Efficiency in agricultural production is indicative of the efficiency level of farm households in their farming activities. Farmers in developing countries do not make use of all the potential technological resources, thus making inefficient decisions in their agricultural activities. Herein, technical efficiency in relation with the production of three types of rice crop (Boro, Aus and Aman) was evaluated, with some determinants of technical efficiency identified, in Bangladesh. It was attempted, throughout this study, to access the status of technical efficiency in rice production in Bangladesh for panel data while using the Stochastic Frontier Production Model with either of truncated normal or half-normal distributional assumptions. Both time-variant and time-invariant inefficiency effects Models were estimated, one at a time. Collected data from agricultural sector pertaining to three main rice crops in Bangladesh for the period of 1980 to 2008 were made used of throughout the study. The results revealed that technical efficiency gradually increased over the reference period with the half normal distribution being found preferable to the truncated normal distribution as regards the technical inefficiency effects. The value of technical efficiency was found high for Boro rice while low for Aus in comparison with Aman rice in Bangladesh for both distributions in either of time-variant or invariant ones. It was observed that the most efficient rice production system has occurred for the case of Boro with a technical efficiency of 0.98. Year wise mean technical efficiency increased during the reference time periods.

Using the monthly data of the returns of 5 assets during 05/31/2011 to 02/28/2021, the volatilities of Iranian asset markets have been Modeled in this paper. Factor multivariate Stochastic Volatility Model in the framework of space-state approach is the basis for decomposing the asset market Volatility into two components, “, Volatility rooted in latent factors”,and “, idiosyncratic Volatility”,and estimating time-varying covariance matrix and dynamic pair-wise correlation of time series. The findings reveal that: first, there are two latent factors. Second idiosyncratic volatilities of 3 assets, including stock, dollar and gold have increased since mid-2017, and emerges evidence of clustering behavior. Third, the Volatility of inflation is explained by the hidden factors, and consequently the idiosyncratic Volatility is almost smooth. Fourth, the Volatility of stock return is highly correlated with the volatilities of inflation and dollar. In addition, there is a significant pairwise correlation between inflation-dollar and inflation-interest rate.

Estimation of the return distribution has a crucial role in Risk measurement and since the precision of risk measures depends on the precision of the return distribution, truly estimation of return distribution has attracted a huge attention. Although using Stochastic Volatility Models with parametric assumptions for estimation and illustration of the volatilities has been common in research, these assumptions usually result in careless estimations. So in the following research a semiparametric approach has been used for estimation of the Volatility by using a normal mixture dirichlet process. In this paper the distribution of the logarithm of the squared returns of banking index of Tehran Stock Exchange has been estimated by using mixtures of normal family and employing an MCMC algorithm. Finally, the results has been compared to the Basic Stochastic Volatility Model. The results show that when the return distribution is skewed, estimates of Volatility using the Model can differ dramatically from those using a Normal return distribution. Furthermore, when return distribution is similar to a normal distribution, the results of this Model are similar to the results of the parametric Model.

The financial markets reveal stylized facts that could not be captured by Black-Scholes partial differential equations (PDEs).  In this research, we investigate 3/2 Stochastic Volatility to pricing options which is more compatible with the interpretation of implied Volatility. Numerical study and calibrations show that the 3/2 Model incorporating jumps effectively encompasses key market characteristics attributed. However, it requires more estimating parameters in comparison to the pure diffusion Model. Stochastic Volatility Models with jumps describe the log return features of the financial market although more parameters are involved in estimations.

The expansion of the globalization process has increased the relationships among financial markets in different countries, which itself has motivated investors to move among them to make more profit. Given the situation in Iran after sanctions, the possibility of investing in well-known financial markets is facing with the risk of sanctions. The present study aims to evaluate the existence of Volatility spillover among the financial markets of Iran and Islamic oil exporters countries. To this aim, a multivariate factor Stochastic Volatility (SV) Model and stock price index data were used with daily frequency for the period 12/05/2008-02/19/2020. Based on the results, the main hypothesis that the Volatility spillover among the financial markets of OPEC oil-exporting Islamic countries follows a common and uniform random trend is accepted for the United Arab Emirates, Saudi Arabia, and Qatar, but not for Iran and Nigeria. Therefore, diversifying the portfolio for Iranian investors in the financial markets of OPEC Islamic oil exporters can reduce the investment risk in the long run which make such economies an appropriate investment destination for Iranians due to the conditions of sanctions.

The main purpose of this paper is to examine the effect of trade openness and government size on macroeconomic Volatility. Economic theories do not clearly show the effects of trade openness and government size on macroeconomic Volatility. Therefore, this is essentially an empirical problem. Hence, we have presented an empirical Model to test the effect of trade openness and government size on macroeconomic Volatility in Iran over the period of 1352-1395. For this, first, the macroeconomic uncertainty was extracted using Stochastic Volatility Model with leverage effects by technique of principal component analysis. The results showed that in the long run, trade openness and government expenditures have a positive effect on macroeconomic Volatility in Iran. In addition, in the short run, there is no significant relationship between the relevant variables and macroeconomic Volatility.

The main purpose of this paper is to examine the effect of trade openness and government size on macroeconomic Volatility. Economic theories do not clearly show the effects of trade openness and government size on macroeconomic Volatility. Therefore, this is essentially an empirical problem. Hence, we have presented an empirical Model to test the effect of trade openness and government size on macroeconomic Volatility in Iran over the period of 1352-1395. For this, first, the macroeconomic uncertainty was extracted using Stochastic Volatility Model with leverage effects by technique of principal component analysis. The results showed that in the long run, trade openness and government expenditures have a positive effect on macroeconomic Volatility in Iran. In addition, in the short run, there is no significant relationship between the relevant variables and macroeconomic Volatility.

Objective The significance of the capital market in driving the economic growth and development of a country necessitates a thorough examination of this market from multiple perspectives. Participating in this market invariably involves a heightened level of risk, prompting the emergence of various tools aimed at mitigating these risks. One of the main factors affecting investment decisions is the accurate valuation of derivatives, including options. The Black-Scholes Model is used to price a wide range of options contracts. The basic assumption in this fixed Model is to consider Volatility, which reduces the accuracy of calculating the option price. The main purpose of this research is to determine the price of a European call option with Stochastic Volatility.   Methods The Heston-Nandi Model is a closed pricing formula for European options that shares numerous assumptions with the Heston Model. The main difference between the Heston-Nandy Model and the Black-Scholes Model is the use of the variance type when option pricing. The Heston-Nandy Model considers the non-normal distribution of returns and random fluctuations more realistically. Since the Heston Model is one of the effective Models among the random turbulence Models, in this study, the option pricing under Heston and Heston Nandi random Stochastic is discussed, which has been investigated considering the non-normality of the data distribution.   Results In this study, data from Iran Khodro was utilized, spanning the period from November 21, 2020, to December 14, 2022. To increase the accuracy, the Volatility was calculated using two historical and implied methods. Following the application of option pricing using all three Models, namely Black-Scholes, Heston, and Heston-Nandi, and subsequent comparison of the results, it was determined that the Heston-Nandi Model exhibited superior performance when compared to the other two Models.   Conclusion The findings of this research indicate that, in both the short, medium, and long terms, the Heston-Nandi Model yields prices that closely align with market prices and exhibits lower error rates. Consequently, it can be inferred that the Heston-Nandi Model demonstrates a high degree of flexibility. The Heston-Nandi Model outperforms the Black-Scholes and Heston Models by capturing unusual patterns like skewness and elongation. This makes it a good alternative to those Models.