Wavelet variance ratio cointegration test and wavestrapping

Eroğlu, Burak Alparslan

doi:10.1016/j.jmva.2018.12.011

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The most rigorous approach to detecting stationarity in time series data is using statistical tests developed to detect specific types of stationarity, such as simple parametric models that generate the stochastic process. Among them, it is important to mention the following: (i) Augmented Dickey-Fuller (ADF) test [ 69 ]; (ii) Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test [ 70 , 71 ]; (iii) Variance ratio test [ 72 ]; (iv) Leybourne-McCabe (LMC) test [ 73 ]; and (v) Phillips-Perron (PP) test [ 74 ].…”

Section: Methodsmentioning

confidence: 99%

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

Hernández

Sánchez-Sutil

Caño

et al. 2020

Sensors

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Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its effectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. Nonetheless, the accuracy of these features was found to largely depend on the load profile analyzed. Indeed, in some households, accurate descriptions were obtained with coarse-grained data. In any case, an intermediate data-resolution of 5 s showed feature characterization closer to those of 0.5 s.

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Section: Methodsmentioning

confidence: 99%

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

Hernández

Sánchez-Sutil

Caño

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…According to (Eroğlu, 2019) at this time many researchers make co-integration test as a test to determine the long-term relationship of the research variable, so that it looks integrated or not. Co-integration test is conducted to find out how the balance of research variables is in the long run.…”

Section: Co-integration Testmentioning

confidence: 99%

Causality of the Growth of Bitcoin Prices with Monetary Conditions in Indonesia

Aimon¹,

Putra²,

Husein³

2020

Proceedings of the 4th Padang International Conference on Education, Economics, Business and Accounting (PICEEBA-2 2019)

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Virtual currency has been a hot topic for discussion lately. One of the popular virtual currencies is bitcoin which has a status as a commodity in Indonesia today. The purpose of this study is to determine whether there is influence and causality between bitcoin and monetary variables in Indonesia. The study used time series data in monthly form March 2012 to July 2019. The study used Johnsen's co integration test, vector auto regression estimation, granger causality test, forecasting with Impulse Response Function (IRF), and decomposition variance analysis. In this research, it is known that in the long run the growth of bitcoin prices, the exchange rate, inflation, the price of gold, and interest rates does not have balance and similarity in movement. Causally there is a direct relationship between the price of gold and the exchange rate with the consumer price index. In the VAR estimation it is known that bitcoin price growth affects the consumer price index in Indonesia, and the consumer price index affects the price of gold. In impulse response function forecasting there are positive and negative responses of each variable in the study of other variables. In decomposition variance analysis, it is known that the contribution of influence of a variable is more dominated by the variable itself when shock or shock occurs.

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Nexus Between COVID-19 Infections, Exchange Rates, Stock Market Return, and Temperature in G7 Countries: Novel Insights From Partial and Multiple Wavelet Coherence

Singh

Bansal

Bhardwaj

et al. 2021

Front. Environ. Sci.

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This study attempts to analyze the time-varying pattern between the exchange rates, stock market return, temperature, and number of confirmed COVID-19 cases in G7 countries caused by the COVID-19 pandemic. We have implemented our analysis using wavelet coherence and partial wavelet coherence (PWC) on independent variables from January 4, 2021 to July 31, 2021. This paper contributes to the earlier work on the same subject by employing wavelet coherence to analyze the effect of the sudden upsurge of the COVID-19 pandemic on exchange rates, stock market returns, and temperature to sustain and improve previous results regarding correlation analysis between the above-mentioned variables. We arrived at the following results: 1) temperature levels and confirmed COVID-19 cases are cyclical indicating daily temperatures have a material bearing on propagating the novel coronavirus in G7 nations; 2) noteworthy correlations at truncated frequencies show that a material long-term impact has been observed on exchange rates and stock market returns of G7 and confirmed COVID-19 cases; 3) accounting for impact of temperature and equity market returns, a more robust co-movement is observed between the exchange rate returns of the respective nations and the surge in COVID-19 cases; and 4) accounting for the influence of temperature and exchange rate returns and the increase in the confirmed number of coronavirus-infected cases and equity returns, co-movements are more pronounced. Besides academic contributions, this paper offers insight for policymakers and investment managers alike in their attempt to navigate the impediments created by the coronavirus in their already arduous task of shaping the economy and predicting stock market patterns.

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Wavelet variance ratio cointegration test and wavestrapping

Cited by 3 publications

References 26 publications

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

Causality of the Growth of Bitcoin Prices with Monetary Conditions in Indonesia

Nexus Between COVID-19 Infections, Exchange Rates, Stock Market Return, and Temperature in G7 Countries: Novel Insights From Partial and Multiple Wavelet Coherence

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