A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration is proposed. Several powerful tests for the (asymmetric) stable Paretian distribution with tail index 1 < α < 2 are used for assessing the appropriateness of the stable assumption as the innovations process in stable-GARCH-type models for daily stock returns. Overall, there is strong evidence against the stable as the correct innovations assumption for all stocks and time periods, though for many stocks and windows of data, the stable hypothesis is not rejected
The component GARCH model (CGARCH) was among the first attempts to split the conditional variance in...
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 ...
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration...
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration...
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...
The focus of this paper is the use of stable distributions for GARCH models. Such models are applied...
It is a well-known fact that financial returns exhibit conditional heteroscedasticity and fat tails....
Abstract. In this paper, we analyze the returns of stocks com-prising the German stock index DAX wit...
Several studies have highlighted the fact that heavy-tailedness of asset returns can be the conseque...
Financial returns exhibit conditional heteroscedasticity, asymmetric responses of their volatility t...
Statistical models of financial data series and algorithms of forecast-ing and investment are the to...
AbstractGeneralized autoregressive conditional heteroskedasticity (GARCH) models having normal or St...
Several studies have highlighted the fact that heavy-tailedness of asset returns can be the conseque...
The component GARCH model (CGARCH) was among the first attempts to split the conditional variance in...
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 ...
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration...
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration...
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...
The focus of this paper is the use of stable distributions for GARCH models. Such models are applied...
It is a well-known fact that financial returns exhibit conditional heteroscedasticity and fat tails....
Abstract. In this paper, we analyze the returns of stocks com-prising the German stock index DAX wit...
Several studies have highlighted the fact that heavy-tailedness of asset returns can be the conseque...
Financial returns exhibit conditional heteroscedasticity, asymmetric responses of their volatility t...
Statistical models of financial data series and algorithms of forecast-ing and investment are the to...
AbstractGeneralized autoregressive conditional heteroskedasticity (GARCH) models having normal or St...
Several studies have highlighted the fact that heavy-tailedness of asset returns can be the conseque...
The component GARCH model (CGARCH) was among the first attempts to split the conditional variance in...
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 ...