Non-Linear GARCH Models for Highly Persistent Volatility

@article{Lanne2002NonLinearGM,
  title={Non-Linear GARCH Models for Highly Persistent Volatility},
  author={Markku Lanne and Pentti Saikkonen},
  journal={Econometrics eJournal},
  year={2002}
}
In this paper we study new nonlinear GARCH models mainly designed for time series with highly persistent volatility. [] Key Method U sing the theory of Markov chains we provide sufficient conditions for the stationarity and existence of moments of the considered smooth transition GARCH models and even some more general nonlinear GARCH models. Empirical applications to two exchange rate return series show that the new models can be superior to conventional GARCH models especially in longer term forecasting.

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References

SHOWING 1-10 OF 61 REFERENCES

Volatility impulse response functions for multivariate GARCH models

In the empirical analysis of financial time series, multivariate GARCH models have been used in various forms. As it is typical for nonlinear models there is yet no unique framework available to

Improving GARCH volatility forecasts with regime-switching GARCH

Abstract. Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We

Persistence and Kurtosis in GARCH and Stochastic Volatility Models

This article shows that the relationship between kurtosis, persistence of shocks to volatility, and first-order autocorrelation of squares is different in GARCH and ARSV models. This difference can

Smooth-Transition GARCH Models

The asymmetric response of conditional variances to positive versus negative news has been traditionally modeled with threshold specifications that allow only two possible regimes: low or high

Forecasting volatility with switching persistence GARCH models

In this paper we examine the forecasting performance of five nonlinear GARCH(1,1) models. Four of these have recently been proposed in literature, while the fifth model is a new one. All five models

A new non-linear GARCH model

This dissertation contains four papers in the field of financial econometrics. In the first paper, A Smooth Transition ARCH Model for Asset Returns, a new class of ARCH models is introduced. The

Smooth transition GARCH models: a bayesian perspective

This paper proposes a new kind of asymmetric GARCH where the conditional variance obeys two different regimes with a smooth transition function. In one formulation, the conditional variance reacts

Modeling the U.S. Short-Term Interest Rate by Mixture Autoregressive Processes

A new kind of mixture autoregressive model with GARCH errors is introduced and applied to the U.S. short-term interest rate. According to the diagnostic tests developed in the paper and further

MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS

This paper first provides some useful results on a generalized random coefficient autoregressive model and a generalized hidden Markov model. These results simultaneously imply strict stationarity,
...