Estimation of conditional variance has lots of application reflecting economic, especially financial economics, social economics and political economics’ risk and volatility research. Therefore, obtaining accurate estimation of the conditional variance is especially important. Recently Hansen has modeled the conditional variance and Realized volatility simultaneously which is known as Realized GARCH model. In this paper, we introduce a fuzzy coefficient in the Realized GARCH, and then compare this model with GARCH, EGARCH and GJR-GARCH methods as well as the RGARCH model with 2 different criteria of the Realized volatility concerning Tehran Stock Exchange Index. The log likelihood value used to evaluate in-sample fitting. According to this criterion, our proposed model has a better fit than the rest of the models. To evaluate the accuracy of prediction of conditional variance, the rolling window method used with two MSE and QLIKE loss functions. The results indicate that our model, the Realized GARCH with fuzzy coefficient has the best performance with both loss functions.

forecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization, and risk management are examples of implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Realized GARCH (RGARCH) that considers a simultaneous model for both Realized volatility and conditional variance at the same time. In this article, we estimate conditional variance with GARCH, EGARCH, GIR-GARCH, and RGARCH with two Realized volatility estimators using gold intraday data. We compared models, for in-sample fitting; by the log-likelihood value and used MSE and QLIKE lose functions to evaluate predicting accuracy. The results show that the RGARCH method for GOLD outperforms the other methods in both ways. So, using the RGARCH model in practical situations, like pricing and risk management would tend to better results.

As a measurement of risk, Value-at-Risk has always been attractive to researchers in the field of risk management and capital markets. This importance has always led them to develop models to increase the accuracy of VaR estimation. GARCH models are used to estimate conditional variances in parametric approaches of calculating VaR. In the latest developments, Hansen presented the Realized GARCH model using intra-day data. Regarding the recent high fluctuations of the Tehran Stock Exchange Index, the use of Realized GARCH models to compute VaR can increase the accuracy of forecasting. In this paper the three different distributions, Normal, T, and GED, are combined with conventional GARCH models, and the new Realized GARCH model. Next, the VaR of the Tehran Stock Exchange index is forecasted using the rolling window sampling method. And finally, the accuracy of predicted VaR has been evaluated and compared using a two-step Backtest method. The outcome of this study indicates that using the new Realized GARCH model in forecasting VaR will tend to result in more accurate estimates, whether at the 5% or 1% level.

Forecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization and risk management are examples for implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Realized GARCH (RGARCH) that considers simultaneous model for both Realized volatility and conditional variance at the same time. In this article, we estimate conditional variance with GARCH, EGARCH, GIR-GARCH and RGARCH with two Realized volatility estimators using Tehran Exchange Price Index (TEPIX). We compared models, for in sample fitting, by the log likelihood value and used MSE and QLIKE lose functions to evaluate predicting accuracy. The results show that the RGARCH method for TEPIX outperforms the other methods in both ways. So, using RGARCH model in practical situations like pricing and risk management would tend to better results.

One of the Usuli issues in charge of acquiring a proper means for discovering the religious percepts is the authority of certitude. The famous Usulis maintain that the Usuli certitude has inherent authority and as a result, consider it as the basis for recognition in Islamic jurisprudence and Usul al-Fiqh and, falsely, refer to it as knowledge; nevertheless, many later Usulis deny the authority of Usuli certitude and believe that the basis of authority is achieving the actual percept, not the mere spiritual condition of the duty-bound. Therefore, it is necessary to consider certitude as the basis of recognition, subject to its correspondence with reality i. e. the logical certitude which is the exact and terminological meaning of knowledge. But the more important question is how to acquire logical certitude i. e. knowledge as to religious propositions. It appears that the only certain way to acquire knowledge as to imperative teachings of religion is hearing them from the Divine Lawgiver or the honest reporter.

Volatility is the primary measure of risk in modern finance and volatility estimation and inference has attracted substantial attention in the recent financial econometric literature, especially in high-frequency analyses. High-frequency prices carry a significant amount of noise. Therefore, there are two volatility components embedded in the returns constructed using high frequency prices: the true volatility of the unobservable efficient returns and the volatility from the existence of microstructure noise. Researchers proposed several methodologies for estimating these two components but each of these estimators has its own pros and cons. however, some of them have higher rate of convergence. Multi-Scale Realized Volatility (MSRV) is one of these estimators that reported to have a high efficiency in estimating true Realized volatility. In this paper, after estimating these two components through the MSRV approach, we investigate the relation between them. Our results suggest that there is a positive meaningful relation between microstructure noise and true Realized volatility.