Granger causality for compressively sensed sparse signals

Kathpalia, Aditi; Nagaraj, Nithin

doi:10.1103/physreve.107.034308

Cited by 2 publications

(3 citation statements)

References 82 publications

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“…As Kathpalia and Nagaraj (2023) FTSE Bursa Malaysia KLCI in the post global COVID-19 pandemic. The co-in derived the long-run equilibrium of independent and dependent variables, and this trace test and maximum eigenvalue test to determine the presence of co-integration the determination of the optimal lag length is crucial for ensuring the accuracy present study's model, following the guidelines of Gujarati (2021).…”

Section: Methodology Research Design and Data Quantificationmentioning

confidence: 99%

“…Instead, it is primarily used to identify statistical dependencies or predictive relationships. AsKathpalia and Nagaraj (2023) has pointed out, whether conducted in the time domain or the frequency domain, Granger causality relies on a linear interaction framework established by the autoregressive model. The equations pertaining to Granger causality analysis were presented as Equation 7 to analyse the global COVID-19 pandemic variables and economic factors in relation to the FTSE Bursa Malaysia KLCI stock returns.…”

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confidence: 99%

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The Co-Integration and Causality Effect Between Global Covid-19 Pandemic and the Stock Market Return in Malaysia: An Exploratory Sequential Mixed Methods Approach

William Choo Keng Soon,

Mohd Yahya Mohd Hussin,

Chan Pui Yee

et al. 2024

JIS

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The COVID-19 pandemic is one of the most unparalleled disasters the world has ever seen. Previously, the global community hasfaced the Middle East Respiratory Syndrome (MERS), Ebola Virus Disease (EVD), and Severe Acute Respiratory (SARS). However,the tremendous rate of infection created by the global COVID-19 pandemic is unmatchable with its rapid spread all over the world. Therefore, it is considered as a Black Swan event as created previously by the 2008 financial crisis, SARS, and 9/11 terrorist attack. All these events have triggered panic selling that affected the confidence of investors and businesses. Subsequently, stock market performance has affected the choice of investment decisions that translated into stock return. Therefore, this paper was undertaken to investigate the co-integration and causality relationship among global COVID-19 daily infected cases, COVID-19 recoveries rate, COVID-19 death rate, investor sentiment, government policy, foreign exchange rate, Malaysia gold price, and crude oil price towards the KLCI stock market return. This study employed an exploratory sequential mixed methods approach along with Johansen and Julius Co-Integration and Granger Causality in explaining the Malaysia stock market return. The empirical analysis concluded that COVID-19 daily cases, COVID-19 recoveries rate, government policy and foreign exchange have had a long-term effect in explaining the Malaysia stock return. On the other hand, all the study variables, except for government policy indicated a short-term effect on the Malaysia stock return. This study will contribute significantly to the body of finance literature on the impact of a disease outbreak, with highlights on the considerations of investors on the global COVID-19 pandemic in illuminating the variation of Malaysia stock market returns.

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Section: Methodology Research Design and Data Quantificationmentioning

confidence: 99%

mentioning

confidence: 99%

The Co-Integration and Causality Effect Between Global Covid-19 Pandemic and the Stock Market Return in Malaysia: An Exploratory Sequential Mixed Methods Approach

William Choo Keng Soon,

Mohd Yahya Mohd Hussin,

Chan Pui Yee

et al. 2024

JIS

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“…With the growing number of potential applications, Granger causality analysis is increasingly being employed to investigate multivariate and multiscale data scenarios. Moreover, recent research has explored the potential to extend Granger causality analysis to the compression domain, highlighting the versatility and adaptability of this technique to various applications and fields [9].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Multiscale Granger Causality Analysis Based on State Space Models

Zhang

Cheng

2023

Symmetry

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Granger causality (GC) is a popular method in causal linkage recovery and has been applied to various fields, such as economics and neuroscience. While the conventional Granger causality model is capable of identifying symmetrical causal relationships among variables, it is the asymmetric Granger causality that provides a more comprehensive perspective of the short- and long-term interactions between variables, which is of greater value for empirical study. Traditional vector autoregressive models lack the ability to explore multiscale information flow and are affected by the moving average component. Therefore, by combining the wavelet-based approach and state space model, we propose a new Granger causality analysis method to overcome the inherent limitation of vector autoregressive models and extend to multiscale causality exploration. Two simulations were conducted to compare the proposed approach to an existing wavelet-based method, and five evaluation indicators were utilized. The results indicate that the proposed method efficiently identifies the accurate asymmetric causalities at varying scales, while improving accuracy and reducing bias as compared to the current wavelet-based method. In conclusion, the combination of the wavelet approach and state space method enhances the multiscale causality detecting capability and can potentially contribute to multiscale Granger causality research.

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Granger causality for compressively sensed sparse signals

Cited by 2 publications

References 82 publications

The Co-Integration and Causality Effect Between Global Covid-19 Pandemic and the Stock Market Return in Malaysia: An Exploratory Sequential Mixed Methods Approach

The Co-Integration and Causality Effect Between Global Covid-19 Pandemic and the Stock Market Return in Malaysia: An Exploratory Sequential Mixed Methods Approach

Wavelet Multiscale Granger Causality Analysis Based on State Space Models

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