textabstractStable and GARCH processes have been advocated for modeling financial data. The aim of this note is to compare the two processes. It is shown that the unconditional distribution of variaties from a GARCH-like process, which explicity models the clustering of volatility and exhibits the fat-tail property as well, can be stable. Given suitable conditions the conditional distributions are stable as well. While it is generally realized that processes with variates that have unconditional nonnormal stable densities have a high frequency of ‘outliers’, it is less well known that they can exhibit the clustering phenomenon too. The clustering is obtained through stable subordination with conditional scaling
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration...
The focus of this paper is the use of stable distributions for GARCH models. Such models are applied...
AbstractGeneralized autoregressive conditional heteroskedasticity (GARCH) models having normal or St...
Generalized autoregressive conditional heteroskedastic (GARCH) model is a standard approach to study...
In this contribution, a basic theoretical approach to stable laws is described. There are mentioned ...
The paper considers a volatility model that includes a persistent, integrated or nearly integrated, ...
It is well known that the class of strong (Generalized) AutoRegressive Conditional Heteroskedasticit...
The component GARCH model (CGARCH) was among the first attempts to split the conditional variance in...
It is a well-known fact that financial returns exhibit conditional heteroscedasticity and fat tails....
This article shows that the relationship between kurtosis, persistence of shocks to volatility, and ...
Two well documented empirical regularities in asset markets, leptokurtosis and clustered volatility,...
and the Deutschen Forschungsgemeinschaft. †Michele Leonardo Bianchi’s research was supported by a Ph...
Consider a scenario where one aims to learn models from data being characterized by very large fluct...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration...
The focus of this paper is the use of stable distributions for GARCH models. Such models are applied...
AbstractGeneralized autoregressive conditional heteroskedasticity (GARCH) models having normal or St...
Generalized autoregressive conditional heteroskedastic (GARCH) model is a standard approach to study...
In this contribution, a basic theoretical approach to stable laws is described. There are mentioned ...
The paper considers a volatility model that includes a persistent, integrated or nearly integrated, ...
It is well known that the class of strong (Generalized) AutoRegressive Conditional Heteroskedasticit...
The component GARCH model (CGARCH) was among the first attempts to split the conditional variance in...
It is a well-known fact that financial returns exhibit conditional heteroscedasticity and fat tails....
This article shows that the relationship between kurtosis, persistence of shocks to volatility, and ...
Two well documented empirical regularities in asset markets, leptokurtosis and clustered volatility,...
and the Deutschen Forschungsgemeinschaft. †Michele Leonardo Bianchi’s research was supported by a Ph...
Consider a scenario where one aims to learn models from data being characterized by very large fluct...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration...