Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting

Orzeszko, Witold

doi:10.3390/en14196043

Cited by 14 publications

(4 citation statements)

References 59 publications

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“…9 According to the results in Table 3, the real exchange rate neither Granger causes, nor is Granger caused by, the price of oil. The latter of these results supports the finding reported in a recent study by Marquez (2022) while both results support some of the findings reported in a recent study by Orzeszko (2021). Neither of these results is supported by those in a recent study by Przekota and Szczepa ńska-Przekota (2022).…”

Section: Conflicts Of Interestsupporting

confidence: 88%

Causal Relationships between Oil Prices and Key Macroeconomic Variables in India

Upadhyaya,

Nag,

Mixon

2023

IJFS

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India is among the largest and fastest-growing economies in the world. To continue its growth, energy is and will continue to be one of its most important considerations. With a population of over one billion, India is the third largest consumer of petroleum on the globe. To maintain this ranking, India imports a large percentage of its total oil consumption. Given India’s current position as a large importer of oil, how does oil price volatility affect the Indian economy? This paper examines the effect of oil price volatility on inflation, economic growth, and the stock market in India. Statistical tests suggest that the overall price level, the real effective exchange rate, and oil prices are negatively related to aggregate output in the long run. Granger causality test results derived from a vector error correction model support bidirectional causality between oil prices and aggregate output, indicating that a change in oil prices also affects aggregate output in the short run.

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Section: Conflicts Of Interestsupporting

confidence: 88%

Causal Relationships between Oil Prices and Key Macroeconomic Variables in India

Upadhyaya,

Nag,

Mixon

2023

IJFS

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“…In recent years, machine learning algorithms have gained popularity for predictive purposes. They are data-driven, self-adaptive methods requiring very few assumptions about investigated data, which are capable of approximating non-linear relations based on noisy and non-stationary data (Orzeszko, 2021;Fiszeder & Orzeszko, 2021). Among the most popular machine learning algorithms are decision trees and other tree-based methods.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence and customers’ intention to use robo-advisory in banking services

Piotrowski,

Orzeszko

2023

Self Cite

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Research background: Robo-advisory is a modern and rapidly developing area of implementing artificial intelligence to support customer decision-making. The current significance of robo-advisory to the financial sector is minor or marginal, and boils down to formulating recommendations and implementing investment strategies. However, the ongoing digital transformation of the economy leads us to believe that in the near future this technology will also be much more widely used with banking products. This makes it necessary for banks and other financial institutions to be prepared to offer this service to their customers. Purpose of the article: The aim of this paper is to identify factors significantly influencing bank customers’ intention to use robo-advisory. Identification of robo-advisory acceptance factors may increase the effectiveness of banks' promotional activities regarding such a service. Methods: Empirical data was obtained through a survey conducted on a representative sample of 911 Polish respondents aged 18–65. Using a multilevel ordered logit model and methods based on machine learning algorithms, the authors identified variables relating to the demographic and socio-economic characteristics, behaviors, and attitudes of consumers that primarily determine respondents’ adoption of robo-advisory. Findings & value added: The results of the study indicate that the variables regarding the respondents' attitude towards the use of artificial intelligence in banking services turned out to be the most important from the point of view of acceptance of robo-advisory. Next in terms of importance were the variables presenting respondents' assessments of the ethics of financial services. An important finding is that experience in using basic financial services is not a significant factor when accepting robo-advisory. From the practical perspective, the article provides recommendations on the use of artificial intelligence technology in finance and ethical aspects of the provision of such services by banks.

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“…In the studies presented in the literature, the value of a distance measure ε between 0.5 and 1.5 is recommended for consideration (Orzeszko, 2021).…”

Section: Nonlinear Granger Causalitymentioning

confidence: 99%

Untitled

2023

SPSUTOM

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Artificial intelligence (AI) can be applied in various areas of crisis management. It can provide numerous benefits concerning speed, effectiveness and precision of actions. AI accelerates the process of massive amounts of data processing, such as sensor data, satellite images, social data streams and much more. This enables to make quick analysis of a situation and make quick decisions in order to monitor various sources of information in real-time and detect early signals of a crisis. Advanced algorithms can identify patterns and anomalies enabling a quick response. AI can be used to model various crisis scenarios based on available historical and current data. This way it is possible to predict the probable effects of a given crisis and prepare for various situations. AI can analyze social media data to monitor community sentiments, identify potential threats and manage crisis communication. Automatic sentiment analysis systems can help understand society's response to a crisis. Advanced AI systems can be used to predict human behavior in various crisis scenarios. This allows to understand better how people may react and what their needs are in a given time. AI can help detect and combat cyber attacks, which constitute a serious threat, especially when it is necessary to maintain critical infrastructures. Integrating AI into crisis management requires caution because decisions made by AI systems should be consistent with ethical standards and they must consider the human supervision and assessment of the crisis situation. Design/methodology/approach: The paper analyzes the AI methods applied during crises. The results of interviews conducted with students, teachers and authors' personal experiences are provided. Findings: AI is a new way of solving problems, especially during crises. The new technologies determine the way and the speed of problems solving. Originality/value: Originality based on the interviews and post-crisis (COVID) points of view are the value.

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Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting

Cited by 14 publications

References 59 publications

Causal Relationships between Oil Prices and Key Macroeconomic Variables in India

Causal Relationships between Oil Prices and Key Macroeconomic Variables in India

Artificial intelligence and customers’ intention to use robo-advisory in banking services

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