سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
parisa Mohajeri; Reza Taleblou
Abstract
The Probability of Informed Trading (PIN) is one of the important measures of market microstructure that is generally used to estimate the level of information asymmetry. Estimating PIN can be challenging due to boundary solutions, local maxima, and Floating Point Exceptions (FPE). Additionally, the ...
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The Probability of Informed Trading (PIN) is one of the important measures of market microstructure that is generally used to estimate the level of information asymmetry. Estimating PIN can be challenging due to boundary solutions, local maxima, and Floating Point Exceptions (FPE). Additionally, the prevailing assumption of the existence of only one information layer per trading day in PIN is inconsistent with the real-world empirical evidence and exposes it to a considerable underestimation bias. In this paper, we estimate information asymmetry for 55 listed companies in the energy sector during the period from 2017:Q1 to 2023:Q2, utilizing the Multi-Layer Probability of Informed Trading (MPIN) model introduced by Ghachem and Ersan (2023). The findings indicate: First, the assumption of a single information layer is satisfied for only 2.67% of the 1,200 stock/season observations, which implies the necessity of using MPIN to estimate information asymmetry. Second, the use of PIN not only leads to significant underestimation bias, but also provides an inaccurate picture of the ranking of companies from the perspective of information asymmetry. Third, the energy sector faces an average information asymmetry of 34.4%, and estimations reveal that private information reached its peak in the summer of 2020, exceeding 49%. Fourth, the symbols "Bepeyvand" from the electricity, gas, and steam sub-sector and "Shapna" from the refining sub-sector hold the highest (64.75%) and lowest (18.9%) information asymmetry, respectively.
• سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
Sarah Akbari; Teymour Mohamadi; Hamid Reza Arbab; Reza Taleblou
Abstract
Oil prices and other oil-products prices are connected to each other and their price volatilities are parallel. Firms which are using crude oil in their products are facing a risk of price volatility which has different reactions in each era and is known under different oil regimes. For example lubricant ...
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Oil prices and other oil-products prices are connected to each other and their price volatilities are parallel. Firms which are using crude oil in their products are facing a risk of price volatility which has different reactions in each era and is known under different oil regimes. For example lubricant industry is completely connected to the oil price. With this philosophy when the economy faced volatility the market players faced loss and so to overcome this issue they began to hedge themselves with another commodity. This hedging process in different regimes has different rates. So there is a need to introduce a new model. From the work of Hamiltonian (1989) oil price has its own volatility and regimes so to this attitude there is an effort to calculate an efficient hedging ratio with regime switching dynamic constant correlation. In this article, monthly data of oil and gold prices for about 10 years from 2010 till 2020 is used and the model is programed with MATLAB. The result showed that the efficient hedge ratio for the first regime (first major change in price of two markets) is 66 percent and the second (second major change in price of two markets) one is 26 percent.
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
parisa Mohajeri; reza Taleblou; Fatemeh KhanAhmadi
Abstract
Firm investment is one of the important financial decisions in the economy, which affects the value of companies and the wealth of shareholders, which can result in increasing welfare. Despite neglecting the effects of uncertainty in traditional investment theories, modern theories have introduced various ...
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Firm investment is one of the important financial decisions in the economy, which affects the value of companies and the wealth of shareholders, which can result in increasing welfare. Despite neglecting the effects of uncertainty in traditional investment theories, modern theories have introduced various mechanisms for the impact of uncertainty on investment expenditures. Using the daily data of oil prices and the data of 131 companies listed on the Tehran Stock Exchange market during the period of 2008-2020, the factors affecting the investment of the companies are identified by emphasizing the oil price uncertainty. For this purpose, in the first step, the stochastic volatility model in the framework of the space-state approach is the basis for estimating the oil price uncertainty, and in the next, according to the results of the Hausman endogeneity test, the instrumental variable method is used to estimate the coefficients of the variables affecting investment. The findings indicate that first, the volatility of oil prices has no significant effect on investment. Second, firm size, profitability, inflation, and Tobin’s Q affect investment positively and significantly. Third, the financial leverage, which is reflected in the capital structure polices, has a significant negative effect on investment, meaning that more focus on debt financing leads to less corporate investment expenditures.
Reza talebloo; Hossein Sheikhi
Abstract
he purpose of this paper is to test the CAPM and APT pricing model for pricing petrochemical companies in Tehran Stock Exchange. In this regard, seasonal data related to stock returns of 18 active chemical and petrochemical companies in the stock market and some important macroeconomic variables as risk ...
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he purpose of this paper is to test the CAPM and APT pricing model for pricing petrochemical companies in Tehran Stock Exchange. In this regard, seasonal data related to stock returns of 18 active chemical and petrochemical companies in the stock market and some important macroeconomic variables as risk factors in the period 1395-1386 were used. First, the CAPM was tested using the GRS test and then by Fama and Macbeth tests. Then, the factor model for the APT test was using factors including real exchange rate, total stock returns, oil returns, yields of the price index Chemical and petrochemical products, risk-free returns, inflation rate, asset risk, GDP volatility, SMB, and sanction factor.
