Multiple Breakpoint Test on Crude Oil Price

Phoong, Seuk Wai; Phoong, Seuk Yen; Moghavvemi, Sedigheh; Phoong, Kok Hau

doi:10.2478/fman-2019-0016

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…Financial data alway exhibit time series components, including seasonality component, trend, cyclical and irregular changes. Statistically, in order to model the uncertainty of the series, there must be some characteristics that have to remain stable over the time [54]. As by an unit root test is able to determine the stationarity of the variables [54,55]; thus, we employ a group unit root test to generate the stationarity of the series.…”

Section: Test Of the Stationarity Of Datamentioning

confidence: 99%

“…Statistically, in order to model the uncertainty of the series, there must be some characteristics that have to remain stable over the time [54]. As by an unit root test is able to determine the stationarity of the variables [54,55]; thus, we employ a group unit root test to generate the stationarity of the series. Further, we also perform a unit root test using Zivot and Andrews's procedure, which allows for the existence of a possible structural break in the series [56].…”

Section: Test Of the Stationarity Of Datamentioning

confidence: 99%

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Energy–Finance–Growth Nexus in ASEAN-5 Countries: An ARDL Bounds Test Approach

et al. 2019

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This study examines the relationship between energy consumption, financial development and economic growth for ASEAN-5 countries, namely Malaysia, Indonesia, the Philippines, Singapore and Thailand, over the period from 1980 to 2017. Finance-growth and energy-growth relationships have been well researched; however, the energy-finance-growth nexus is an equally important but less explored area. Our Auto Regressive Distributed Lags (ARDL) bounds test for cointegration results suggests that the variables tend to move together in the long run for all countries, apart from Indonesia. Our study also considers the effect of a structural break due to financial crisis and confirms that the break does not affect the long-term relationship among the variables; in other words, the financial crisis does not affect the energy-finance-growth nexus. Hence, considering the consistency of energy consumption, the importance of the energy sector must not be undermined, and appropriate energy policies are instrumental in maintaining a well-managed financial sector for sustainable economic growth.Sustainability 2020, 12, 5 2 of 16 demand [15], while the importance of financial variables in investigatng the energy-growth nexus is well documented by [16]. Financial development indicates the actual level of financial resources available for production and channels these funds through banks and stock markets [17]; thus, this promotes economic growth by boosting investment via transparent transactions for productive ventures [15]. A higher level of economic development through raising the capacity of the production sector and attracting foreign investment requires a stable power supply, which necessitates support by the financial sector through channeling resources to the energy sector [18]. A well-developed financial market makes financial resources available to high-return projects; at the same time, this implies better accounting and reporting standards, which increases investors' confidence [19], and attracts Foreign Direct Investment(FDI) [7]; which in turnaffects energy consumption. Similarly, financial development increases energy consumption by enhancing liquidity through the allocation of assets to appropriate ventures [15]. Hence, financial development affects the growth of an economy, which may influence energy consumption [20], and consequently, can act as the missing link in the energy-growth nexus [5]. With the growth in the economy, more productive inputs are needed which are supported by activities such as capital accumulation and energy expenditure where the role of finance is undisputable [21].However, to date, the nature of the relationship remains inconclusive from the relatively small amount of prevailing literature. Some researchers have found a negative relationship between energy consumption and financial development [22], while others detect a positive relationship [13,23]. Similarly, the results of the cointegration and causality analysis-based studies are also inconclusive. Several studies reported bidirectional ...

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Section: Test Of the Stationarity Of Datamentioning

confidence: 99%

Section: Test Of the Stationarity Of Datamentioning

confidence: 99%

Energy–Finance–Growth Nexus in ASEAN-5 Countries: An ARDL Bounds Test Approach

et al. 2019

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“…To identify the possible structural breaks, the Quandt-Andrews breakpoint test was used to specify the null hypothesis of no break is occurring within 15% of the trimmed data. Trimming the sampling data is mainly used to ensure that the subsample is not close to the endpoint of the sample [2].…”

Section: Quandt-andrews Breakpoint Testmentioning

confidence: 99%

“…This estimator is able to provide asymptotically efficient estimates of the co-integrating vectors and adjustment parameters. Therefore, the Johansen test was applied in this study to examine the existence of co-integrating vectors among the variables [2].…”

Section: Co-integration Testmentioning

confidence: 99%

“…The non-stationarity of time series might be affected by time series components, such as the stochastic trend, the cyclical variation, or the seasonal variation. The non-stationary properties of financial data cannot simply be calculated through the application of filters in order to continue within the framework of stationary models [1,2]. A Markov switching model has been developed and thoroughly discussed [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Structural Changes in Financial Datasets Using the Breakpoint Test and the Markov Switching Model

Phoong

Phoong³

2020

Symmetry

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The price movements of commodities are determined by changes in the expectations about future economic variables. Crude oil price is non-stationary, highly volatile, and unstructured in nature, which makes it very difficult to predict over short-to-medium time horizons. Some analysts have indicated that the difficulty in forecasting the crude oil price is due to the fact that economic models cannot consistently show evidence of a strong connection between commodities and economic fundamentals, and, as a result, regarded the idea that economic fundamentals help predict price values as random luck. This study aimed to overcome the limitations of the economic models through the detection of structural changes as well as breaks in the data, using a breakpoint test. The Markov switching model is used to address the price patterns that led to a different market state. The results show that there are several changes as well as breaks in the estimated model. Moreover, there is an asymmetric correlation between the crude oil price and the GDP.

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