Results 1 to 10 of about 10,202,946 (277)
Calculating Value at Risk: DCC-GARCH-Copula Approach [PDF]
فصلنامه پژوهشهای اقتصادی ایران, 2020 In this paper, in order to calculate portfolio market risk of 10 selected industries indices in Tehran Stock Exchange, two models of Value Risk (VaR) and Expected shortfall (ES) have been used.
Reza Taleblou, Mohammad Mahdi Davoudi
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Estimation of value at risk (VaR) index of mobarakeh steel company using two-sided lomax GARCH model [PDF]
تصمیم گیری و تحقیق در عملیات, 2022 Purpose: We have introduced the two-sided Lomax-GARCH (TSLx-GARCH) model. We have used this model to create a more realistic value-at-risk value index than other distributions for all confidence levels. We find this index for applied data.Methodology: In
Rasool Roozegar, Samane Arkia
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Scientific African, 2023 This study introduces a new conditional innovation density called the generalized odd generalized exponentiated skew-t (GOGEST) distribution for the generalized autoregressive conditional heteroscedasticity (GARCH) volatility models.
O.D. Adubisi, A. Abdulkadir, D.J. Adashu
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Investor Sentiment on the Stock Market using Artificial Neural Networks [PDF]
Revista de Management Comparat International, 2019 The present study uses volatility as a measure of investor sentiment on the Romanian capital market. The GARCH(1,1) model and the GARCH(1,1) model with Student-t innovations are used in order to describe the volatility of the Bucharest Exchange Trading ...
Oana Mădălina POPESCU
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Comparison and Forecasting of VaR Models for Measuring Financial Risk: Evidence from China
Discrete Dynamics in Nature and Society, 2022 With increasing extremal risk, VaR has been becoming a popular methodology because it is easy to interpret and calculate. For comparing the performance of extant VaR models, this paper makes an empirical analysis of five VaR models: simple VaR, VaR based
Yuling Wang, Yunshuang Xiang, Huan Zhang
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SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL
Media Statistika, 2021 The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data.
Didit Budi Nugroho +2 more
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Entropy, 2021 This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatility forecasting models and eight composed volatility forecasting models to explore whether the neural network approach and the settings of leverage effect ...
Jung-Bin Su
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Volatility Modeling of Emerging Foreign Exchange Market: A Case of Bangladesh [PDF]
Journal of International Logistics and Trade, 2013 This paper examined the volatility models for exchange rate return, including Random Walk model, AR model, GARCH model and extensive GARCH model, with Normal and Student-t distribution assumption as well as nonparametric specification test of these ...
Laila Arjuman Ara +1 more
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GARCH Models under Power Transformed Returns: Empirical Evidence from International Stock Indices
Austrian Journal of Statistics, 2021 This study evaluates the empirical performance of four power transformation families: extended Tukey, Modulus, Exponential, and Yeo--Johnson, in modeling the return in the context of GARCH(1,1) models with two error distributions: Gaussian (normal) and ...
Didit Budi Nugroho +2 more
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Predicting daily streamflow with a novel multi-regime switching ARIMA-MS-GARCH model
Journal of Hydrology: Regional Studies, 2023 Study region: Weihe River Basin of China Study focus: In recent decades, changing environments destroyed the natural structure of streamflow, making accurate streamflow prediction challenging.
Huimin Wang +3 more
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