Based on the importance of inflation in Iran economy, paying careful attention to inflation determinants is essential. According to the results of various studies, the evaluation of inflaion determinants by using standard VAR model leads to wrong conclusions due to omitted variables bias in VAR model. The problem of price puzzle in the empirical literature is an example. In this study, for a more accurate assessment of the determinants of inflation in Iran economy and forecasting the inflation, TVP-VAR models are used to model the inflation instead of VAR model with constant coefficients. So that the variables of GDP, growth of monetary basis, inflation, exchange rates, banks interest rates and inflation uncertainty are modeled. The results represent changeable relationships between variables over time and the impact of economic conditions on the effects of variables on each other.

In this study, we estimated the parameters using the state Space model described in ARIMA form. We’ve also used the Monte Carlo Method for simulating the process in 10000 reputations. Then the estimated parameters and the Monte Carlo simulation method are used to forecast TEPIX index, including 739 observations as an in-sample data from 21th of January 2011 to 19th February 2014 and 59 observations from 20th February 2014 to 21th May 2014 as an out of sample data. Furthermore, For more investigation we’ve considered different horizons of forecasting, short-term (equal to 1 week), mid-term (equal to 1 month) and long term (equal to 3 month). The results showed that Tehran stock market data has enough efficiency to forecast them, and showed that the state Space in Form ARIMA model and the Monte Carlo simulation method can be used as a predictive algorithm for TEPIX index and other indices with similar nature.

The main focus of this paper is to extend the analysis of some ruin related problems to a class of state-Space compound binomial risk models for a sequence of independent and identically distributed random variables of interclaim times when the claim occurrences are homogeneous‎. ‎First‎, ‎we obtain the mass function of a defective renewal sequence of random {Fn }n≥0 -stopping times‎, ‎using the compound binomial of aggregate claim amount together the net profit condition‎, ‎and compute the infinite time ruin probability with Markov property of risk process‎. ‎Moreover‎, ‎we derive the distribution of the time to ruin among many random variables associated with ruin using the convolution of claim amount and Lagrange’s implicit function theorem‎. ‎Lastly‎, ‎the theoretical results are illustrated with numerical computations‎.

Regarding The Importance Of Forecasting And Its Precision And Accuracy Significance In Various Economic Conditions, This Research Is An Attempt For Denoising Of Stock, By Implementing Of Wavelet Transform (A Branch Of Physic And Specially Signal Analysis) Considering TEPIX Index. Results Indicate That Existence Of Noises Lead To Reduction In Predictive Power Of The models And Denoising Cause To Improvement In Compatibility Of Dataset With The models And Finally Improve Predictive Power Of The models. In This Respect, The ARCH And state Space models And In-Sample Of 739 Daily Observations, Using The TEPIX Index Data Between Years 1389 – 1392, Are Implemented. The Results Clearly Indicate The Impactful Role Of Haar Wavelet Transform For Denoising TEPIX Dataset. This Method, Strongly, Lead To Increase In The Predictive Power Of The models And Accuracy Of Estimated Coefficients.

The main objective of this paper is to compare the accuracy of ARIMA, GARCH, ARIMA-GARCH and state Space models in estimating and forecasting the Tehran Exchange Stock Index (TEPIX). For this purpose, daily data from 21st of January 2011 to 19th February 2014 (corresponding to Iranian calendar: from first of Bahman 1389 to 30th of Bahman 1392) as out -of- sample and daily data from 20th of February 2014 to 19th May 2015 (corresponding to Iranian calendar: from first of Esfand 1392 to 30th of Ordibehesht 1393) as in- sample have been used. On the other hand, for more consideration and for boosting the accuracy of the mentioned models’ forecasting the TEPIX in the long run, simulation has been done using the Monte Carlo Method for two periods of time. These include the medium run and the short run, using out- of- sample and using in- sample for a general comparison. Next, the accuracy of the forecasts have been evaluated by calculating the Root Mean Square Errors (RMSE). According to the out- of- sample, the results indicate that the GARCH model has more accuracy of forecasting rather than the other models in the three periods of time (long run, medium run and short run), and compared with in-sample, the ARIMA model is a more sufficient model.

Macro-economic relationship between fiscal policy and economic growth has long been considered by economists. In this study to evaluate the more accurate effect of the government's fiscal policy in the economy, using quarterly data for the years 1988 to 2016, a factoraugmented vector autoregressive (FAVAR) model with time varying parameter model (TVP) in Iran's economy has been modeling. The variables of GDP growth, investment growth, inflation, exchange rates, the growth of private consumption expenditure and latent variable of government fiscal policies are used in model. Based on results the effects of fiscal policy on economic growth in the whole period is positive and investment increased the rate of economic growth. Also the additive positive effects of fiscal policy on the unofficial exchange rate has increased over time. In addition, the effect of fiscal policy on inflation is positive, so that the additive effect in economic prosperity period is more. Finally, the effect of fiscal policy on private sector spending is negative. Results of this study show changes in relationships between variables over time and also indicate that economic conditions of the country affects the impacts of independent variables.

Objective: After beginning of stock markets liberalization in mid-1980, integration of these markets into global markets is increasing. The countries are attempting to attract international investors to divide their portfolio risk, increase liquidity level and promote informational transparency and hence improve stock market efficiency. This study investigates the effects of financial liberalization on stock market informational efficiency in developing economies, during 2000-2016. Methods: With regarding to informational efficiency level in economies with developing characteristics is time-varying because of benefit from their investor experiences and increase of informational access and quality. In order to, we applied a state Space model with GARCH effects to determine the degree of informational efficiency during time for any selected countries. In continue, we use panel GMM model that allow measuring and assessing the impact of financial liberalization on the informational efficiency. Results: The results of state Space model realized the weak efficiency hypothesis in selected countries. In other words, the past returns do not contribute much to anticipate future returns, but it affected by macroeconomic, external shocks and political events. Also, the results of GMM model indicate that financial liberalization significantly (of course tiny) improves the degree of informational efficiency. Likewise, we found that information variable (turnover) and macroeconomic variables (inflation rate and exchange rate volatilities) would strengthen significantly informational efficiency in developing countries. Conclusion: This research contributes to improving informational efficiency in developing economies, this is necessary to implement financial liberalization process as well as improving macroeconomic circumstance and declining exchange rate volatilities.