Financial Markets

The mutual relationship between monetary and fiscal policies and value at risk is one of the most important topics in the financial economics literature and accounts for the vast majority of empirical studies. Therefore, the main objective of this paper is to investigate the effects of monetary and fiscal policies on conditional value at risk in the financial sectors of the stock exchange, bank and insurance during the years 1995-2017. For this purpose, by quantile regression method and in the form of Adrian and Brunnermeier approach, the conditional value at risk of these three financial sectors is estimated and then by using the seemingly unrelated regression equation approach in panel data evaluated the effect of liquidity money variables. The interest rate on facility payments, the real exchange rate, the government's budget deficit, real GDP growth, and the degree of economic openness are subject to conditional risk. The results of the model estimation indicate the significance of the effect of liquidity money, interest rate on facility payments and real exchange rate variables on conditional value at risk in each of three relevant equations, and real GDP growth variable in the model, Exposure to the conditional value at risk of the insurance sector has a negative and significant effect. Also, the degree of openness of the economy in any of the three estimated equations has no significant effect on the conditional value at risk. 

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this paper, while we introduce the most efficient features, we will show how valuable results could be achieved by the use of a financial time series technical variables that exist on the Tehran stock market. The suggested method benefits from regression-based machine learning algorithms with a focus on selecting the leading features to find the best technical variables of the inputs. The mentioned procedures were implemented using machine learning tools using the Python language. The dataset used in this paper was the stock information of two companies from the Tehran Stock Exchange, regarding 2008 to 2018 financial activities. Experimental results show that the selected technical features by the leading methods could find the best and most efficient values for the parameters of the algorithms. The use of those values results in forecasting with a minimum error rate for stock data.

Monetary environment as the core of financial system has been functionally designed in light of the new set of extensive goals including financial stability, sustainable noninflationary growth, external sustainability, and price stability. A comprehensive monetary policy framework is proposed for Iran which systematically include the new goals, stance variables, instruments, transmission mechanism as well as timely monitoring system. Accordingly, macroeconomic data provides a reliable momentum to evaluate how far the macroeconomic condition is away from the monetary goals in case the data is timely-consistently compiled by policy makers. A wide variety of policy instruments are occasionally applied in the context of the new monetary policy framework by the conventional transmission channels which are technically tracked via monetary condition index, early warning system, leading indicators, and stress tests that give a timely feedback to policy makers to draw contemporaneously a picture of macro prudential stance. Given the prominent share of asset market (housing and capital) in the whole financial and nonfinancial markets in Iran, the monetary policy is empirically required to streamline assets market’s flow of funds instead of extra concentration on broad money growth and lending channel. Meanwhile, balance sheet channel is obviously expected to be more effective against monetary policy stance rather than lending channel in order to achieve monetary goals. In this regard, housing and capital markets are both significantly considered more efficient to finance flow of funds and fiscal deficit. JEL Classifications: E52, E59, G10

This study was designed to explain the Blockchain acceptance indices in Iran's financial markets aimed at identifying different angles for the implementation of Blockchains. The Blockchain acceptance indices were extracted in 4 levels, 12 variables, and 53 indices of related research literature in the field of e-commerce and mobile banking. To validate the research indicators, the Fuzzy Delphi technique was used to refine the indices in addition to the documentary study. The survey was conducted in three stages and the results of each stage were refined. Based on data analysis, 39 indicators were confirmed. The results of this study can provide useful insights for researchers and policymakers of Iran’s financial markets to understand the prerequisites and effects of the Blockchain implementation on financial markets, and thereby, they would be able to change business models to take advantage of Blockchain capabilities in the infrastructure of Iran’s financial markets by considering different aspects.

Financial markets are currently experiencing sharp volatility. Studying how the returns and volatility in one market affect other markets has always been one issue that helps investors and policymakers to make optimal decisions. Given the importance of volatility spillovers in the Iranian financial market, this study aimed to investigate the mechanisms behind the volatility spillovers in the foreign exchange, gold, and stock markets to the oil market in Iran. This descriptive study was conducted using the daily and monthly data from the oil, foreign exchange, gold, and capital markets from 2010 to 2019 and to analyze the data, ARCH and GARCH models have been used. The results of this study showed that the abnormal volatility of the foreign exchange and gold in the previous day positively affects the abnormal volatility of the oil market today, this indicates that money flows in the currency market, spilling over the fluctuations into the oil market. hey also found that the abnormal volatility of the capital market in the previous day negative affects the abnormal volatility of the oil market today, indicating that if money flows in the capital market, which indicates the flow of money in the capital market from yesterday, increasing the transfer of emotions to the current capital market but does not spillover into the oil market and volatility is not transferred into the oil market. Overall, the findings of this study confirmed the positive impact of the foreign exchange and gold markets on the abnormal volatility in the oil market in the short term (daily) and long term (monthly), but did not confirm the positive impact of the capital market on the abnormal volatility in the oil market.

Objective: In today's interconnected global economy, changes in one market can have ripple effects across related markets, making it essential for economic and financial policymakers and experts to accurately predict these mutual impacts. Various methods have been developed to forecast the impact and mutual impressions of financial markets. In this study, a generic framework is proposed for forecasting economic and financial market indicators using the interrelationship of shocks among global financial markets and a system dynamic approach. Methods: To demonstrate the stages of the proposed generic framework and system dynamics modeling, as an example, the study forecasts the Iranian economic and the Tehran Stock Exchange indicators using their interactions with eleven major global financial markets, including London, Tokyo, Shanghai, Frankfurt, Paris, Milan, SIX Swiss, Istanbul, Korea, Bombay Stock Exchanges, and Dubai Financial Market. The New York Stock Exchange index return is used as a stimulant or driver for the other stock exchanges in the model.Results: The results indicate that the proposed forecasting model successfully predicted the Iranian economic and the Tehran Stock Exchange indicators. Furthermore, the study finds that while Iranian exports are sensitive to global financial markets, the sensitivity of imports and production returns to global financial markets is low. Conclusions: The proposed generic framework and system dynamics modeling can provide valuable insights for predicting different economies using their interactions with the global economy and finances.

The unpredictability of the stock market makes it a serious area of study and analysis. With the help of the accumulated information available in the current digital age and the power of high-performance computing machines, there is a great focus on using these capabilities to design algorithms that can learn stock market trends and successfully predict stock prices. The main goal is to create an intelligent system that provides these features for predicting short-term stock price trends to facilitate the investment decision process. To increase the accuracy and productivity of these systems and facilitate the routine of using common-sense knowledge in machine learning systems, developing or enriching knowledge bases and ontology for market modeling will be one of the effective measures in this field. In this research, an attempt has been made to strengthen and enrich the basic ontology created by the authors by using other global ontologies related to the subject of the stock market, and parts of the target space that were not addressed have been added to the ontology. By combining reference ontologies, a level of standardization is also created for the ontology and stability in the representation of concepts and relationships is ensured. In the next step, it has been tried to test the impact of the concepts and relations of the ontology in predicting stock price movements. For this purpose, news in the field of economy is considered as input and a model is created that first filters the textual inputs related to the desired stock symbol and then observes their effect on the price changes of the related stock. After improving the performance and comprehensiveness of the ontology, the study conducted in this report presented a model to measure and prove the effect of the relationships in this ontology on price changes. In practice, according to human limitations and the tools used, this effect was observed and confirmed with a proper level of certainty by checking the economic news.