fereydon ohadi

This study examines the accuracy of Heterogeneous Autoregressive (HAR) models in forecasting the Conditional Value-at-Risk (CVaR) of Exchange-Traded Funds (ETFs) on the Tehran Stock Exchange. The significance of this study stems from the need for better risk management in financial markets, where volatility and jumps significantly affect investment decisions.

Data from nine equity, index, and fixed-income funds were analyzed intraday with high frequency (daily and fifteen-minute intervals) from 2019 to 2022. Three main families of HAR models were evaluated by considering the relevant variables.

The results revealed that models based on second-order variations outperformed others in forecasting Realized Volatility (RV). Additionally, CVaR prediction was more accurate for index funds than for equity and fixed-income funds, with the HARQ model demonstrating superior performance.

Originality/Value: 

This study investigates the application of HAR models in predicting ETF risks and provides a novel framework for risk management and investment decision making, particularly in the Iranian financial market.

The aim of this study is to accurately estimate the cost of capital and manage risk in energy projects based on public-private partnerships (PPP) in emerging markets.
 
To explore the mechanism of determining capital costs and evaluating capital budgeting, Dynamic Conditional Correlation (DCC) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were employed. Additionally, the pure-play method was used as a tool for measuring systematic risk in energy-related projects in emerging markets.
 
The results showed that the DCC-GARCH model is capable of more accurately predicting volatility and capital costs in emerging markets. Moreover, the use of the pure-play method for calculating systematic risk led to reduced volatility and improved capital cost estimation. The findings also indicated that market volatility and risks associated with economic and financial policies in emerging markets have a significant impact on capital costs.
 
This study demonstrates that the application of advanced financial models such as DCC-GARCH and the pure-play method can lead to more accurate capital cost estimations in PPP projects and provide a better understanding of risks in emerging markets. These models offer valuable tools for risk management in the dynamic economic conditions of emerging markets.

This research aims to model and present an appropriate pattern in order to forecast systemic liquidity risk of corporate stocks in capital market of Iran. For this purpose, 486 listed companies in Tehran stock exchange and OTC from 2011 to 2020 were sampled and then the companies were divided into four groups (portfolios) according to combination of indicators and types of activites of companies. Then by using types of multivariate GARCH models and comparing them, finanlly the VAR(1)-DBEKK(1,2) was selected as an optimum pattern . The results of research showed significant relationships among of liquidity shocks and volatilities with all of subsections, and consequently the main hypothesis based on “presence of systemic liquidity risk of corporate stocks in capital market of Iran” was accepted. In a way that the portfolios of company stocks with a “low level of liquidity- industry section” and “low level of liquidity- financial section” respectively had maximum and minimum liquidity shocks transmission of effects on future returns of the other portfolios, as well as the portfolio with a “high level of liquidity- financial section” had maximum volatility persistence and liquidity risk transmission to other portfolios.

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.

One way to help you capitalize on investment opportunities and better allocate resources is to anticipate business risk. Predicting the probability of future events based on present and past information, In this way, first of all, by providing the necessary warnings, companies can be alerted to the occurrence of business failure so that they can take appropriate action accordingly. And second, investors and lenders distinguish favorable investment opportunities from unfavorable ones. And invest their resources in the right opportunities; Therefore, predicting the business risk of companies has always been one of the topics of concern for investors, creditors and the government. The purpose of this study is to design a business risk forecasting model using machine learning techniques.. The statistical population of the present study is the selected companies listed on the Tehran Stock Exchange during a period of nine years between 2007-2018. Hypothesis test results show that firm size, firm liquidity, firm profitability, firm growth opportunity, industry size, number of firms in industry have a negative effect on business risk. Meanwhile, the company's debt ratio has a positive and significant effect on business risk. The results also show that statistically, the life of the company, the decentralization of the industry has no effect on business risk. The results of model design and machine learning techniques show the efficiency of NB technique and then SVM technique compared to other machine learning techniques.

