macroeconomic variables

Non-financial data may refer to variables that represent the dimensions of the company's management. Company size and industry are other non-financial dimensions used when assessing business risk. In this regard, the purpose of this study is to explain the effect of micro and macro factors (specific characteristics of the company, industry characteristics, corporate governance, ownership structure and macroeconomic variables) on business risk in listed companies on the Tehran Stock Exchange. In order to achieve this purpose, 1752 firm-year (146 companies for 12 years) observations collected from the annual financial reports of listed companies on the Tehran Stock Exchange during the period 2007 to 2018 have been tested.Findings show that firm size, firm liquidity, firm profitability, firm growth opportunity, firm size, number of firms in industry, board independence, existence of audit committee and management ownership have a negative effect on business risk. while debt ratio and the exchange rate have a positive and significant effect on business risk. The results also show that statistically, firm life, industry decentralization, board size, duality of CEO duties, board financial expertise, audit committee size, audit committee independence, audit committee financial expertise, ownership type, Institutional shareholder ownership, major shareholder ownership, government ownership, interest rates and oil price growth have no effect on business risk.

The main purpose of this study was to investigate the effect of macroeconomic variables on unobserved systematic risk using the Kalman filter. Systematic risk indicates the degree of dependence between changes in share prices and changes in the market index. However, the amount of systematic risk can be very different from the expected amount, due to the confusion in stock price changes, resulting from emotional transactions, overreactions and price manipulation. Therefore, it is necessary to control the effects of these disturbances in measuring systematic risk. The method of the present study is descriptive-correlational which was performed using statistical methods to examine the relationships between variables based on Ives software. To analyze the data in this study, it is suggested to use Kalman filter. Also, filtered and turbulent values have been used under the heading of unobserved systematic risk. According to the obtained result, it can be said that all variables have a significant relationship with the systematic risk not observed in the model. Then, using data analysis, the hypotheses were examined. The results obtained in relation to statistics and a significant level show the confirmation of all hypotheses in terms of the impact of economic variables on the components of inflation, economic growth, exchange rate, stock market index and volume. Money showed unprecedented systematic risk. Also, the effect of different variables and finally the estimation of coefficients showed that the highest coefficient among the variables is related to inflation index and stock market.

Since financial developments that are not driven by fundamental factors of monetary policy may have a large impact on the economy, policymakers have placed significant emphasis on the financial conditions index over the past recent years. Policymakers must maintain a vigilant watch over financial conditions, as their significance becomes increasingly significant. The construction and use of the financial condition index include three issues, which are: a) the selection of financial variables to enter the financial index, b) weights used to relate financial variables to the index, and finally c) the relationship between this index and the macroeconomy. There are many reasons for the changeability of these three cases over time, which can be discussed about the reasons for their occurrence and effect on the results. Many changes affect the way a financial index is made. Therefore, in this research, the goal is to design an index of financial conditions using time-varying dynamic models to improve the forecasting of macroeconomic variables.

The process of implementing the conceptual model can be explained in the following steps. First, extracting the desired variables to be used in the desired models (for this purpose, we have used monthly data during the years 2001 to 2021 for 19 financial variables and 5 macroeconomic variables). It should be noted that since all the variables must be in the form of rates - if necessary, all the variables were converted into growth rates. Also, all the variables were examined from the dimension of stationary, and the problem of their stationary has been solved. Second, the desired models were calculated to predict macroeconomic variables. Using time-varying models, dynamic averaging models, and dynamic selection models, the financial condition index was constructed in such a way that this index could include various variables to adopt different coefficients from its previous and subsequent periods and challenge constant parameter and variable models. Third, the predictive power of each of the proposed models in the estimation of macroeconomic variables (sum of squares of prediction error) was analyzed and estimated. Fourth, financial index extraction was done based on the model selected in the third step.

The findings suggest that employing models that solely consider variability in the coefficients (without accounting for variability in the variables) leads to enhanced predictions of the unemployment rate compared to vector autoregression models and vector autoregression models incorporating the basic component. In addition, by moving from the generalized factor vector autoregression models and the time-varying generalized factor vector autoregression and the generalized factor time-varying vector autoregression, in which only the variability in the parameters is included (without considering the variability in the variables) towards the models that consider the variability in the parameters both in a Bayesian and dynamic way, a reduction will occur in the forecast error in the variables of the consumer price index, liquidity, monetary base, and gross domestic product. However, the amount of improvement in each of the variables is different from each other.

This study employed time-varying models to derive the financial condition index, aiming to provide the most accurate estimation of macroeconomic variables. The findings demonstrate that the utilization of these models outperforms other approaches in forecasting a majority of the macroeconomic variables

The Risk-Adjusted Return on Capital (RAROC), as a modern performance measure, is introduced in comparison to traditional performance measures and has been calculated for all the banks listed on the Tehran Stock Exchange and Iran Fara Bourse, based on a new method extracted from earlier studies. The research period is 8 years, from 2012 to 2019. Assessing the effect of macroeconomic variables on this performance measure is another aim of this research. In doing so, RAROC has been estimated by using the Net Income, Expected Loss, and Supervisory Equity of banks. At the next stage, some macroeconomic variables have been selected to run the model. The impact of these variables on RAROC is investigated by a Multiple Linear Regression Model and Panel data analysis. Based on the results, the Inflation rate, the growth of currency exchange rate to Consumer Price Index (CPI) ratio and Liquidity Growth affect RAROC. Except for the inflation rate which has a reversed effect on the dependent variable, others have a straight impact on RAROC.

