Data with high frequency have a particular type of none stationary that is called fractional none stationary. This property causes the emergence of long-term memory in financial time series with high frequency. The existence of long-term memory in cement industry time-series is studied in this paper at first and its presence will be confirmed in a high confidence level by two tests R/S and GPH. Next, the accuracy of financial time-series forecast models such as ARMA and GARCH which dont consider the feature of long-term memory in time series modeling and models such as ARFIMA and FIGARCH that take this feature into account are compared with presented new meta heuristic that is composed of algorithm (harmony search) and weighted fuzzy time series by the way of rolling window and by the use of Root Mean Square Error criteria (RMSE) in different time intervals. The results show that the presented Meta heuristic method submits better result of common econometric models in all time intervals.

The objective of this study is to investigate the impact of financial crisis on global economy of agriculture sector as well as raisin and date exports. Export demand of the selected crops was estimated using time series and panel data. GNP of the importing countries and exchange rate volatility were applied as indices indicating the global financial crisis. Time series data showed a long-run relationship between export demand and its affecting factors. Findings also showed the significant impact of the decrease in GNP of the importing countries in long run. Impact of this variable was also recognized important on demand estimated by panel data. Generally speaking, exchange rate volatility may affect agriculture sector and raisin export positively while in the case of date a negative impact was more evident.

Economists and financial experts are always looking for ways to predict the behavior of economic and financial variables such as exchange rates. Many studies have been done on structural models and forecasting time series of exchange rates. However, predicting the exchange rate has always been a complex issue and has become a challenge to the modeling the exchange rates among international finance researchers and econometric experts.In this study, first a combination of structural models and time series models using the genetic algorithm are presented. Then, the performance of structural models will be compared to single time series model and to the other combination methods such as the use of averages approach. The results show that the genetic algorithm combined model, among other methods of forecasting exchange rates, has a higher accuracy.

Raising saving, level removal of financial resource obstacles to investment and increase in productivity allare require the financial structure reform and banking system improvement. This study, investigate the impact of financial development composition Index on economic growth in Iran by use of composition approach, using time series data 1352-86, and econometric models with ARDL and ECM method, test and analysis Cobb-Douglas production function for Iran. The results show that economic growth doesnt react to financial development changes (in the short time and long run). It is necessary, to harmonize financial sector development in finance and money services sector with macro economic objectives, while adjusting economic policies to rising financial and money service sector efficiency.

Forecasting financial markets is an important issue in finance area and research studies. Importance of forecasting on one hand and its complexity, on the other hand, researchers have done much work in this area and proposed many methods. In this research, we propose a hybrid model include wavelet transform, ARMA-EGARCH and NN for day-ahead forecasting of stock market price in different markets. At first WT is used to decompose and reconstruct time series into detailed and approximated parts. And then we used ARMA-EGARCH and NN models respectively for forecasting details and approximate series. In this model we used technical index by approximate part to the improvement of our NN model. Finally, we combine prediction of each model together. For validation, proposed model compare with ANN, ARIMA-GARCH and ARIMA-ANN models for forecasting stocks price in UA and Iran markets. Our results indicate that proposed model has better performance than others model in both markets.

Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Forecasting accuracy is one of the most important features of forecasting models. Nowadays, despite the numerous time series forecasting models which have been proposed in several past decades, it is widely recognized that financial markets are extremely difficult to forecast. Artificial Neural Networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Both theoretical and empirical findings have suggested that integration of different models can be an effective method of improving upon their predictive performance, especially when the models in the ensemble are quite different. This is the reason that hybrid methodologies combining the linear models such as ARIMA and nonlinear models such as ANNs have been proposed in the literature of time series forecasting. These hybrid techniques decompose a time series into its linear and nonlinear form in order to use the unique advantages of linear and nonlinear modeling methods and are one of the most popular hybrid models, which have recently been shown to be successful for single models. In this paper, an improved version of the hybrid ANNs/ARIMA models is proposed for time series forecasting. In the proposed model, the performance of the hybrid ANNs/ARIMA models is improved using diagnosing the trend of residuals by Probabilistic Neural Networks (PNNs). Empirical results of exchange rate forecasting indicate that the proposed model is more satisfactory than ANNs/ARIMA models.

