in all three stages. Furthermore, by comparing overall efficiency scores and modified overall efficiency scores, it can be seen that by modifying overall performance scores obtained from data envelopment analysis method, the overall efficiency of all branches is examined at a lower level. It should be noted that efficient branches are  those whose efficiency is equal to one in terms of standard data envelopment analysis method, and if a branch with the highest modified efficiency is considered as an efficient branch, it means that bootstrap method is accepted as a method for ranking branches in terms of performance, while bootstrap method is not a valid method for ranking and only corrects the bias of standard data envelopment analysis method results. The results also showed that the efficiency of two or more branches in terms of standard data envelopment analysis method may be equal to one and these branches are on the edge of efficiency but their levels of performance  are different. This indicates that bootstrap method,  is both able to correct the bias of standard envelopment analysis method results,  and differentiate between the performance of branches that are on the edge of efficiency in terms of standard data envelopment analysis method but  have different levels of performance.  

According to the results of this study, none of the studied branches have been able to be efficient in all three levels and the lowest level of efficiency, among the three levels, is related to profitability efficiency. One of the policy recommendations to increase the efficiency of branches is that due to low efficiency scores in the third level (profitability efficiency), banks need to strengthen the credit and accreditation system of customers. This has a direct impact on reducing the volume of overdue loans. It should be noted that high volume of overdue loans, as an unfavorable output, reduces  both efficiency scores in the second level (credit efficiency) compared to the first level and the desired output in the third level by reducing interest received fromloans. , however unfavorable inflows (deferred loans) increase. Since the results of standard envelopment analysis method is skewed due to  studying a limited sample and insufficient knowledge of the statistical community features, in this study we tried to use bootstrap method to approximately modify the skewed results of  standard cover method. The results of this study show that bootstrap method,  is both able to correct skew  and differentiate between the performance  ofefficient branches (in terms of standard data envelopment analysis method), which have different levels of performance. Therefore, the second policy recommendation to banks is to use the method used in this study to evaluate the performance of their branches.Finally, for future studies, it is suggested to use the dual bootstrap method to correct the skew and study the effects of various factors on the efficiency of bank branches.

Credit risk is very important for banks and determining an optimal level for it can save banks from economic crises. By evaluating the banking system and the economic situation of the country, banks predict their deferrals and identify the factors influencing credit risk in order to create financial stability and reduce credit risk. Banks are influenced by both banking and extra-banking factors and the risk of these factors cannot be eliminated, but it can be managed. In this study, the effect of realized and optimal credit risk status of banks listed in Tehran Stock Exchange in different seasons and under different time conditions during 2006-2018 was investigated. The results of the research hypothetical show that the optimal credit risk calculated changes over time and should be selected according to the time under study, an optimal level of credit risk should be selected and it can be said that in the long run explanatory variables have a significant effect on credit risk and asset return rate and equity return have a positive effect on credit risk.

In recent years, many articles and researches have been published on the use of machine learning methods and algorithmic trading in financial markets in order to earn returns. The aim of this study is to create an automated trading system using image processing by convolutional neural network. For this purpose, initially, after receiving the data required for the selected stocks, 28 technical analysis indicators were selected and the values of each were calculated separately for each stock. Then the time series of these indicators were converted to 2D images, and as a result, for each data on the time series of the stock price, a 2D image with dimensions of 28 x 28 was created. After labeling each image with one of the buy, sell, or hold labels, these images entered the convolutional neural network. Also, to evaluate the return and risk of the proposed system, a method for buying and selling based on the results of the model in the past has been introduced. The results show that in 80% of cases, this method is more effective than the buy and hold strategy. It also always performs better in terms of standard deviation risk and maximum drawdown. Also, the results show the high impact of trading commission on the Tehran Stock Exchange on the return of the model. In such a way that the model loses many times the profit earned for the payment of the commission.

After the financial crisis of 2007-2009, in which liquidity problems led to insolvency and consequently the bankruptcy of many large banks and financial institutions such as Lehman Brothers, Basel Committee on Banking Supervision introduced liquidity requirements for the most part to reduce the possibility of bank insolvency caused by liquidity shocks. This research develops an agent-based model of a banking system to be used to analyze the impact of the liquidity requirements on the solvency position of banks. The model devises a banking system with 12 heterogeneous banks in which banks perform their traditional activities namely taking deposits and making loans. Banks can fulfill their liquidity needs by engaging in interbank lending, selling their securities, and using central bank lending assistance. The model aims to study the behavior of different banks in response to imposing liquidity requirements. This model is calibrated using the data of Iranian listed banks during 2018-2020. Liquidity requirements are measured using liquidity coverage ratio, and the solvency position of a bank is measured using the capital adequacy ratio. The results of the simulations demonstrate that as liquidity requirements increase, the solvency position of some banks improves, some banks deteriorate, and some remain unchanged. Regarding this reaction among other various factors, profitability, inflow, and outflow of liquidity, and finally, the outflow rate parameter play an essential role.

