In this paper we consider a GARCH-in-Mean (GARCH-M) model based on the so-called z distribution. This distribution is capable of modeling moderate skewness and kurtosis typically encountered in financial return series, and the need to allow for skewness can be readily tested. We apply the new GARCH-M model to study the relationship between risk and return in monthly postwar U.S. stock market data. Our results indicate the presence of conditional skewness in U.S. stock returns, and, in contrast to the previous literature, we show that a positive and significant relationship between return and risk can be uncovered, once an appropriate probability distribution is employed to allow for conditional skewnessConditional skewness, GARCH-in-Mean, R...
This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. ...
Using GARCH-in-Mean models, we study the robustness of the risk-return relationship in monthly U.S. ...
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distribut...
Past financial crises show the importance of adequate risk measurement techniques which adapt more r...
In this paper, we study marginal and conditional skewness in financial returns for nine time series ...
A new GARCH-type model for autoregressive conditional volatility, skewness, and kurtosis is propose...
This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. ...
Although the GARCH model has been quite successful in capturing important empirical aspects of finan...
The main goal of this paper is an application of Bayesian model comparison, based on the posterior p...
AbstractThis paper builds a GARCHC-M model to explore the effect of the gain or loss on investors’ r...
In this paper, I propose a new semi-parametric GARCH-in-Mean model. Since many empirical papers have...
We present the results of an application of Bayesian inference in testing the relation between risk ...
Empirical research on European stock markets has shown that they behave differently according to the...
This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. ...
We present the results of an application of Bayesian inference in testing the relation between risk ...
This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. ...
Using GARCH-in-Mean models, we study the robustness of the risk-return relationship in monthly U.S. ...
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distribut...
Past financial crises show the importance of adequate risk measurement techniques which adapt more r...
In this paper, we study marginal and conditional skewness in financial returns for nine time series ...
A new GARCH-type model for autoregressive conditional volatility, skewness, and kurtosis is propose...
This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. ...
Although the GARCH model has been quite successful in capturing important empirical aspects of finan...
The main goal of this paper is an application of Bayesian model comparison, based on the posterior p...
AbstractThis paper builds a GARCHC-M model to explore the effect of the gain or loss on investors’ r...
In this paper, I propose a new semi-parametric GARCH-in-Mean model. Since many empirical papers have...
We present the results of an application of Bayesian inference in testing the relation between risk ...
Empirical research on European stock markets has shown that they behave differently according to the...
This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. ...
We present the results of an application of Bayesian inference in testing the relation between risk ...
This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. ...
Using GARCH-in-Mean models, we study the robustness of the risk-return relationship in monthly U.S. ...
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distribut...