Assessing the bias of maximum likelihood estimates of contaminated garch models

Mendes, Beatriz Vaz de Melo

doi:10.1080/00949650008812051

Cited by 22 publications

(18 citation statements)

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“…Finally, it is also important to notice that if the sample size is not very large, it is possible to obtain estimates that do not satisfy the usual non‐negativity restrictions (see, e.g. the simulation results in Mendes, 2000).…”

Section: Effects Of Outliers On the Estimation Of Arch Modelsmentioning

confidence: 99%

“…Muller and Yohai, 2002, who show that the mean squared error of the ML estimator of the parameters of ARCH(1) models is dramatically influenced by isolated outliers]. On the other hand, Mendes (2000) shows that the influence functional of the ML estimator of the parameters of an ARCH(1) model is the product of a constant vector by a quadratic function of the outlier size. Consider the simplest ARCH(1) model.…”

Section: Effects Of Outliers On the Estimation Of Arch Modelsmentioning

confidence: 99%

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Effects of outliers on the identification and estimation of GARCH models

Carnero

Peña

Ruiz

2006

Journal Time Series Analysis

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Abstract. This paper analyses how outliers affect the identification of conditional heteroscedasticity and the estimation of generalized autoregressive conditionally hetero scedastic (GARCH) models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations generated by stationary processes and show that the properties of some conditional homoscedasticity tests can be distorted. Second, we obtain the asymptotic and finite sample biases of the ordinary least squares (OLS) estimator of ARCH(p) models. The finite sample results are extended to generalized least squares (GLS), maximum likelihood (ML) and quasi maximum likelihood (QML) estimators of ARCH(p) and GARCH(1,1) models. Finally, we show that the estimated asymptotic standard deviations are biased estimates of the sample standard deviations.

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Section: Effects Of Outliers On the Estimation Of Arch Modelsmentioning

confidence: 99%

Section: Effects Of Outliers On the Estimation Of Arch Modelsmentioning

confidence: 99%

Effects of outliers on the identification and estimation of GARCH models

Carnero

Peña

Ruiz

2006

Journal Time Series Analysis

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“…12 A possible explanation for these findings could be the presence of small patches of outliers in our sample. 13 It is well known that the estimates based on normal likelihood are very sensitive to the presence of few outliers in the sample [23,24]. In fact, authors like Muler and Yohai [24] have reminded us that a single huge outlier may have a very large effect on the QML estimates.…”

Section: Resultsmentioning

confidence: 97%

Inflation uncertainty and economic growth: evidence from the LAD ARCH model

Baharumshah

Hamzah

Sabri

2011

Journal of Applied Statistics

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In this paper, we combined the panel data and least absolute deviation autoregressive conditional heteroscedastic (ARCH) (L1-ARCH) model to infer on the relationship between inflation uncertainty and economic growth in five emerging market economies. Two interesting findings emerged from the analysis; first, we confirmed that the inflation uncertainty has a significant and negative effect on economic growth. Second, inflation is also an important variable and it is detrimental to economic prospects in the fast-growing Association of Southeast Asian Nations (ASEAN) economies. All in all, the empirical findings suggest that greater stability in the economy may be desirable in order to stimulate economic growth in the region.inflation uncertainty, economic growth, LAD ARCH,

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“…Outliers can affect identification and estimation of the GARCH-type models (Carnero et al, 2007 and 2012); they can wrongly suggest conditional heteroscedasticity or hide true heteroscedasticity (see e.g., Balke and Fomby, 1994;Dijk et al, 1999;Franses and Ghijsels, 1999;Aggarwal et al, 1999;Carnero et al, 2007); they can bias the GARCH parameters estimation (see e.g., Sakata and White, 1998;Mendes, 2000;Charles, 2008); and they can affect out-of-sample forecasts (see e.g., Franses and Ghijsels, 1999;Carnero et al, 2007;Charles, 2008).…”

Section: Introductionmentioning

confidence: 99%

The role of outliers and oil price shocks on volatility of metal prices

Behmiri

Manera

2015

Resources Policy

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SummaryThis study investigates the price volatility of metals, using the GARCH and GJR models. First we examine the persistence of volatility and the leverage effect across metal markets taking into account the presence of outliers, and second we estimate the effects of oil price shocks on the price volatility of metals, allowing for the asymmetric responses. We use daily spot prices for the selected metals, including aluminum, copper, lead, nickel, tin, zinc, gold, silver, palladium and platinum.The main findings indicate that, returns show a high degree of volatility persistence before and after correcting outliers, outliers bias the parameters estimation of the GARCH-type models, and removing outliers improves the performance of models in capturing volatility. However in a comparison, Student-t distribution outperforms the approach of correcting outliers in capturing volatility. Moreover, we find the existence of inverse leverage effect for seven metals, the leverage effect for copper and no leverage effect for nickel and palladium. Finally, price volatility of metals differently reacts to oil price shocks and there is an asymmetric reaction of volatility to oil price shocks for seven metals.Keywords: Metals, Commodities, Volatility, Oil Price, Outliers. JEL Classification: G13, Q4, C1Acknowledgments: We thank participants to the FEEM seminars. Moreover, the authors acknowledge financial support from the "Energy: Resources and Markets" research program of Fondazione Eni Enrico Mattei (FEEM).

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Assessing the bias of maximum likelihood estimates of contaminated garch models

Cited by 22 publications

References 20 publications

Effects of outliers on the identification and estimation of GARCH models

Effects of outliers on the identification and estimation of GARCH models

Inflation uncertainty and economic growth: evidence from the LAD ARCH model

The role of outliers and oil price shocks on volatility of metal prices

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