0000000000548146
AUTHOR
Pentti Saikkonen
Nonlinear GARCH models for highly persistent volatility
In this paper we study new nonlinear GARCH models mainly designed for time series with highly persistent volatility. For such series, conventional GARCH models have often proved unsatisfactory because they tend to exaggerate volatility persistence and exhibit poor forecasting ability. Our main emphasis is on models that are similar to previously introduced smooth transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition variable. This choice of the transition variable corresponds to the idea that high persistence in conditional variance is related to relatively infrequent changes in regime. U sing the theory of Markov chains w…
Why Is It So Difficult to Uncover the Risk-Return Tradeoff in Stock Returns?
The low power of the standard Wald test in a GARCH-in-Mean model with an unnecessary intercept is shown to explain the apparent absence of a risk-return tradeoff in stocks. The importance of this finding is illustrated with monthly U.S. data. (c) 2006 Elsevier B.V. All rights reserved.
Modeling Conditional Skewness in Stock Returns
Abstract In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the mon…
A Skewed GARCH-in-Mean Model: An Application to U.S. Stock Returns
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 conditi…