Outliers and GARCH models in financial data

Charles, Amélie; Darné, Olivier

doi:10.1016/j.econlet.2004.07.019

Cited by 76 publications

(54 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that Charles and Darné (2005) extend the above test for additive outliers to take into account innovative outliers in a GARCH model, that is outliers that reflect an endogenous change in a series and affect all future realizations of the variable through the memory of its process.…”

Section: Resultsmentioning

confidence: 99%

“…In a univariate GARCH setting, Sakata and White (1998), Franses and Ghijsels (1999), Carnero, Pena, andRuiz (2007, 2008), Charles and Darné (2005) and Muler and Yohai (2008) show that, in the presence of additive jumps, the Gaussian Quasi-Maximum Likelihood (QML) estimator of GARCH models tends to overestimate the volatility for the days following a jump and to produce upward biased estimates of the long-run volatility. 3…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Laurent

Lecourt

Palm

2016

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

Financial series occasionally exhibit large changes. To deal with those events, we assume that the observed return series consists of a conditionally Gaussian ARMA-GARCH (or -GJR) model contaminated by an additive jump component. In this framework, we propose a new test for additive jumps. The test is based on standardised returns, where the first two conditional moments of the non-contaminated observations are estimated in a robust way. Simulation results indicate that the test has very good finite sample properties, i.e. correct size and high proportion of correct jump detection. We apply our test on daily returns and detect less than 1% of jumps for the three exchange rates and between 1 and 3% of jumps for about 50 large capitalization stocks from the NYSE. Once jumps have been filtered out, all series are found to be conditionally Gaussian. We also find that simple GARCH-type models estimated on filtered returns deliver better out-of-sample forecasts of the conditional variance than GARCH and GAS models estimated on raw data.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Laurent

Lecourt

Palm

2016

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

show abstract

“…Future research is encouraged to address this issue. 15 (BHHH) algorithm. 16 For each Table, (Table 5).…”

Section: Results Of the Persistence Estimatesmentioning

confidence: 99%

Volatility persistence in crude oil markets

Charles

Darné

2014

Energy Policy

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, the high persistence measures may reflect structural changes in the mean or variance of growth rates, which the GARCH estimations ignore (Diebold, 1986;Lamoureux and Lastrapes, 1990;Mikosch and Stărică, 2004;Hillebrand, 2005;Krämer and Azamo, 2007;and Fang and Miller, 2008). Following Franses and Ghijsels (1999) and Charles and Darné (2005), we, first, employ the method of Chen and Liu (1993) to detect and correct for additive outliers (AOs) and 6 Balke and Fomby (1994) analyze fifteen post-World War II U.S. macroeconomic time series using the outlier identification procedure based on Tsay (1988) and find that outliers may prove important for U.S. macroeconomic data, and such aberrant observations may lead to large ARCH test statistics. van Dijk, Franses, and Lucas (1999) demonstrate that neglecting additive outliers frequently leads to a rejection of the null hypothesis of homoskedasticity, when it is in fact true.…”

mentioning

confidence: 99%

Modeling the volatility of real GDP growth: The case of Japan revisited

Fang

Miller

2009

Japan and the World Economy

View full text Add to dashboard Cite

Previous studies (e.g., Hamori, 2000;Ho and Tsui, 2003;Fountas et al., 2004) find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Perrons (1998, 2003) multiple structural change test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the time-varying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility. Journal of Economic Literature Classification: C32, E32, O40

show abstract

Outliers and GARCH models in financial data

Cited by 76 publications

References 9 publications

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Volatility persistence in crude oil markets

Modeling the volatility of real GDP growth: The case of Japan revisited

Contact Info

Product

Resources

About