  This paper analyzes the possibility of publishing mortality bonds. This class of securities provides attractive investment opportunities for capital market and is a good hedging tool for managing the risk of life insurance companies. It allows the insurers to access a new source of financing and provides some facilities for investors to diversify their portfolios.

It is required to predict future rate of mortality in order to conduct pricing of mortality bonds. We used lee and carter model simulation mortality rate, as it is very flexible and can be used for limited data. First, the mortality rate of 5 _year age groups for years between 1996 and 2016 was calculated. The reason for using Gregorian calendar is that we use the united nation’s database. With Singular Value Decomposition (SVD), Lee-Carter model parameters were estimated and we have produced various scenarios for Iran’s mortality index for the next three years using the Monte Carlo simulation. In the last step, a sensitivity analysis for the interest rate and catastrophe threshold was provided to examine the price response to changes in interest rates and changes in the threshold.One of the characteristics of the q-forward contracts if that they can be used for hedging both mortality and longevity risk. Mortality risk refers to the risk that mortality rates are higher than expected whereas longevity risk refers to the risk that mortality rates are lower than expected. A q-forward is an agreement between two counterparties to exchange at a future date (the maturity of the contract) an amount equal to the realized mortality rate of a given population at that future date (the floating leg), in return for a fixed mortality rate agreed upon at the inception of the contract (the fixed leg). In principle there are hedgers who are interested in both sides of the trade of a q-forward contract.

The result shows that at maturity of three-year mortality bonds, increasing the interest rate, will rise the prices of these securities. Moreover, as the threshold grows, the price of these securities also increases; it is because the probability of default by issuer is reduced. Furthermore, by keeping other conditions constant, if the issuer determines the threshold range to two units, the price of these securities is reduced. Ultimately, the result shows that there is a small possibility of issuing these papers in the Iranian financial market.

In addition to introduction of mortality bonds which is an available investment instrument in global financial markets, this study aims to investigate possibility of issuing it in Iran and also overview the pricing of these bonds for domestic investors and issuers based on earnings discount approach.

Entering into the interbank market in order to balance profitability and liquidity risk management, depending on the conditions of short-term activities, banks are required to equip resources through this market or to lend short-term loans to other banks. Banks' commitments to each other mainly arise in the interbank market, which can lead to increased systemic risk due to the spillover effect. Therefore, the objective of this paper is to analyze the network dynamic stability of the Iranian overnight money market through methods of statistical mechanics applied to complex networks .The results show that the network structure changes during time depending economic conditions. Systemic risk measures such as clustering coefficient, average short path, heterogeneity and centrality, show that the networks systemic risk increases and then by occurring default and crisis in one bank, default spillover during the domino effect in whole network. Also, in the event of failure, the most vulnerable group is to privatized and specialist governmental banks, and the private banks, due to the high volume of exchanges and net negative flows, can put a considerable systemic risk to the interbank market network. Morever, the signals of speculative activity by private banks are found.

According to the Basel III regulatory framework, uniform minimum liquidity requirements have been imposed on all types of banks. Using an agent-based model of a banking system, we investigate the effects of liquidity requirements on banks' insolvency under two policy experiments in one of which the minimum liquidity requirements are applied uniformly and in the other differentially across banks. The model introduces a banking system with 12 heterogeneous banks that must also comply with two liquidity requirements while performing their daily activities of taking deposits and making loans. The model is applied to the Iranian banking system. Results illustrate that because banks respond differently to liquidity requirements, applying one size minimum liquidity requirements to all kinds of banks, strengthens the likelihood of a liquidity shock turns into banks' insolvency and could increase banking system instability. Thus our findings highlight that to achieve financial stability at the national level, policymakers should revise the current one size fits all approach when designing liquidity requirements.

The objective of this paper is to analyze the network topology of the Iranian overnight money market through methods of statistical mechanics applied to complex networks in order to assessing systemic risk. We investigate differences in the activities of 33 Iranian banks dividing into different four types between 2010-2015 by analyzing 66 montly adjacency matrixs. Using degree distribution analysis of the networks, we find that that Iranian interbank market network is scale-free network and cumulative degree, in-degree and out-degree follows the power-law distribution. In terms of the criterion of assortativity, the interbank market network of Iran is assortative and core-periphery with one or more banks as the money center. The results show that the Iranian interbank network is vulnerable to shocks and has high level of systemic risk. Also, in the event of failure, the most vulnerable group is to privatized and specialist governmental banks, and the private banks, due to the high volume of exchanges and net negative flows, can put a considerable systemic risk to the interbank market network.