The present study aims at developing an aggregate financial stability index by using banking sector indices to assess financial stability and examine if the variable of credit-to-GDP gap corresponds to its long-term trend which represents the macro-prudential indicator has co-movement with the built financial stability index? To this end, monthly banking balance sheet data were collected from the Central Bank of the Islamic Republic of Iran from March 2007 to March 2017. Co-movement of two time series was assessed at two dimensions of time and frequency through wavelet analysis. It can be observed that there is a greater relationship between the two variables at short-term and medium-term. In the short-term, there is a negative correlation between financial stability and the representative of the macro-prudential variable. The increase of the credit-to-GDP gap results in a decrease in financial stability while these variables are positively correlated at medium-term. An increase in the credit-to-GDP gap increases financial stability, whereas such a relationship cannot be observed for the long-term. Thus, it seems necessary to adopt a macro-prudential policy more at medium term.

In this paper, we investigate how novel approaches can improve the predictions made by traditional econometric approaches in the field of forecasting. Autoregressive integrated moving average (ARIMA) is known as one of the most widely used methods for predicting economic and financial time series, providing a good framework, especially for short-term linear predictions of time series. However, the assumption of nonlinear effects in time series and the emergence of novel deep learning algorithms, which can extract complex features of time series and model them, have motivated researchers to examine the predictive power of traditional and novel modeling approaches. In this study, two methods are examined for predicting the prices of the four most valuable cryptocurrencies. ARIMA and three approaches in the field of deep learning, including (RNN, LSTM, and GRU), are investigated. In addition, a hybrid model of deep learning and ARIMA has been introduced, which is a combination of the strengths of both models to increase the accuracy of predictions. The results show that the hybrid models perform better in predicting future time series than each of the ARIMA and deep learning models separately. Also, the ARIMA-GRU model has fewer prediction error values than all estimated models.

Occurrence of financial crisis Occurrence of financial crisis and increase concerns about existing inequalities have led to the introduction of financial stability sub-indicators by international institutions for insurance companies. The occurrence of any financial problems and the subsequent scandal known as financial and economic bankruptcy in insurance companies, not only affects their shareholders, even also affects many members of society, including insurers and other stakeholders. Since insurance industry observers need to achieve an efficient tool to study and identify the internal challenges of the country's insurance industry, and in the meantime, one-dimensional indicators do not have the necessary strength to express the effects of such challenges, the purpose of this article is to calculate the combined financial stability index. For the insurance industry, according to the statistics and information of the period of 1397-1385 insurance companies. The combined index of financial stability has been calculated using 27 sub-indices from the theoretical and experimental literature and the principal components method. The results show that in the long run, the insurance industry is facing a downward trend in financial stability, but in the short run, it is fluctuating. The long-term downward trend of the financial stability index indicates the critical situation of the insurance industry.

Accurate precipitation forecasts are much attractive due to their complexity. This study aimed to use the hybrid Seasonal Autoregressive Integrated Moving Average (SARIMA) model and machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to improve precipitation forecasts. Time variation analysis and time series decomposition were the two concepts applied to construct the hybrid models. The performance of the two concepts was evaluated with monthly precipitation time series of two stations in northern Iran. Time variation analysis of time series was conducted with the clustering analysis, which increased the accuracy of forecasting with 20.99% decrease in the geometric mean error ratio for the two stations. SVM model decreased the forecasted error compared to ANN in the internal process of time variation analysis. Average of Mean Relative Error (MRE) were MRESVM= 0.72, MREANN= 0.89, and Mean Absolute Error (MAE) in the two stations were MAESVM= 18.02 and MAEANN= 23.88. Therefore, SVM outperformed the ANN model. Comparison of the two hybrid models indicated that more accurate results belonged to the concept of time series decomposition (the decrease in root mean square error from time variation to time series decomposition concepts was 13.35%). Extracting the pattern of data with SARIMA-based hybrid model with time series decomposition improved the precipitation forecasting. Configurations related to nonlinear components of time series with time steps of residual had good performance (the average of agreement index was 0.9). The results suggest that the hybrid model can be a valuable and effective tool for decision processes, and time series decomposition to linear and nonlinear components has a better performance.