سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
parisa Mohajeri; reza taleblou; samaneh ranjkhah zenuzghi
Abstract
This study employs a vector autoregression approach with time-varying parameters (TVP-VAR) to investigate the spillover of volatility and risk among the stock markets of 13 OPEC and OPEC+ member countries, alongside Gold, Brent Oil, OPEC Oil, and the Dollar index. Daily data spanning from March 1, 2014, ...
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This study employs a vector autoregression approach with time-varying parameters (TVP-VAR) to investigate the spillover of volatility and risk among the stock markets of 13 OPEC and OPEC+ member countries, alongside Gold, Brent Oil, OPEC Oil, and the Dollar index. Daily data spanning from March 1, 2014, to March 1, 2023, is utilized for analysis. Our findings reveal several key insights. First, the average systemic risk within the network of investigated variables has escalated in the years following the onset of the COVID-19 pandemic. Second, among the investigated countries, the stock markets of Saudi Arabia, Kuwait, UAE, Russia, Malaysia, and Nigeria serve as transmitters of fluctuations within the network, while the stock markets of Bahrain, Kazakhstan, Venezuela, Oman, Iran, Iraq, and Mexico act as receivers of volatilities. Third, significant volatilities in Iran's stock market returns originate from idiosyncratic shocks, with variables such as OPEC oil prices and the stock markets of Bahrain, Iraq, and Kuwait playing pivotal roles in explaining these fluctuations. Fourth, approximately 60% of the volatility in gold returns and the dollar index can be attributed to idiosyncratic risks. Fifth, compared to other OPEC+ member countries, the Saudi stock market's volatility exerts a more substantial influence on the volatilities observed in the global oil, gold, and dollar markets.
• سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
reza taleblou; parisa Mohajeri
Abstract
This study investigates the application of recurrent neural network (RNN) models—specifically RNN, long short-term memory (LSTM), and gated recurrent unit (GRU)—in predicting the stock indices of the Iranian energy industry. Using daily time series data from May 1, 2020, to May 1, 2024, the ...
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This study investigates the application of recurrent neural network (RNN) models—specifically RNN, long short-term memory (LSTM), and gated recurrent unit (GRU)—in predicting the stock indices of the Iranian energy industry. Using daily time series data from May 1, 2020, to May 1, 2024, the dataset was divided into a training period (80%) and a testing period (20%). In the first step, the optimal architectures of each model (estimating hyper-parameters) were determined for prediction horizons of 1, 2, 5 (one week), and 20 trading days (one month). Subsequently, prediction errors of the three machine learning models were compared with the linear econometric model (ARIMA) across various forecast horizons. The findings in two areas of cross validations of machine learning models as well as predication error reveal the following insights: First, as the forecast horizon increases, the batch size of optimal prediction decreases for all three machine learning models, and the larger the input training sample size leads to the smaller batch size. Second, in short-term forecast horizons (1, 2, and 5 trading days), machine learning models—particularly LSTM—demonstrate lower prediction errors than ARIMA, while in the 20-trading-day (1-month) forecast horizon, ARIMA's predictive accuracy approaches to the nonlinear machine learning models. Third, forecast accuracy decreases as the horizon lengthens, with accuracy dropping from approximately 98.5% (for a 1-day horizon) to 92.5% (for a 20-day horizon). Finally, selecting the appropriate forecasting method for the stock market indices of energy industries depends on the forecast horizon and data characteristics.
