Results 11 to 20 of about 10,202,946 (277)
Nonparametric option pricing under Beta-t-GARCH process with dynamic conditional score
One of the advantages of nonparametric option pricing methods is that they only require a set of future price scenarios, eliminating the need for an explicit risk-neutral model for the price of the underlying asset. In this paper, we explore the score-driven Beta-t-GARCH volatility model, introduced by Harvey (2013), to generate the price scenarios ...
Manoel F. de S. Pereira, Alvaro Veiga
openaire +1 more source
Estimation for a non-stationary semi-strong GARCH(1,1) model with heavy-tailed errors [PDF]
This paper studies the estimation of a semi-strong GARCH(1,1) model when it does not have a stationary solution, where semi-strong means that we do not require the errors to be independent over time.
Linton, O., Wang, H., Pan, J.
core +4 more sources
Modeling crude oil price volatility in Nigeria: using GARCH (1,1), EGARCH (1,1), and GJR-GARCH (1,1) models [PDF]
This study investigates the performance of various GARCH models for volatility forecasting, focusing on the GARCH (1,1), EGARCH (1,1), and GJR-GARCH (1,1) frameworks, each tested with normal and Student’s t-distributions.
Frederick A. Omoruyi +2 more
doaj +1 more source
In this paper, based on the Realized GARCH model, the fractional integration Realized GARCH model is proposed by combining long memory parameters with conditional variance and replacing the original realized measure with the realized measure obtained ...
Mei Xiao +4 more
doaj +1 more source
Volatility regimes of selected central European stock returns: a Markov switching GARCH approach
This paper investigates the weekly stock market data of the Hungarian stock index BUX, the Czech stock index PX and the Polish stock index WIG20 spanning from January 7, 2001 to April 18, 2021.
Michaela Chocholatá
doaj +1 more source
Day-of-the-week effect on the Tunisian stock market return and volatility
In this paper, we examine empirically the day-of-the-week effect on the Tunisian stock exchange index (TUNINDEX) return and volatility. We use three multivariate general autoregressive conditional heteroscedasticity models (GARCH (1,1), EGARCH (1,1), and
Abdelkader Derbali, Slaheddine Hallara
doaj +1 more source
Bayesian Estimation of Student-t GARCH Model Using Lindley’s Approximation
The dependency of conditional second moments of financial time series is modelled by Generalized Autoregressive conditionally heteroscedastic (GARCH) processes. The maximum likelihood estimation (MLE) procedure is most commonly used for estimating the unknown parameters of a GARCH model.
Arı, Yakup, Papadopoulos, Alex
openaire +1 more source
Risk forecasting in (T)GARCH models with uncorrelated dependent innovations
(G)ARCH-type models are frequently used for the dynamic modelling and forecasting of risk attached to speculative asset returns. While the symmetric and conditionally Gaussian GARCH model has been generalized in a manifold of directions, model innovations are mostly presumed to stem from an underlying IID distribution.
Beckers, Benjamin +2 more
openaire +2 more sources
M-estimation in GARCH models. [PDF]
This paper derives asymptotic normality of a class of M-estimators in the generalized autoregressive conditional heteroskedastic (GARCH) model. The class of estimators includes least absolute deviation and Huber's estimator in addition to the well-known ...
Mukherjee, Kanchan
core +4 more sources
Modelos GARCH em ações financeiras: um estudo de caso
Este artigo tem por objetivo detalhar o protocolo de aplicação e avaliação dos modelos autorregressivos de heteroscedasticidade condicional generalizados (GARCH), com ênfase em especificar adequadamente a distribuição de probabilidade para os resíduos e
Paulo Siga Thomaz +4 more
doaj +1 more source

