Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studying in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models of white noise distributions
In this paper we performed an analysis in order the make an evidence of GARCH modeling on the perfor...
his study aims to develop a predictive model for stock prices using time-series analysis. The primar...
This paper gives a tour through the empirical analysis of univariate GARCH models for financial time...
Financial returns are often modelled as autoregressive time series with random disturbances having c...
Financial returns are often modelled as autoregressive time series with random disturbances having c...
Autoregressive Conditional Heteroskedasticity (ARCH) models have been applied in modeling the relati...
The paper deals with estimation of both general GARCH as well as asymmetric EGARCH and TGARCH models...
The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model has been widely used in ...
ARCH and GARCH models have become important tools in the analysis of time series data, particularly ...
Recently, volatility modeling has been a very active and extensive research area in empirical financ...
One of the challenging aspects of conditional heteroskedasticity series is that if we were to plot t...
AbstractRapid developments of time series models and methods addressing volatility in computational ...
ARIMA model is basically one of the models that can be applied in the time series data. In this ARIM...
This paper investigates the implications of time-varying betas in factor models for stock returns. I...
AbstractFinancial returns are often modeled as autoregressive time series with innovations having co...
In this paper we performed an analysis in order the make an evidence of GARCH modeling on the perfor...
his study aims to develop a predictive model for stock prices using time-series analysis. The primar...
This paper gives a tour through the empirical analysis of univariate GARCH models for financial time...
Financial returns are often modelled as autoregressive time series with random disturbances having c...
Financial returns are often modelled as autoregressive time series with random disturbances having c...
Autoregressive Conditional Heteroskedasticity (ARCH) models have been applied in modeling the relati...
The paper deals with estimation of both general GARCH as well as asymmetric EGARCH and TGARCH models...
The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model has been widely used in ...
ARCH and GARCH models have become important tools in the analysis of time series data, particularly ...
Recently, volatility modeling has been a very active and extensive research area in empirical financ...
One of the challenging aspects of conditional heteroskedasticity series is that if we were to plot t...
AbstractRapid developments of time series models and methods addressing volatility in computational ...
ARIMA model is basically one of the models that can be applied in the time series data. In this ARIM...
This paper investigates the implications of time-varying betas in factor models for stock returns. I...
AbstractFinancial returns are often modeled as autoregressive time series with innovations having co...
In this paper we performed an analysis in order the make an evidence of GARCH modeling on the perfor...
his study aims to develop a predictive model for stock prices using time-series analysis. The primar...
This paper gives a tour through the empirical analysis of univariate GARCH models for financial time...