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Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model. [PDF]
PLoS ONE, 2018 In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory.
Marius Galabe Sampid +2 more
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Dynamical Approach in studying GJR-GARCH (Q,P) Models with Application
Tikrit Journal of Pure Science, 2022 This paper deals with finding stationarity Condition of GJR-GARCH(Q,P) model by using a local linearization technique in order to reduce this non-linear model to a linear difference equation with constant coefficients and then obtain the stationarity ...
Nooruldeen A. Noori, Azher A. Mohammad
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Stochastic properties and pricing of bitcoin using a GJR-GARCH model with conditional skewness and kurtosis components [PDF]
Review of Quantitative Finance and Accounting, 2022 Using a flexible statistical framework that accounts for time-varying skewness and leptokurtosis, we examine the stochastic behavior of Bitcoin in comparison to five major currencies. The empirical findings reveal that the distribution of all series is leptokurtic.
Theodossiou P, Ellina P, Savva C.
europepmc +4 more sources
GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies [PDF]
Journal of Risk and Financial Management, 2021 Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market
Fahad Mostafa +3 more
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Analytic moments for GJR-GARCH (1, 1) processes
International Journal of Forecasting, 2021 For a GJR-GARCH(1,1) specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moments for the most commonly used GARCH models are stated as special cases.
Alexander, Carol +2 more
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How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach [PDF]
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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Value at Risk Prediction for the GJR-GARCH Aggregation Model
Pattimura International Journal of Mathematics (PIJMath), 2022 Volatility is the level of risk faced due to price fluctuations. The greater the volatility brings, the greater the risk. We need a measure such as Value at Risk (VaR) and volatility modeling to overcome this. The most frequently used volatility model in the financial sector is GARCH.
Ariestha Widyastuty Bustan +2 more
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Price Risk Measurement of China’s Soybean Futures Market Based on the VAR‐GJR‐GARCH Model [PDF]
Complexity, 2021 As one of the main forces in the futures market, agricultural product futures occupy an important position in China’s market. As China’s futures market started late and its maturity was low, there are many risks. This study focuses on the Dalian soybean futures market.
Chuan-hui Wang +4 more
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PEMODELAN BEBAN PUNCAK ENERGI LISTRIK MENGGUNAKAN MODEL GJR-GARCH [PDF]
Jurnal MSA ( Matematika dan Statistika serta Aplikasinya ), 2018 Energi listrik adalah salah satu kebutuhan pokok yang memiliki peranan yang sangat penting dalam kehidupan. Kebutuhan akan energi listrik tidak bisa terlepas dari kehidupan baik itu untuk kebutuhan rumah tangga, industri, maupun pemerintahan.setiap harinya konsumsi listrik pada waktu tertentu akan mengalami puncak pemakaian, sehingga dipandang perlu ...
Ermawati, Ermawati +2 more
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Modeling Saudi stock index returns and volatility: a dual approach using GARCH and neural networks [PDF]
Frontiers in Artificial IntelligenceThe financial markets are the drivers of economic growth as they organize savings, bring in foreign investment, and they efficiently allocate resources.
Sukainah AL-Besher, Dania AL-Najjar
doaj +2 more sources