A GARCH model for testing market efficiency

Narayan, Paresh Kumar; Liu, Ruipeng; Westerlund, Joakim

doi:10.1016/j.intfin.2015.12.008

Cited by 73 publications

(63 citation statements)

References 61 publications

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“…However, while the tests became available following Lee and Strazicich (2003), subsequent work (see, for instance, Narayan and Popp, 2010) took issue with the precision in estimating the break dates themselves, because accurate identification of breaks has implications for precise understanding of the form of the data (Narayan and Popp, 2010). More recent work (Narayan and Liu, 2015;Narayan, Liu and Westerlund, 2016) takes issue with the fact that when modeling for unit roots, it is not only structural breaks that are important, but also the role of a time trend and data heteroskedasticity can be equally important in delivering an unbiased understanding of the data.…”

Section: Introductionmentioning

confidence: 99%

Understanding Indonesia’s Macroeconomic Data: What Do We Know and What Are the Implications?

Sharma¹

2018

BEMP

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Unit root properties of macroeconomic data are important for both econometric modelling specifications and policy making. The form of variables (whether they are a unit root process) helps determine the correct econometric modelling. Equally, the form of variables helps explain how they react to shocks (both internal and external). Macroeconomic time-series data are often at the forefront of shock analysis and econometric modelling. There is a growing emphasis on research on Indonesia using time-series data; yet, there is limited understanding of data characteristics and shock response of these data. Using an extensive dataset comprising 33 macroeconomic time-series variables, we provide an informative empirical analysis of unit root properties of data. We find that regardless of data frequencies the empirical evidence of unit roots is mixed, some series respond quickly to shocks others do take time, and almost every macroeconomic data suffers from structural breaks. We draw implications of these findings.

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Section: Introductionmentioning

confidence: 99%

Understanding Indonesia’s Macroeconomic Data: What Do We Know and What Are the Implications?

Sharma¹

2018

BEMP

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“…The results of the calibrated heteroscedastics model reveal that existence of stationary can be established for corn, oat and wheat. In order words, we reject the null hypothesis of unit root test for these variables when we employed GARCH based unit root test proposed by (Westerlund, J. and Narayan, 2015) and (Narayan, P. K., Liu, R. and Westerlund, 2016). From the results we can equally observed that the null unit root cannot be rejected for rice and soybean using GARCH based models 2 .…”

Section: Conducting Research and Resultsmentioning

confidence: 76%

“…This implies that commodity prices are efficient and that shock to price movement is permanent. We extend our analysis by calibrating test for heteroscedastic in the model by employing GARCH-based unit root test such as the (Westerlund & Narayan, 2015) hereafter (W&N, 2015) and (Narayan, Liu, & Westerlund, 2016)…”

Section: Methodsmentioning

confidence: 99%

Testing mean-reversion in agricultural commodity prices: Evidence from wavelet analysis

Lawal¹,

Omoju²,

Babajide³

et al. 2019

Journal of International Studies

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This study examines the validity of the random walk hypothesis for some selected soft agricultural commodity prices within the context of heterogeneous market hypothesis and mean reversion hypothesis. The study employs a battery of traditional unit root tests, GARCH-based models and a novel frequency-based wavelet analysis to analyze daily data sourced from 6th of Jan 1986 to 29 th Dec 2018. Contrary to other existing studies that employed only traditional time domain unit root tests, our results reveal that soft commodity prices are mean reverting, suggesting the existence of potential excess returns for investors. Overall, our results show that the selected soft commodity series are inefficient when we factored in heteroscedascity and frequency domain into our model. Our study is an improvement on the existing studies as we analyze our data using both time and frequency domain estimates. Besides, unlike other studies that did not offer structural breaks, the current study provides structural break dates with major events in the global socioeconomic space, which are key to identifying the date of bubbles and potential signs of commodity price bubbles. Our findings have some critical implications for investors, policy makers,

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“…While researchers like Vortelinos (2015) used HAR (Heterogeneous Auto-Regressive Model); Principal Components Combining; Neural networks; GARCH; Volatility studies in the study. GARCH models were used to test the efficiency of the markets (Narayan, 2016). Aboura and Chevallier (2013) highlights of the effect of implied volatility on WTI benchmark prices.…”

Section: Review Of Literaturementioning

confidence: 99%

Understanding volatility and leverage effects in bunker markets

Ranjan¹,

Raju

Jayaraj

et al. 2018

IJGEI

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The research analysed the volatility and the leverage effect in the bunker prices in IFO 380 and IFO 180 grades. Limited studies were found on bunker prices using EGARCH and bivariate analysis. The research investigated the causal relationships between IFO 380 and IFO 180 grades of bunkers. Serial correlations were found through using Lagrange multiplier (LM test) and heteroskedastic tests in the bunker price data. The analysis exhibited the model to be highly significant at 1%. Both the grades showed the short and long-run shocks and a positive leverage effect. The recommendations were made to the maritime industry to strategise during the long run and short-run shocks of bunker markets.

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A GARCH model for testing market efficiency

Cited by 73 publications

References 61 publications

Understanding Indonesia’s Macroeconomic Data: What Do We Know and What Are the Implications?

Understanding Indonesia’s Macroeconomic Data: What Do We Know and What Are the Implications?

Testing mean-reversion in agricultural commodity prices: Evidence from wavelet analysis

Understanding volatility and leverage effects in bunker markets

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