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  • Open Access


    CBOE Volatility Index Forecasting under COVID-19: An Integrated BiLSTM-ARIMA-GARCH Model

    Min Hyung Park1, Dongyan Nan2,3, Yerin Kim1, Jang Hyun Kim1,2,3,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 121-134, 2023, DOI:10.32604/csse.2023.033247

    Abstract After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The… More >

  • Open Access


    Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

    Tariq T. Alshammari1, Mohd Tahir Ismail1, Nawaf N. Hamadneh3,*, S. Al Wadi2, Jamil J. Jaber2, Nawa Alshammari3, Mohammad H. Saleh2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2589-2601, 2023, DOI:10.32604/iasc.2023.024001

    Abstract In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the most volatile data, and improve… More >

  • Open Access


    A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

    Kais Tissaoui1,2,*, Sahbi Boubaker3,4, Waleed Saud Alghassab1, Taha Zaghdoudi1,5, Jamel Azibi6

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4291-4309, 2022, DOI:10.32604/cmc.2022.028830

    Abstract The application of optimization methods to prediction issues is a continually exploring field. In line with this, this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective. The complex characteristics of implied volatility risk index such as non-linearity structure, time-varying and non-stationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown parameters. We use the Hybrid Particle Swarm Optimization (HPSO) tool to identify the model parameters of nonlinear polynomial Hammerstein model. Findings indicate that, following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input… More >

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