In this paper the effect of learning environment on the professional socialization of graduate students of accounting at the Islamic Azad University have been studied. This study is a descriptive correlational study whose data were collected through a questionnaire. The time of conducting the research is 2020. The statistical sample of the study includes 505 graduate students (Masters – PhD) in Accounting of Islamic Azad University across the country, which has been done using the probabilistic sampling method available. To test the hypotheses, the structural equation modeling technique has been used.  The results of hypothesis testing show that constructive learning environment has a positive effect on cognitive and motivational strategies .But this relationship between a constructive learning environment and the professional socialization of accounting students does not make sense. Also, the cognitive strategies of the professional sociability of accounting students have a significant and positive effect. While the motivational strategies of the professional sociability of accounting students do not have a significant effect. The results of this study can be useful and beneficial for policy makers and legislators of higher education in Islamic Azad University in order to develop educational programs that lead to greater awareness of university graduates about legal, professional, industry and ultimately meet their professional needs.

Tax compliance is important for governments around the world to fund. Traditional ways to improve tax compliance, such as increasing auditing, are the cost to implement this goal. Meanwhile, the impact of social and psychological norms on various variables is an interesting topic recently investigated in the field of behavioral finance. The purpose of the research is to investigate the effect of social factors on the objectives of tax compliance of individuals. The statistical sample includes 550 accountants and financial managers of companies and auditors in the public sector and private sector in 1396. The research data were collected through a Delphi method and then a questionnaire was collected and analyzed using the structural equation model with the help of LISREL software. The findings suggest the indirect effect of social factors on tax compliance. Social norms have a significant impact on tax compliance indirectly through personal and psychological norms. Therefore, the greater the power of social norms in favor of tax compliance, the personal and psychological norms of tax compliance will also increase, which will lead to a further increase in the goals of subjection. The results of this study will provide useful information about the social factors affecting tax compliance with its users and can be used by the tax authorities in planning to develop more cost-effective and effective solutions to increase taxpayer's compliance.

Nowadays, banking industry is an area in which innovation is required more than any other service industries. Studying the factors and indices effective on service innovation in tourism bank, is the aim of this research. Research statistical population consists of 580 out of the whole tourism bank personnel all over the country including users, senior users, bosses and deputies. The sample size is estimated 232 persons using Morgan Table, and the research time span is from Bahman 95 to Mordad 96. Data gathering is done through electronic questionnaire whose validity is verified by expert’s views, and its reliability by the coefficient of Chronbach’s alpha. In this research, based on studied literature, 58 indices effective on service innovation in the form of 9 factors at the bank level were identified including communication, strategic, human, market and customers, technologic, organizational and systematic, processing, financial, and learning ones; and their effects were studied by structural equations and Amos software. Also, the present status of service innovation factors in tourism bank indicates that its innovative performance is under alarming level for the communication, processing, organizational and systematic, and financial factors; it is nearly equal to alarming level for strategic, market and customers, and learning ones; and finally the human and technology factors are at higher level of alarming.

Finding the most optimize way to make a portfolio “ feasible “ has been ,and always will be a challenge and concern for those active in investment management industry. For several decades, Markowitz's (1952) Mean Variance Theory (MVT) has been considered as the cornerstone of modern portfolio theory.
The Behavioral Portfolio Theory (BPT) developed by Shefrin and Statman (2000) is often set against Markowitz's (1952) Mean Variance Theory (MVT). In this paper, we compare the asset allocations generated by BPT and MVT without restrictions. Using Tehran Securities Exchange stock prices from the TSE database for the 2012– 2017 period, A sample of 247 companies are listed on the Tehran Stock Exchange data for a period of 5 years was used for statistical analysis . stock prices contained in the TSE database to generate a possible asset allocations via bootstrap simulation.this paper is the study that empirically determines the BPT optimal portfolio.We show that Shefrin and Statman's (2000) optimal portfolio is Mean Variance (MV) efficient inmore than 70% of cases.

This study developed in 8 of the R & D management model to analyze the factors affecting the Pars Khodro Company will pay R & D management. And determines the performance of the company. . -Pymayshy Descriptive research and data gathering instrument was a questionnaire which its validity with the judgment of experts and its reliability was confirmed by Cronbach's alpha and SPSS software. Due to the limited population of the study, the total number method is used to rank the factors affecting the management of research and development in Pars Khodro Company of paired comparisons with ANP techniques using software is used SUPER DECISIONS. The results obtained from the software indicates that financial factors were ranked first and managerial factors responsible for learning and innovation, technological platforms, operating commercial, technical factors, the operating system have won the next rank.