Results 1 to 10 of about 424 (146)

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, Haslifah M Hasim, Hongsheng Dai   +2 more
doaj   +6 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
Nguyet Moon Nguyen, Mohammad Rafiqul Islam, Fahad Mostafa   +2 more
exaly   +3 more sources

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
doaj   +2 more sources

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
doaj   +2 more sources

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   +3 more sources

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.
Emese Lazar, Carol Alexander
exaly   +2 more sources

Modeling Saudi stock index returns and volatility: a dual approach using GARCH and neural networks [PDF]

Frontiers in Artificial Intelligence
The 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

Volatility Forecasting Using a Hybrid GJR-GARCH Neural Network Model

Procedia Computer Science, 2014
AbstractVolatility forecasting in the financial markets, along with the development of financial models, is important in the areas of risk management and asset pricing, among others. Previous testing has shown that asymmetric GARCH models outperform other GARCH family models with regard to volatility prediction.
David Enke
exaly   +2 more sources

Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS [PDF]

Science Journal of Applied Mathematics and Statistics, 2015
In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of one kind of Chinese stock index--- Shanghai Composite Index and the series of independent and identically distribution standardized residuals is formed from the filtered model residuals and conditional volatilities from the return series with an GJR-GARCH ...
exaly   +2 more sources

Empirical Research on VAR Model Based on GJR-GARCH, EVT and Copula [PDF]

Science Journal of Applied Mathematics and Statistics, 2015
In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of two index--- New York stock exchange composite index (NYA) and NASDAQ. and estimate the distribution function of the residuals utilizing Gaussian kernel method and Extreme Value Theory.
exaly   +2 more sources