The Relative Forecasting Performance of the Divisia and Simple Sum Monetary Aggregates

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.

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Forecasting performance comparison of daily maximum temperature using ARMA based methods

Journal of Physics Conference Series ◽

10.1088/1742-6596/1921/1/012041 ◽

2021 ◽

Vol 1921 ◽

pp. 012041

Author(s):

J Asha ◽

S Santhosh kumar ◽

S Rishidas

Keyword(s):

Performance Comparison ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Forecasting Performance

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Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

Financial Innovation ◽

10.1186/s40854-021-00247-z ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Serdar Neslihanoglu

Keyword(s):

State Space Model ◽

Additive Model ◽

Market Model ◽

Time Varying ◽

Daily Data ◽

Forecasting Performance ◽

Modeling And Forecasting ◽

Crypto Currency ◽

Beta Risk ◽

The Relationship

AbstractThis research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.

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