Nowadays, banks in the country are faced with serious problems in terms of their assets. One of the factors that led to this situation is the poor quality of banks' assets, which can be attributed to the lack of a rating system and an improper assessment of credit risk. This study predicts the probability of default during a specific time using the Cox regression model as well as the survival model of spline-based logistic regression. For modeling of credit risk, using these two methods, 10 variables related to 2861 customers of an Iranian bank were used. We compared two models using ROC method, the Cox regression model with AUC = 0.799 was more efficient than the spline-based logistic regression model with AUC = 0.746.

After restructuring, Iran’s electricity market has become one of the most competitive markets in which generation companies offer their proposed price on several price benches. So, the decisions in this market can use statistical concepts. In this paper, a conceptual model is presented according to simultaneous analysis of probabilistic distribution for historical data of market clearing price and frequency of acceptance of bids. Based on this model, the probable value of market clearing price is measured through the risk of historical data price intervals and the decision is made about bidding strategy. Also, a method is proposed to calculate the probability of acceptance and risk of rejection of bids on the electricity market. The data (Spring 2014) from a power plant as a numerical example have been used to implement the method. The results show that there is no direct relation between the high or low price intervals and the risk of acceptance, but the probable data regarding the distribution of price intervals should be analyzed. Also, due to the risk of the proposed bid prices, this model is capable of applying risk-taking and risk-aversion and finally appropriate bid price is selected based on the risk appetite and acceptable risk level.

At the core of any investment lies the return on investment. To gain a favorable return, an investor should take investment-related risks. The interaction between risk and return can lead to decisions on asset allocation. A key strategy in investment discussions is diversification in investment portfolio. Investment strategy is undetermined in different assets such as security, gold, currency and cryptocurrency. Despite the temporary recession and success of certain assets, it is hard to prioritize investment among assets (in terms of risk and return) to ensure that the investor makes the highest profit at the lowest risk. Thus, the present research used the Mean-CVaR model along with the Extreme value theory (EVT) based on Copula’s theory to estimate the correlations among time series. It used the dynamic conditional correlation (DCC) estimation method to measure the joint distribution of assets regardless of the normality assumption of data, collinearity, priorities and weights of investment in assets as optimal values.

Theoretical Framework:

Diversification of conventional investment portfolio which only involves cash and security with alternative assets such as goods, currency and estate helps to decrease the correlation of assets and increase its resistance to severe changes in stock market. It, thus, helps to improve the performance of investment portfolio (Fischer & Lind-Braucher, 2010). Moreover, due to the low correlation between conventional assets and cryptocurrency and its high-efficiency, cryptocurrency is a good instrument to be combined with diverse investment portfolios and can increase the Sharpe ratio (Chuen et al., 2017).One assumption of the distribution of financial return time series is the normality of data. However, in actuality, many financial return time series are not normal. When the normality assumption is violated, Value at Risk (VaR) is a proper measure.Risk exposure value is not an integrated risk measurement and due to the lack of sub-aggregation property, may be inefficient in optimizing investment portfolio. Thus, researches introduced Conditional Value at Risk (CVaR) as an integrated measure and an alternative for VaR.

As the return on financial assets is marked by a fat tail and is not marked by a normal distribution of data, to better predict the distribution of series, EVT was used. Moreover, Copula’s theory was adopted and the structure of correlations among series as time-varying was modelled via the dynamic conditional correlation estimation and the joint distribution of assets was analyzed regardless of data normality and collinearity. Then, the Mean-CVaR model was used to estimate the risk exposure value and set the investment priorities and weights among assets in Tehran Stock Exchange, Gold Coin, USD and Bitcoin as optimized values.In this research, for an optimal allocation of investment among four above assets in daily return, Tehran Stock Exchange index (TEPIX) was used. The daily return on investment in Gold Coin (the old version) was estimated in Rials. That of USD was estimated in Rials in Tehran free market. Finally, the daily return on investment in Bitcoin was estimated in Rials between October 2014 and April 2018.

A negative correlation was estimated via DCC-Copula between certain assets, which shows that when a particular asset gains higher return on investment (than the mean value), the other asset does not follow the same trend. Thus, investors are capable of diversifying their portfolio accordingly.Prediction of return on assets showed that the highest expected daily return on investment belonged, respectively, to Bitcoin, Gold Coin, Dollar and Tehran Stock Exchange. Moreover, as the optimized weights showed for zero CVaR, due to the low variance, the greatest investment weight was investment in Tehran Stock Exchange. The higher the investor’s risk-taking, the greater the investment weight of Gold Coin and Bitcoin.

