Stationarity of stable power-GARCH processes

By Stefan Mittnik, Marc S. Paolella, Svetlozar T. Rachev. 2002

89 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ed920eb3dbc69a0663235b5ba3c43888cf83ef2c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

A new approach to modeling co-movement of international equity markets: evidence of unconditional copula-based simulation of tail dependence (2009, methodology): “The reason we only consider ARMA-GARCH at lag order 1 is to make our analysis satisfy the stationary conditions for the processes we adopted for the marginal distribution (see Mittnik et al. 2002 ).”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, unknown): “Mittnik et al. (2002) and Panorska et al. (1995) derived conditions for strict stationarity of GARCH and APARCH models with stable Paretian conditional distributions.”
Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions (2003, unknown): “2 Mittnik et al. (2002) derived sufficient conditions under which the Sδα,βGARCH(r, s) process has a unique strictly stationary solution.”
Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check (2006, background): “Copyright © 2006 John Wiley & Sons, Ltd. key words CaViaR; coverage probability; filtering; quantile loss; reality check; stress testing; VaR”
Necessary and Sufficient Restrictions for Existence of a Unique Fourth Moment of a Univariate Garch(P,Q) Process (2005, methodology): “GARCH processes and their generalizations (Mittnik, Paolella, and Rachev, 2002) have been used to model volatilities or time-varying variances of underlying variables, usually financial-asset returns or residuals from estimated time-series models.”