نشریه اقتصاد محاسباتی
سال دوم شماره 2 (بهار 1402)

The purpose of this study is to experience the stock prices crash and the factors affecting it in companies registered in the Tehran Stock Exchange in the period of 2008-2019. For this purpose, the generalized method of movements (GMM) was used. The presence of incorrect pricing in assets usually causes a sudden drop in prices and the fall of the stock market. Therefore, identifying the factors affecting incorrect pricing can make prediction possible and help market players to more accurately predict the future return of stocks and timely detect the formation of a price bubble in the composition of the stock portfolio. Keep less risk and thus reduce the risk of falling prices. The data used in this research have been reviewed monthly. The findings of the research show that there is a positive and significant relationship between stock price volatility and the risk of falling prices and future stock returns. Also, there is a positive and significant relationship between the number and volume of transactions and the risk of stock prices crash. The data used in this research have been reviewed monthly. The findings of the research show that there is a positive and significant relationship between stock price volatility and the risk of falling prices and future stock returns. Also, there is a positive and significant relationship between the number and volume of transactions and the risk of stock prices crash.

Liquidity is an important indicator for the development of the capital market because it shows that a capital market with high liquidity and improved capital allocation leads to an increase in the long-term prospects of economic growth. The present research examines the relationship between financial market illiquidity impulses and economic growth dynamics in the period of 2008:4-2021:4. Amihud index (2002) has been used to extract the illiquidity index of the stock market. Using generalized Dickey-Fuller unit root (ADF) and Heggie tests, the normality of the time series has been confirmed. The results of threshold vector autoregression (TVAR) estimation show that the effect of illiquidity on inflation in high and low regimes is positive and significant, and also the effect of illiquidity on inflation is greater at lower levels of illiquidity. The effect of illiquidity on economic growth is negative. On the other hand, lack of liquidity at higher levels can cause more fluctuations in economic growth. Therefore, in the field of economic growth management, it is necessary for policymakers to pay special attention to the liquidity situation in the financial markets. In severe recession situations, reducing illiquidity in financial markets by using market stabilization funds can prevent recession and improve economic growth.

There is a lot of evidence today that informal economic activities are growing in the world, and therefore Study of shadow economy, as one of the main components in orienting and defining economic policies, has always been a concern for economists, but in this field, the impact of development of financial markets and internal credits on the volume of the shadow economy has been dealt in less.Development of financial markets play a considerable role in providing required credit to entrepreneurs and business corporations so they impress shaping economic activities and continual of economic transactions. This research aims in investigating the effects of shocks in financial development and domestic credit on the shadow economy, conducted using the VAR auto-regression model for country (Iran) from 1372 to 1395 (1993 to 2016). Instantaneous response functions and variance analysis shows the effect of bank credit shocks on the shadow economy. The analysis of the results suggests that financial development, the ratio of private sector domestic credit to GDP and the ratio of financial sector domestic credit to GDP in studied years, impose a long time negative and significant effect on the country shadow economy.

The purpose of this paper is to analyze and measure the systemic risk between the cryptocurrency and real currencies using the conditional risk exposure value approach and the expected marginal loss. In this study, statistical data of real and virtual currencies during the years 2015-2021 have been used. For this purpose, systemic risk indices have been calculated using CoVaR and MES indices and then the correlation between systemic risk of currencies has been evaluated. In this study, the statistical data of the currencies of the exchange rate of the pound to the dollar, the exchange rate of the yuan to the dollar, the exchange rate of the lira to the dollar, the exchange rate of the euro to the dollar, bitcoin, atrium, ripple, litcoin and atrium based on daily price returns Currencies and real currencies were used. The results showed that there was a correlation between systemic risk indicators for the studied currencies and virtual currencies had a lower systemic risk index than real currencies. The purpose of this paper is to analyze and measure the systemic risk between the cryptocurrency and real currencies using the conditional risk exposure value approach and the expected marginal loss. In this study, statistical data of real and virtual currencies during the years 2015-2021 have been used. For this purpose, systemic risk indices have been calculated using CoVaR and MES indices and then the correlation between systemic risk of currencies has been evaluated.

The purpose of this article was to predict the price of electricity in the cash and cash markets and to design the optimal model of electricity sales in the aforementioned markets with the Copula function approach. For this purpose, daily information was used in the period of 1396-1401. In order to forecast, time series models and OLS, GARCH and Copula approaches were used. The results showed that trigonometric functions can well explain the behavior of electricity prices, which is caused by the seasonal behavior of electricity prices during one-year periods. In the random part, the estimated values show that the random component has an average of almost zero and the speed of returning to the average in prices is high. The average of the shocks, their negativity and variance are very small. The small average values of the shocks actually show that the shocks that occurred in the price of the electricity market in Iran are very insignificant and more importantly, these shocks were more of the negative type. Regarding the optimal strategy when entering into futures transactions, our advice to players is to use the Copula-Garch method to calculate the optimal ratios for risk hedging, for two reasons. The need for risk hedging is less and as a result the transaction cost is lower, secondly, due to the existing restrictions and especially the low liquidity in energy exchange transactions, it is practically possible to cover more risk than the cash position

Some market participants believe that with the increase in market volatility and the decrease in the number of small shareholders, a type of stability will be created in the decision-making process of bank management that only considers the interests of majority shareholders. This disrupts market activities and reduces the cash asset reserves of banks based on optimal risk incentives. The aim of this study is to investigate the effect of risk incentives on the market value of banks. This research is a library-based analytical study based on panel data analysis. Financial information from 10 accepted banks in Tehran Stock Exchange during the period from 2016 to 2021 was examined. The results showed that liquidity fluctuations as one of the risk incentives had a positive and significant effect on the market value of banks during the studied period. Financial performance fluctuations also had a positive and significant effect on the market value of banks.

In this research, using the method of fuzzy neural networks, the price of Bitcoin is predicted. In order to identify the appropriate criteria in this research in order to predict the price of Bitcoin, we have used previous studies and researches in this field in the first stage. In the following, using interviews with experts and experts in this field, the available information about Bitcoin became the final factors. Research information was collected using related sites and identified criteria. In this way, we first normalized the collected data. In the next step, by entering the normalized information into the MATLAB software and using the designed toolbox and using the fuzzy neural network method, Bitcoin price was predicted. In this way, 60% of the input data, which includes 1330 data, was considered as training data and 40% of the data, which is 887 data, was considered as testing. The research results show high accuracy prediction using the proposed method. As the error was considered in two cases, a small value was calculated for the error of the method.