Conclusions & Suggestions:

Considering the investor’s risk-taking level, if s/he tolerates the least risks, s/he is suggested to make the most investment in securities. An increase in the minimum expected return on investment is followed by an increase in the investment share of Gold Coin and Bitcoin. Thus highly risk-taking investors are suggested to invest in Bitcoin and low risk-taking counterparts are suggested to invest in Gold Con. Considering the conditional Sharp (C-Sharp) ratio optimal portfolio indicated a better performance of various portfolios than any other asset, and the best performance of the portfolio includes Gold Coin with more than 70% and Dollars and Bitcoins with an equal weight. Furthermore, according to the C-Sharp ratio in the optimal portfolio, the minimum weight of Gold Coin is 60% and the maximum share of Dollar and Bitcoin is 20%.

The aim of this paper is to study the correlation structure of different risk measures. These measures include idiosyncratic skewness (IS), idiosyncratic kurtosis (IK), volatility, idiosyncratic volatility (IV), Cornish-Fisher VaR(CFVaR), extreme downside risk (EDR) and right tail index. The GARCH(p,q) model is estimated by Fama-French regression innovations and then using the residuals to calculate the EDR and other measures for 175 stocks in TSE. In this research, Extreme Value Theory (EVT) is used to estimate Extreme downside risk and Cornish-Fisher expansion of value at risk. The results show that the IS and CFVaR methods have the highest correlation coefficient with the EDR method, but given the low correlation values, this implies that the EDR effect cannot be completely subsumed by other risk measures. Only the EDR and CFVaR measures focus on left tail of distribution and are comparable to each other. Back testing results for Cornish-Fisher expansion of value at risk and Extreme downside risk approaches show Extreme downside risk has a better prediction in compare with Cornish-Fisher expansion of value at risk.

This research is aimed at offering an order splitting strategy to divide a large order into a number of smaller orders to reduce Market Impact cost and imbalances created by Large orders in the market.

Due to the limited access to data and high volume of calculations, for some shares of the Tehran Stock Exchange (TSE), market impact cost function of bought trades were calculated using the I-star model. Then, by using the MI function and based on the investor's trading horizon, we split a large order into a series of small orders to place them at intervals rather than ordering all at once. The goal is to reduce the market impact cost, and avoid creating an imbalance of large orders in the market.

According to the intraday patterns of the average trading volumes and the market impact cost, it is observed that the trading volume of shares is low and market impacts cost are high at the beginning of the day, then at the end of the day, as the trading volume and market liquidity increase, the market impact cost incurred by traders reduces. This is mainly because investors will not need to increase trading prices to complete their orders when the stock market experiences an increase in liquidity and trading volumes, and this is also seen in the Tehran Stock Exchange. The market impact cost function for the shares in Tehran Stock Exchange is also concave and investors behave much more aggressively when buying compared to selling.

The results show that by examining the intraday patterns of liquidity parameters such as trading volumes and market impact then designing a trading strategy for the splitting of large orders can reduce the additional costs incurred by traders and result in orders being traded in relatively more reasonable prices.

One of the important issues between venture capital and entrepreneur is the challenges posed by asymmetric information and benefits' contrast between them. The main target of this research is to design a contract not only reduces benefits' contrast between the parties but also creates an interactive incentive for each of them. In some countries such as Iran, contract parties are facing with decision making under ambiguity. In this study, while presenting different models for measuring ambiguity and the effect of active investment, an optimal mathematical model has been proposed in order to maximize the venture capitalist and entrepreneur's profit. Eventually by using Karush-Kuhn-Tucker method, an optimal value for each variable has been determined. The results show that the level of entrepreneur effort, directly affects on the final revenue of the project. Also, increasing the level of entrepreneur ability increases his effort level and ultimately increases the profit of the incentive contract for venture capitalist.

The objective of this research is to analyze the risks involved in funding public private partnership (PPP) projects with project finance in Iran. PPP structures in Iran cannot achieve the desired objectives such as optimal risk sharing and mitigation due to the lack of suitable instruments to mitigate legal, regulatory, political, and credit risks. Thus, expected cash flows for loans repayment do not build up and this leads to a decrease in asset quality of banks. In this paper, in order to better manage the project finance loans, the challenges and characteristics of this type of finance are considered; the risks are identified; and finally the risk sharing methods are proposed. The results of this paper show that first, considering the benefits of project finance, it is better to fund projects by project finance. Second, it is recommended that commercial and development banks calculate the probability of default of projects by using the method proposed in this research. Moreover, according to the results of this study in order to prevent bankruptcy and default of companies, it is suggested that in feasibility studies, risk assessment and risk management should be carried out to share or to cover risks.