Study of the comovement between financial markets plays a crucial role in increasing the performance of trading strategies. Due to the strong dependence of Iran economy on oil prices and the fluctuation of the dollar exchange rate in the open market, it is very important to study these macroeconomic level factors on stock market performance for portfolio management. In this research, using time-frequency domain analysis, while discovering the interdependence between financial markets, capital turnover trends in Tehran stock exchange are analyzed and the effect of exchange rate fluctuations and the price of the OPEC oil basket on the indices including TEPIX, industry, banking, automobile, and oil products are studied. The results show that the longer the investment horizon, the stronger this effect is, and the increase in the exchange rate causes the index to increase in order for the market to prosper more, while there is a weak interdependence between TEPIX and oil price. During the investment horizon of 4 to 9 months, any increase in USD encourages shareholders to trade more, while in similar conditions, the increase in oil prices, except in the banking and the petroleum sectors, cause money to flow out of the market in other sectors.

The purpose of this study is to provide a model for predicting the factors affecting the stock price Crash Risk in the Tehran Stock Exchange. In addition to a comprehensive review of the thematic literature related to the stock price crash risk, in the qualitative section, 12 people through theoretical sampling method, Selected among capital market experts and then by collecting the required data by means of document reference and interview tools to extract the factors affecting the risk of stock price crash risk using MAXQDA18 software and to extract the model from the structural equation model And PLS software has been used. Statistical population in the quantitative part, a sample of 100 companies was selected from the companies listed in the Tehran Stock Exchange between 2009 and 2019. The results show, it is possible to provide a model to predict the factors affecting the stock price crash risk and Financial variables, business strategies, managerial ability and information asymmetry as internal factors and macroeconomic variables and political relations and social responsibility as external effective factors on Stock Price Crash Risk and respectively social responsibility in the first priority, business strategies in the second priority, macroeconomic variables in the third priority, managerial ability in the fourth priority, political communication in the fifth priority, variables Financial in the sixth priority and information asymmetry in the seventh priority affect the stock price crash risk.

The cost of capital is one of the most significant criteria in making financial decisions as well as evaluating the financial performance of management. This criterion affects the internal variables of the company and at the same time, it is affected by the environmental factors of the company, especially the macroeconomic variables. Considering the recent major changes of macroeconomic variables in our country, the aim of this study was to investigate the long-term and short-term effects of macroeconomic variables on the cost of capital. Using data from 219 companies listed in Tehran Stock Exchange from 2006 to 2016, five research hypotheses were tested and analyzed by using Autoregressive Distributed Lag (ARDL) method. The findings of the study based on the results of long-run relationship estimation show that interest rate coefficients, inflation rate, and oil price change rate are significant as expected and these variables have a positive and significant effect on company capital cost. So, in the long run, by increasingthe amount of these variables, the corporation cost of capital increases, and by reducing them, the cost of capital can be reduced. Likewise, in the short run, interest rate variable with one period delay, inflation rate variable with three periods delay, exchange rate variable with four periods delay, liquidity variable with four periods delay, and oil price variable with two periods delay have shown their impact on corporate capital cost. Therefore, although the period of impact of these variables is not the same, they have a positive and significant impact on corporate capital cost.

Market power specifies how firms in a market influence prices, and reveals the level of competition in the market. This paper investigated the impact of market power and macroeconomic variables effect on banking deposits in Iranian deposit money market using dynamic Bresnahan-Lau’s and Bayesian model averaging approach for the 18 bank in the Iran industrial banking sector. Annual data for the period of 2006- 2016 has been collected from annual financial statements of Iranian banks, Statistical Centre of Iran, Monetary and Banking Research Institute of Iran and central bank of Iran. By applying BMA approach the main macroeconomic variables have been determined from 8 variables. The results of model estimation indicated that market power for its 18 Iranian banks were 0.58 and the macroeconomic variables of liquidity money has negative and other explanatory variables such as fixed assets, growth of GDP, first order lag of deposits, deposits rate and cross effect of GGDP with deposits rate have positive posterior average on banking sector's deposits during the period of study.

Recognizing variables which are able to explain stock price movements is a very important matter for the capital market activists. Three main groups of variables that seem to have a significant role in this explanation are: 1) Fundamental Variables 2) Historical Stock Price Returns 3) Macroeconomic Variables. This research investigates the relationship between these three groups of variables and stock price returns on the listed companies of Tehran Stock Exchange. Moreover, the complementary role of fundamental variables and historical stock price returns in explaining stock price movements is investigated. Eventually the difference between the roles of fundamental variables and historical stock price returns in large and small firms is studied. According to the results of the present research there is a significant relationship between all three mentioned groups of variables and stock price movements and also fundamental variables and historical stock price returns are complementary variables for explaining stock price movements. Moreover, the growth variable is more important in small firms compared to larger firms for interpreting stock price movements.

