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Objective This paper seeks to employ fractional cointegration methodology to model high and low stock prices, as well as the range series, indicating the difference between high and low stock prices. Additionally, it tries to examine the regime-switching characteristic in the cointegrating relationship between price series. Methods The study utilizes the Fractionally Cointegrated Vector Autoregressive (FCVAR) approach to explore the cointegrating relationships among six key indices of the Tehran Stock Exchange - TEPIX, First Market index, Second Market index, Industry index, Mali index, and Fara Bourse overall index - across various time frequencies from August 14, 2007, to August 15, 2022. To test for the fractional unit root in each price series, the GPH and ELW methods are also employed. Furthermore, the Qu (2011) method is employed to test the true long memory against spurious long memory on the range series. Finally, the threshold effect in the cointegrating relationship between the high and low price series is analyzed by the two regimes' Self-Exciting Threshold Autoregressive approach (SETAR). Results In most indices, the estimated fractional parameter of the range series is lower than that of the high and low stock price indices. Moreover, the high and low prices and the range series are affected by regime changes or a smoothly varying trend. The high and low stock price indices are fractionally cointegrated, in the two levels of stationary and nonstationary ranges. Further, the fractional cointegration approach gives a lower measure of dependency in price range series than the fractionally integrated approach. These findings are robust to different time frequencies, including daily, weekly, and monthly. Finally, the results affirm the time-varying cointegrating relationship between high and low stock prices. Thus, the FCVAR framework should be generalized to adjust according to this characteristic. Conclusion The empirical results show that, unlike the return, which is stationary and unpredictable, the range prices have characteristics of the long-memory processes, falling into nonstationary and stationary levels with mean-reverting behavior. Accordingly, one can obtain a non-stationary range-based volatility estimator, which is more efficient than a stationary return-based realized volatility estimator. These results imply that traders, investors, and policymakers could predict the future extreme prices of the market indices from past values and exploit such predictions to design investment strategies. The cointegration between high and low prices of indices implies limited arbitrage opportunities for the investors and traders in the Tehran Stock Exchange and Iran Fara Bourse Co. Furthermore, the efficiency of these markets is shown to be unstable across indices. Still, there are robust long-run relationships between the high and low stock price indices. Thus, to design hedge fund strategies containing combinations of the stocks from these markets, the behavior and long-run relations of the indices must be considered. Moreover, to design portfolio allocation and diversification strategies, one should mix assets that haven't short-term behavior with indices that show long-term relationships. This conclusion is derived from the notion that mispricing and over-hedging may occur in the absence of cointegrating relationships between price series. [ABSTRACT FROM AUTHOR]

Purpose: One of the well-known stylized facts of the distribution of asset returns is the pattern of skewness and kurtosis. Previous research has shown that financial crises and turbulences induce shocks that significantly affect the return distributions, which in addition to creating fat tails, also leads to an asymmetric reaction of the tails. Although skewness and kurtosis have a common impact on tail risk, their significance for risk forecasting has not been considered in empirical financial studies. Developing models for accurate risk forecasting is an important consideration that has always received considerable attention from policymakers, economists, financial market participants, and researchers. For this purpose, in this study, following Jimenez et al. (2022), to estimate the return density, a semi-nonparametric approach is adopted which is based on the asymptotic properties of Gram-Charlier extensions. This approach allows examining the significance of the inclusion of Hermit polynomials and their crossed products in the Gram-Charlier densities for risk forecasting. In fact, in the framework. Evaluating the risk measures in a semi-nonparametric framework allows for capturing all stylized facts of the return series for assessing skewness and kurtosis and their interactions by adding new parameters to the density function as a relevant source of information. Method: This research, for the first time, employs the modified Gram-Charlier density function (mGC), including the second and third moments (skewness and kurtosis) and their interactions for modeling the risk of distribution of the daily losses of the TEPIX. Moreover, the performance of alternative models based on different specifications of Gram-Charlier is evaluated in terms of the accuracy of risk forecasting measures using modern backtesting tests. For this purpose, the Value-at-Risk criteria and Median Shortfall measure, implied for the first time in the present study, are used. The sample includes the daily series of the TEPIX index covering the period from May 20, 2008, to August 22, 2023. Focusing on the right-tail of the TEPIX distribution, the loss series is calculated as a negative of log differences of prices. The models are estimated by R and MATLAB software. Modeling the losses is done through a two-step estimation process according to the following steps. Step 1: the ARMA(1,1)-GARCH(1,1) model is estimated using the quasi-maximum likelihood (QML) approach by assuming the Gaussian distribution for error terms. Step 2: the modified Gram-Charlier expansion and alternative specifications are estimated using standardized residuals extracted from the previous step. Different specifications of the Gram-Charlier density density fit using the maximum likelihood method. For in-sample fitting of the model, the estimation window size is chosen to be W=2656 observations and the step is chosen to be one new observation. The remaining 1000 observations are used for out-of-sample forecasts. Findings: The empirical findings from the in-sample fitting of the ARMA(1,1)-GARCH(1,1) model, under the Gram-Charlier densities for the innovations, indicate that skewness and kurtosis and their interactions are economically and financially significant. The results of the Backtesting for both 99%-VaR and 99%-MS confirm the out-of-sample forecasting performance of the Gram-Charlier density specification incorporating the skewness parameter, especially for the tails, compared to other specifications that have been taken into account in this research. Conclusion: Overall, the results show that the parameter related to the asymmetry of distribution alone can be a valuable source of information to the market participants by providing accurate risk measures. The empirical findings have practical implications for designing strategies for managing risk and decision-making in times of market instability. The novelity of this study is the application of a semi non-parametric approach to evaluate the risk forecasting of the TEPIX index. The previous studies have mainly modeled the return series based on parametric and non-parametric distributions. Therefore, the empirical findings of the present study provide a novel application for risk management in the Tehran Stock Exchange. The empirical findings can have useful implications for stabilizing the financial markets. [ABSTRACT FROM AUTHOR]

In this paper, a dual-mode (DM) micromechanical elliptical ring resonator for use in temperature sensing is reported. The proposed resonator is made of single crystal silicon and works based on beat frequency (fb). In the designed sensor, temperature coefficient of beat frequency (TCfb) will be increased significantly by minimizing the fb and provides better temperature sensitivity. This proposed device was designed and simulated by COMSOL Multyphysics software. By engineering the device geometry, we introduce two adjacent resonant frequencies which produce very small fb in the range of 2 kHz. The device shows TCf1 of about 44 ppm/ºC and TCf2 of 5 ppm/ºC. Combination of small fb and large ΔTCf, present temperature coefficient of beat frequency (TCfb) about 112000 ppm/ºC which has approximately 75× improvement in TCfb compared to previous demonstrated DM resonators. [ABSTRACT FROM AUTHOR]

This paper attempts to analyze the dependence structure between returns on the Tehran Stock Exchange (TEPIX) index and (Bahar-Azadi) Gold Coin Futures (GCF) during the period from December 13, 2008 to December 21, 2015. To address this issue, we employ different copula models that allow for capturing extreme dependence (tail dependence) and asymmetry. The empirical results show that the dependence structure between the return series is low and positive. Furthermore, we find evidence of time-varying upper tail dependence, which highlights that the return series co-move in a bullish market. Our results suggest that investors and risk managers may obtain diversification benefits from GCF, especially during market upturns. [ABSTRACT FROM AUTHOR]