Robust Lagrange multiplier test for detecting ARCH/GARCH effect using permutation and bootstrap

Gel, Yulia R.; Chen, Bei

doi:10.1002/cjs.11149

Cited by 9 publications

(5 citation statements)

References 60 publications

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“…Campbell and Diebold (2005) claim that their preprocessed (deseasonalized and autoregressive filtered) daily mean temperatures exhibit conditional heteroscedasticity. Remarkably, in our case of mean monthly data, both the 2 -approximated and bootstrap-based LM tests also yield highly statistically significant p-values, implying the likely existence of ARCH effects (Engle, 1982;Lee, 1991;Gel and Chen, 2012). Finally, in terms of trend, Campbell and Diebold (2005) assume a linear trend for their model of preprocessed daily surface temperatures, but they do not present any testing results.…”

Section: Water Pollution: the Turkey Lake Watershed Study 1980-2003mentioning

confidence: 58%

On detecting non‐monotonic trends in environmental time series: a fusion of local regression and bootstrap

2013

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In this paper, we propose a new testing procedure for detecting smooth (non)monotonic trends embedded into a linear noise that possibly does not degenerate to a finite‐dimensional representation or into a conditionally heteroscedastic (autoregressive conditionally heteroscedastic/generalized autoregressive conditionally heteroscedastic (ARCH/GARCH)) noise. The proposed nonparametric trend test is local regression‐based, and we develop a flexible and computationally efficient hybrid bootstrap procedure to approximate its finite sample behavior. Because the proposed trend test does not assume prior knowledge on the dependence structure and probability distribution of the observed process, the new testing procedure is fully data‐driven and robust to misspecification of dependence structure and distributional assumptions, which is of particular importance for noisy environmental measurements. Moreover, because the proposed methodology allows to test for monotonic versus non‐monotonic trends and hence, to assess existence of extremums in the hypothesized trend function, the developed approach may be also employed for preliminary detection of regime shifts and change points in the observed environmental data series. Our simulation studies indicate competitive performance of the proposed nonparametric procedure for detection of (non)monotonic trends against conventional trend tests. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Water Pollution: the Turkey Lake Watershed Study 1980-2003mentioning

confidence: 58%

On detecting non‐monotonic trends in environmental time series: a fusion of local regression and bootstrap

2013

Self Cite

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“…Even if we assume a GARCH model to be heteroskedastic, the testing procedure is the same as that in by Lee (1991) and Gel and Chen (2012). Therefore, we focus only on the ARCH test.…”

Section: Arch Tests Using Nonparametric Regression Approaches For Conmentioning

confidence: 99%

“…Catani and Ahlgren (2017) propose an LM test for ARCH using high-dimentional vector autoregressive models. In addition, Gel and Chen (2012) introduce bootstrap ARCH tests. 3.…”

Section: Fundingmentioning

confidence: 99%

Robust tests for ARCH in the presence of a misspecified conditional mean: A comparison of nonparametric approaches

Maki

Ota

2021

Cogent Economics & Finance

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“…In financial data analysis, the detection of GARCH effects can use the Lagrange Multiplier (LM) test (Gel & Chen, 2012). One of the important steps before applying GARCH methodology is to examine the residual of risk and return for evidence of heteroscedasticity.…”

Section: Multivariate Generalized Autoregressive Conditional Heteroscedasticitymentioning

confidence: 99%

Capital Market Volatility MGARCH Analysis: Evidence from Southeast Asia

Rusmita¹,

Rani²,

Swastika³

et al. 2020

JAFEB

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This paper is aimed to explore the co-movement capital market in Southeast Asia and analysis the correlation of conventional and Islamic Index in the regional and global equity. This research become necessary to represent the risk on the capital market and measure market performance, as investor considers the volatility before investing. The time series daily data use from April 2012 to April 2020 both conventional and Islamic stock index in Malaysia and Indonesia. This paper examines the dynamics of conditional volatilities and correlations between those markets by using Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH). Our result shows that conventional or composite index in Malaysia less volatile than Islamic, but on the other hand, both drive correlation movement. The other output captures that Islamic Index in Indonesian capital market more gradual volatilities than the Composite Index that tends to be low in risk so that investors intend to keep the shares. Generally, the result shows a correlation in each country for conventional and the Islamic index. However, Internationally Indonesia and Malaysia composite and Islamic is low correlated. Regionally Indonesia's indices movement looks to be more correlated and it's similar to Malaysian Capital Market counterparts. In the global market distress condition, the diversification portfolio between Indonesia and Malaysia does not give many benefits.

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Robust Lagrange multiplier test for detecting ARCH/GARCH effect using permutation and bootstrap

Cited by 9 publications

References 60 publications

On detecting non‐monotonic trends in environmental time series: a fusion of local regression and bootstrap

On detecting non‐monotonic trends in environmental time series: a fusion of local regression and bootstrap

Robust tests for ARCH in the presence of a misspecified conditional mean: A comparison of nonparametric approaches

Capital Market Volatility MGARCH Analysis: Evidence from Southeast Asia

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