Neglecting parameter changes in GARCH models

By Eric Hillebrand. 2005

327 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/a0db3b5be575fb6911c7b01d2f3786ad06e69d7c

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Citations in the corpus

A regime-switching Heston model for VIX and S&P 500 implied volatilities (2014, methodology): “…markets with Markov regime change were studied in Hamilton (2008), the importance of identifying regime change for GARCH models was identified in Hillebrand (2005), and the importance of jumps was highlighted in Ait-Sahalia et al. (2012), Bollerslev and Todorov (2011), Tauchen and Todorov…”
A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries (2008, background): “…in the context of structural breaks in the GARCH literature by Diebold (1986), Lamoureux and Lastrapes (1990), Mikosch and Starica (2004), and Hillebrand (2005), and in the literature on long memory models by Lobato and Savin (1998), Diebold and Inoue (2001), Granger and Teräsvirta (2001),…”
Asymptotic Properties of GARCH-X Processes (2015, methodology): “These works made use of either simulations or indirect approaches, but Mikosch and Starica (2004) and Hillebrand (2005) showed theoretically that the IGARCH could be generated, due to the behavior of estimators, by neglecting structural change.”
Volatility transmission between gold and oil futures under structural breaks (2013, background): “A structural break in the unconditional variance will result in a structural break in the GARCH process (see Hillebrand, 2005).”
Are Correlations Constant? Empirical and Theoretical Results on Popular Correlation Models in Finance (2017, unknown): “The existing literature on univariate volatility models shows that breaks in the volatility dynamics introduce a bias in the estimation which results in inaccurate volatility forecasts (Hamilton and Susmel, 1994; Hillebrand, 2005; Rapach and Strauss, 2008).”
Modelling structural breaks, long memory and stock market volatility: an overview (2005, background): “Characterisation of the break (Montanes et al., 2004; Perron and Zhu, 2004; Hillebrand, 2004) In a series of papers, Paul Newbold and his co-authors have argued that the presence of a break in a process which is integrated of order one under the null hypothesis (such as the characterisation presente”
Structural breaks and GARCH models of exchange rate volatility (2008, methodology): “2 Mikosch and StarM (2004) show this for Whittle estimators, while Hillebrand (2005) shows this for a wide class of estimators, including maximum likelihood and quasi maximum likelihood estimators, the most common estimators of GARCH models in the literature.”
Testing for Structural Breaks in Variance with Additive Outliers and Measurement Errors (2006, background): “…there is a growing interest to analyze variance stability in financial markets (see, among others, De Santis and Imrohoroglu 1997) and the effects of neglected breaks in time-series modelling (see Lamoureaux and Lastrapes 1990, Granger and Hyung 2004, Mikosch and Stărică 2004, and Hillebrand 2005).”
Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models? (2019, background): “It is claimed that when the model has high persistence the forecasts suffer, so that we can expect an improvement in forecasting performance when model persistence is reduced by properly accounting for structural changes (Hillebrand, 2005; Teterin, Brooks, & Enders, 2016).”
Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “In the literature on GARCH models, this is usually interpreted as either evidence for long memory (see, e.g., Baillie, Bollerslev, & Mikkelsen, 1996), structural breaks (see, e.g., Hillebrand, 2005), or an omitted persistent covariate (see Han & Park, 2014) in the conditional variance.”
Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns (2006, methodology): “Structural breaks in the intercept parameter of GARCH models have been studied in Hillebrand (2005). In this simplification, breaks cause spurious persistence but do not generate spurious asymmetries.”
Regime-switching stochastic volatility model: estimation and calibration to VIX options (2017, unknown): “The original introduction of regime switching in the econometric literature can be attributed to Hamilton [30], the relevance of regime shifts in return GARCH models is highlighted in Dueker [20], Hillebrand [32] and a multivariate Markov-modulated Gaussian model is calibrated to stock returns in Da”
Realized Volatility: A Review (2008, background): “Hillebrand (2005) also discussed the effects of breaks on the estimation of volatility models (see also Hillebrand and Medeiros, 2006).”