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 1396:Q1 to 1402:Q1, 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” sector and "Shapna" from the refining sector hold the highest (64.75%) and lowest (18.9%) information asymmetry, respectively. IntroductionThe Probability of Informed Trading (PIN) is a prominent metric in market microstructure, utilized to assess the level of information asymmetry by estimating the probability of informed trading. Despite extensive international research on measuring information asymmetry and its applications across various domains, the Iranian academic landscape reveals two significant gaps. First, studies in this field remain limited in number. Second, corrective and generalized methodologies—whether in terms of estimation techniques or the underlying assumptions of the models—have not been sufficiently explored in domestic research.The primary contribution of this study is to address this research gap and provide a more precise representation of information asymmetry levels within the stock market of companies operating in the energy sector. This investigation is structured around three central research questions: First, what differences exist between the average estimated levels of asymmetric information in the energy industry when using the PIN and MPIN models? Second, what is the magnitude of bias in the estimated asymmetric information derived from the PIN model across energy subsectors, including "chemical and petrochemical," "refining," and "electricity, gas, and steam"? Third, which companies in the energy sector exhibited the highest levels of asymmetric information, as estimated by the MPIN model, during each quarter from the first quarter of 1396 to the first quarter of 1402?To answer these questions, asymmetric information will be estimated using the PIN and MPIN models, which are rooted in the extended methodologies proposed by Ersan and Alici (2016) and Ghachem and Ersan (2023). This approach aims to enhance the accuracy and reliability of the findings, contributing to a deeper understanding of asymmetric information dynamics in the energy sector. Methods and MaterialsTo estimate information asymmetry in this study, high-frequency daily stock data from 55 companies operating in the energy sector were collected. These companies are categorized into three subsectors: "chemical and petrochemical," "refining," and "electricity, gas, and steam." The data spans the period from the first quarter of 1396 to the first quarter of 1402 and was sourced from the Tehran Stock Exchange website. The data was subsequently cleaned using Python software. The selection of these 55 companies was based on two critical criteria: the availability of high-quality, high-frequency data (with minimal non-trading days) and the inclusion of a diverse range of companies of varying sizes.At any given moment, a vast number of bid and ask quotes exist at different price levels. Therefore, the initial step involved collecting order data, which amounted to over 30 billiard rows of data for each of the 55 symbols across the 25 quarters under study. Due to the irregular timing of trades, the bid and ask quotes were aggregated into one-second intervals. Subsequently, price data was aggregated using a weighted average, while trade volume data was summed up within each one-second interval. Following this, the traded prices and volumes were matched with the corresponding bid and ask quotes.The second-by-second data for each day were processed using the Lee and Ready (1996) algorithm to identify the origin of each trade (i.e., whether it was buyer-initiated or seller-initiated). Finally, within the frameworks proposed by Ersan and Alici (2016) and Ghachem and Ersan (2023), the parameters of the PIN and MPIN models were estimated, respectively. These parameters were used to calculate the probability of informed trading for each quarter. The likelihood function was constructed separately for each company and each quarter, and the estimation was performed using parallel processing on a Core i9 processor in the R software environment. Results and DiscussionIn Figure (1), the average estimated values of PIN and MPIN for 1,387 quarter/stock pairs are presented, while Figure (2) illustrates the trend of PIN bias alongside the number of layers identified in the MPIN model. The findings reveal the following:Figure (2). Average PIN bias and the number of layers identified in MPINFigure (1). Average values of PIN and MPIN in the energy industry from 1Q:1396 to 1Q:1402 The average PIN ranges between approximately 14% and 37%, while the average MPIN fluctuates between 21% and 51%.The average MPIN values are consistently higher than PIN values across all quarters, with MPIN being, on average, approximately 46% higher than PIN. This observation suggests a higher probability of encountering informed traders when using the MPIN model, which aligns with theoretical expectations.The average values of MPIN and PIN exhibit similar patterns of fluctuation over time. However, the difference between the two is not constant. Although they began the study period with relatively similar values, the gap between them gradually widened. As shown in Figure (2), the underestimation of PIN increased from approximately 5% in the first and second quarters of 1396 to around 12% in the first quarter of 1402. The highest bias was observed in the fourth quarter of 1398, where PIN underestimated information asymmetry by 18%.These results highlight the importance of using the MPIN model for a more accurate estimation of information asymmetry, particularly in dynamic and complicated markets such as the energy sector. The increasing divergence between PIN and MPIN over time underscores the limitations of the PIN model in capturing the full extent of informed trading, especially in periods of heightened market activity or volatility. ConclusionThe estimated PIN values in the energy industry fluctuate between 14% and 37% across different quarters, with an average of 22.9%. Among the three energy subsectors, the refining industry exhibits the lowest level of information asymmetry (20.4%), while the chemical and petrochemical sector shows the highest (23.5%). The electricity, gas, and steam subsector has an information asymmetry level of 23.1%. The estimated PIN values in the Iranian energy sector are higher than those reported by Easley et al. (2002) for the U.S. stock market (approximately 19.1%) and Hwang et al. (2013) for the South Korean stock market (20.1%), but are close to the estimates by Martins and Paulo (2014) for the Brazilian stock market (25%).Given that the level of information asymmetry in Iran is relatively high compared to estimates from other countries and has been increasing over time, and considering that private information is more prevalent in subsectors with smaller market shares (such as electricity, gas, and steam) and smaller companies (e.g., Bepeyvand, Begilan, etc.), it is recommended that the Securities and Exchange Organization of Iran enhance its oversight of smaller companies and industries. These entities should be required to improve transparency by promptly disclosing material information that impacts current and future revenues and costs.Additionally, analyzing the effectiveness of policies, particularly trading restrictions such as volume limits and price fluctuation limits—which are implemented in various countries to reduce information asymmetry and enhance retail investor confidence—could serve as a focus for future complementary research. Such studies would aid in designing and implementing optimal policies to address information asymmetry and improve market efficiency. Keywords: Market Microstructure, Information Asymmetry, Multi-Layer Probability of Informed Trading (MPIN), Hierarchical Agglomerative Clustering (HAC)JEL Classification: C13، G10، G14
• 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.
