By demonstrating the inability of standard financial models that are based on perfect rationality, behavioral finance school turned to psychology and behavioral decision knowledge. Behavioral finance means the study of investment behavior by using the ideas and beliefs that investors may act irrationally. According to behavioral finance model, because many factors are involved in investors' decisions and only one of these factors is valuation models, so biases can be seen in investors’ behavior. Using the data from 155 listed firms in Tehran Stock Exchange. This study attempts to investigate the relations between accruals quality, and Conditional volatility. The results showed that accruals quality have an inverse impact on Conditional volatility in Tehran Stock Exchange.

Stock market as a part of the capital market plays a very important role in directing savings to the manufacturing sector in all countries. Today, in the economy of many developing countries, the situation of macroeconomic variables is not consistent with the ascension of stock indices, and in fact the relationship between the economy and the stock has been discontinued. Today, in the economy of many developing countries, the situation of macroeconomic variables is not consistent with the increase in stock indices, and in fact the relationship between the economy and the stock has been discontinued. In the present study, for the prediction of price bubbles, the daily data of 144 companies in the Tehran Stock Center during the period of 1389 (1396) has been analyzed by the generalized autoregressive Conditional heteroscedasticity (GARCH). Based on the results of the data analysis, member firms in the stock center in the years under consideration have been priced bubbles that were higher in the first six months of the year. The factors that triggered price bubbles include political shocks, returns in parallel bubbles, such as oil, currency and gold.

Stock market as a part of the capital market plays a very important role in directing savings to the manufacturing sector in all countries. Today, in the economy of many developing countries, the situation of macroeconomic variables is not consistent with the ascension of stock indices, and in fact the relationship between the economy and the stock has been discontinued. Today, in the economy of many developing countries, the situation of macroeconomic variables is not consistent with the increase in stock indices, and in fact the relationship between the economy and the stock has been discontinued. In the present study, for the prediction of price bubbles, the daily data of 144 companies in the Tehran Stock Center during the period of 1389 (1396) has been analyzed by the generalized autoregressive Conditional heteroscedasticity (GARCH). Based on the results of the data analysis, member firms in the stock center in the years under consideration have been priced bubbles that were higher in the first six months of the year. The factors that triggered price bubbles include political shocks, returns in parallel bubbles, such as oil, currency and gold.

Modeling and predicting stock market volatility using neural network and Conditional variance patterns The fluctuation forecast is one of the most important issues in the financial markets, which attracted the attention of many academic researchers and experts in the field over the past few decades. In this study, considering this necessity, we examine the modeling and prediction of stock market volatility using the combination of artificial neural networks and Conditional variance patterns. In this research, multi-layer perceptron nerve networks (MLP), Conditional variance heterogeneity models (ARCH) and self-regression model and Conditional variance (GARCH) (P, Q) have been used. The statistical population of the study is the Tehran Stock Exchange index for the period of April 2008 to April 2018. The research seeks to reject or confirm the hypothesis that "the use of an artificial neural network and Conditional variance models increases the accuracy of the forecast of stock market fluctuations in the Tehran Stock Exchange relative to the Conditional variance model". The results, confirm the validity of the above hypothesis.

Characteristic of skewness, fat tails and frequency dimension are important features of financial time series that have not been taken into account in classical econometric models. Therefore, in this study, the Bayesian method for Conditional variance heteroscedasticity based on wavelet analysis has been used to investigate the volatility spillover effect and dynamic Conditional correlations in three sub-periods between the daily return data of selected Tehran Stock Exchange (TSE) indices during the period from December 14, 2008 to April 20, 2019. Sub-periods are defined according to the Joint Comprehensive Plan of Action (JCPOA), which includes the pre-JCPOA period, JCPOA period, and the period after United States withdrawal from the JCPOA. The results of the Bayesian DCC GARCH (1, 1) model, with the rejection of the constant Conditional correlation hypothesis versus the dynamic Conditional correlation hypothesis based on posterior marginal distribution in all subsections, indicates that the impact of shocks on the volatility of stock returns in wavelets and sub-periods are not the same. Also, Bayesian dynamic Conditional correlation graphs are recommended for each sub-periods and in each wavelet, a different stock for a suitable investment. The main idea used in this research can be used in the calculations of asset risks and the selection of optimal asset portfolios.

To study price volatility and vertical market integration in the livestock and poultry vertical market levels, the multivariate volatility models, including constant (CCC) and dynamic (DCC and VCC) Conditional correlation models were estimated using monthly time series data of broiler feed, chicken, broiler, hay, sheep, calf, beef and mutton prices from April 1997 to March 2014. Estimating the Conditional correlation models showed that the constant Conditional correlation assumption is a major restriction for the variables under our investigation. Except for retail and wholesale price correlations in poultry market which is constant over time the dynamic Conditional correlations are significantly different from the constant Conditional correlations, so that the dynamic Conditional correlation has experienced strong fluctuations in all cases. The results suggest that in the livestock and poultry markets price information's transmit more from the input level to the retail and wholesale levels than the reverse direction. Moreover, the correlation between the wholesale and retail market levels was larger which means stronger price relations between these two market levels. Estimated volatility models indicate that shocks and news rather than past volatility have greater impact on the current volatility in Iranian livestock and poultry markets. This indicates the necessity of news management in Iran.