Financial markets especially capital market can make strong connections with other parts of the economy. After 2007/2008 financial crisis and global extensive economic recession, the economists show interest in the financial markets function again. The purpose of this study is to design and calibrate a Dynamic Stochastic General Equilibrium new Keynesian model with Stock market dynamism to investigate the stock market channal effectiveness mechanism on macroeconomics variables. So an open DSGE model containing households, firms, banks, government and central bank was designed and after log-linearization, then the model’s parameters were calibrated using quarterly data 1996:3-2013:2 and experimental studies results. This study shows that a negative shock to stock price index in the DSGE model via financial accelerator and bank capital channel will result in decrease production, consumption, investment, deposits and inflation and therefor the macroeconomic variables such as consumption, investment and production have stronger relationship with stock market dynamism

This paper attempts to Expression and design a model to forecast the exchange rate shocks and stress testing of the currency in Iran . In other words, what factors influence the currency shock and is there the shock predictability in the currency market. On the other hand, are there in critical condition (shock) the ability to predict currency risk through stress tests? The research method is descriptive-analytic and data collection library using econometric regression model using econometric software Eviews is carried out. The research is method in this study (time series modeling) . The results suggest that based on estimations made clear that the exchange rate shock predictable feature of the model is capable MGARCH. . In other words, using multivariate GARCH model and Conditional value at risk predictability of the exchange rate shock and influence the variable of Macroeconomic variables has been clarified. Finally, using the Back Testing the validity and effectiveness model was estimated and validation tests with stress tests to estimate the shock has been in critical condition.

Stock Exchange as the most important element of the capital market, is affected by economic conditions. Returns and Exchange price of the exchange, is the result of the economic situation and macroeconomic variables. Also, index of Stock Exchange, which reflects the general state of market, always, has been affected by macroeconomic variables. The main objective of this study is to investigate the relationship between the index of Stock Exchange and macroeconomic variables. For this purpose, quarterly data related to the index and macroeconomic variables during years 1992-2012 as a time sequence are used. Method used in this research is Autoregressive model. Distributed Lag (ARDL) is. Phillips - Perron test showed that, all variables in the lower bound has a unit root with Distributed Lag, but at the upper bound they are static. The results of Granger causality error correction model, represents a long term equilibrium relation and cointegration between the index and variables of consumer index, money supply, exchange rates, GDP, liquidity, governmental payments, coin price and manufacturer index prices.

The relation between macroeconomic variables and stock market index has been vastly investigated in previous studies. To contribute to the literature, this study employs the VARX-DCC-GARCH econometric model. The advantages of using this new model are the consideration of variable change effectiveness in different moment levels (mean and variance of changes), inclusion of oil price as an exogenous variable that has short-run and long-run effects in this model, and the consideration of time series correlation between volatility of the variables. This model is estimated for the period of 1381 to 1391 by using Iran economic and the Tehran Stock Exchange monthly data. Results show that exchange rate, inflation and oil price have a positive effect on stock index in the long run, and the exchange rate has the strongest effect. Also in the short run horizon, oil price shocks have stronger effect on the stock index. Estimated time-varying correlations between variables volatility show that exchange rate volatility has a positive effect on stock market volatility. This correlation is reinforced during 1387 to 1391. In addition, inflation volatility has weak positive correlation with stock index volatility, while oil price volatility does not show any significant effect on stock index volatiliry.

Dividend policy is one of the most controversial financial issues. Moreover, decisions about dividend policy are taken by corporate executives and shareholders with comprehensive consideration of macroeconomic factors and financial risks. Thus this study uses panel data econometric model to examine the impact of financial risk and volatility of macroeconomic variables on dividend policy in selected companies listed on Tehran Stock Exchange during the period 2001-2012.The results of this study indicate that systematic risk has positive impact on dividend, while non-systematic risk has negative impact. Other obtained results indicate that economic growth and inflation volatility have insignificant impact, while the impact of volatility on dividend in negative.

An empirical analysis is carried out establishing the existent relationship among the stock market (Beta) and (a) macroeconomic and (b) accounting informations. Regression analysis, using static and dynamic panel data estimations, is applied to a sample of 61 listed Iranian firms, between 2001and 2010. Systematic risk is estimated through OLS, EGARCH and M-GARCH models and use TEDPIX index (Tehran Stock Exchange Dividend & Price "total return" Index) as market portfolio.
The results show a connection between the systematic risk and the independent variables. The best models are found when we estimate the systematic risk using EGARCH model and applying a combination of macroeconomic and accounting variables. The most important variables explaining the systematic risk are Size (SZ), Growth Rate (GR) and Debt Ratio (DR) from accounting variables and Gross Domestic Product (GDPI),inflation (Change in CPI index), Legal interest rate (LINT), Oil price (Oilprice) and Exchange rate (EXCR). The results vary using different models and might be different if we use a different sample.