Financial Economics
Reza Taleblou; mohammad mehdi bagheri todeshki
Abstract
This paper investigates the impact of sentiment as a critical risk factor in the capital market, leading to behavioral deviations in the pricing of financial assets. We propose an estimation of the asset pricing model based on the Stochastic discount factor (SDF) framework, incorporating both traditional ...
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This paper investigates the impact of sentiment as a critical risk factor in the capital market, leading to behavioral deviations in the pricing of financial assets. We propose an estimation of the asset pricing model based on the Stochastic discount factor (SDF) framework, incorporating both traditional and behavioral approaches. By extending the consumption-based asset pricing model (CCAPM) and introducing sentiment into the utility function through the Euler equations and the generalized method of moments (GMM), we analyze the Tehran Stock Exchange.To quantify sentiment, we utilize the market turnover sentiment index as a reliable indicator. Our study covers the period from 1390 to 1399 and encompasses 18 stock exchange groups, consisting of 63 listed companies on the Tehran Stock Exchange.The results indicate that the behavioral SDF model offers higher consistency and efficiency compared to the traditional model, aligning closely with the dynamics observed in the Tehran Stock Exchange. Moreover, the coefficient of sentiment proves to be statistically significant. In terms of risk, the behavioral model demonstrates higher coefficients than the traditional model. Interestingly, both models suggest that market participants exhibit a high time preference factor and demonstrate patience in their investment behavior.
Financial Economics
Reza Taleblou; Parisa Mohajeri; Abbas Shakeri; teymoor mohammadi; zahra zabihi
Abstract
Achieving the correct insight into the structure of connectedness and the spillover of volatilities between different stock exchange industries plays an important role in risk management and forming an optimal stock portfolio. Also, the analysis of inter-sectoral connectedness helps policy makers in ...
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Achieving the correct insight into the structure of connectedness and the spillover of volatilities between different stock exchange industries plays an important role in risk management and forming an optimal stock portfolio. Also, the analysis of inter-sectoral connectedness helps policy makers in designing policies that stimulate economic growth and implementing preventive measures to curb the propagation of systemic risk. In this regard, this article tries to use the data of 3370 trading days during the period of 1388/07/01 to 1402/06/31, encompassing 20 stock market industries (which constitute more than 80% of the Iranian stock market) and applying the connectedness approach based on the vector autoregression model with time-varying parameters (TVP-VAR), to estimate the systemic risk and volatility connectedness of the stock market network. In addition, we implement the minimum connectedness approach in the optimal stock portfolio and compared its performance with two other conventional approaches. The findings reveal that, first; the systemic risk in Iranian stock market is significant and has reached unprecedented figures of 80% in the last three years. Second, the four major export industries (petrochemicals, metals, mining and refining) experience the strongest pairwise connectedness, and among them, base metals appear as one of the most important transmitters of volatilities to the entire stock network. Thirdly, the stock portfolio based on the minimum connectedness method, compared to the minimum variance and minimum correlation methods, shows a better performance based on the criteria of cumulative return and hedge ratio efficiency.
Information and communication technology economy
Reza Taleblou; Teymor Mohammadi; Hossein Aghaei
Abstract
This article examines the theory of network-based economics (two-sided markets) and considers payment cards in Iran as a case study. Based on the monthly data of the Central Bank of Iran and the payment cards of electronic networks in Iran (SHAPARAK) from Dec. 2014 to March 2019, demand elasticity and ...
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This article examines the theory of network-based economics (two-sided markets) and considers payment cards in Iran as a case study. Based on the monthly data of the Central Bank of Iran and the payment cards of electronic networks in Iran (SHAPARAK) from Dec. 2014 to March 2019, demand elasticity and monopoly power have been estimated. The results show that the elasticity of the cardholder and the card acquirer with respect to the interchange fee rate is 0.55 and nearly 1 (1.04), respectively. These results show that the cardholders have smaller elasticity to interchange fee compared to acquirer of payment cards. The estimated market power indicates that the payment card network in Iran is highly monopolistic. The payment card platform in Iran (SHAPARAK) does not impose its market power on the buyer side (card holders) and subsidizes them in order to create balance in transactions, but this platform impose exclusive power on merchant side (card acquirer) of payment cards. With this policy, card holders are attracted to the market which increase trading on the platform and platform profits. In general, on the buyer side of Iran payment card we have P = MC but on the merchant side P> MC. Therefore, the regulatory authorities in Iran must regulate SHAPARAK market power.
Reza Taleblou; Mohammad Mahdi Davoudi
Abstract
In this paper, in order to calculate portfolio market risk of 10 selected industries indices in Tehran Stock Exchange, two models of Value Risk (VaR) and Expected shortfall (ES) have been used. Different models of multivariate GARCH and various Coppola models have been used in order to estimate the volatility ...
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In this paper, in order to calculate portfolio market risk of 10 selected industries indices in Tehran Stock Exchange, two models of Value Risk (VaR) and Expected shortfall (ES) have been used. Different models of multivariate GARCH and various Coppola models have been used in order to estimate the volatility of the portfolio and nonlinear correlation of asset portfolio. Backtesting has been done by Kupiec, Christoffersen, Engle and Manganelli and McNeill and Ferry tests. Results show that the DCC-GARCH model by t-Student distribution compared to other competing models has the best results in estimating volatility of the asset portfolio. Also among all Copula models reviewed in this paper, t-student copula model has shown better results for estimating asset dependence. Finally, the results of backtesting of different models showed that both the DCC-GARCH model with t-Student distribution and DCC-GARCH-Copula with t-Student distribution have acceptable results in estimating VaR and ES. However, the Lopez and Blanco and Ihle tests showed that the DCC-GARCH model with t-Student distribution compared to the DCC-GARCH-Copula model with t-Student distribution gives a more accurate and efficient estimate of the VaR and ES of asset portfolios.
Reza Taleblou; mohammad mahdi davoudi
Abstract
In recent years, by using extreme value theory (EVT), researchers have estimated the market risk for rare events (crises) more accurately. This paper examines the different methods of measuring market risk at different levels of reliability. According to the assumptions of the EVT methods, measuring ...
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In recent years, by using extreme value theory (EVT), researchers have estimated the market risk for rare events (crises) more accurately. This paper examines the different methods of measuring market risk at different levels of reliability. According to the assumptions of the EVT methods, measuring the effects of the financial crises on the value of assets requires a lot of time-series observations. Therefore, this paper has used four indices: total index, industry index, the first market index and the second market index of the Tehran stock exchange. The backtesting results showed that among the various methods, semi-parametric approach or the EVT approach in comparison with parametric (EWMA, MA, GARCH) and nonparametric approaches (Historical simulation) is more efficient and has a higher level of reliability. Also HS method shows acceptable results at high confidence level, while in calculating the value at risk in the 0.90 and 0.95 confidence levels, parametric methods (EWMA, MA, GARCH) provide more reliable results. Also the richness of the dynamics of GARCH and EWMA models are much more than the other models. In the next step by incorporating various models, the three models EWMA-EVT, GARCH-EVT and AWHS were made. Backtesting these three patterns showed that, AWHS and EWMA-EVT have provided the best results among various patterns, and have provided acceptable adequacy in estimating the value at risk at all levels of reliability. However; GARCH-EVT model shows acceptable results only in 0.999 reliability level.
