Cross‐sectional return dispersion and stock market volatility: Evidence from high‐frequency data

Niu, Zibo; Demirer, Rıza; Suleman, Muhammad Tahir; Zhang, Hongwei

doi:10.1002/for.2959

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…To evaluate the predictive performance of the regression model, we choose four different criteria, including root mean square error (RMSE), mean absolute error (MAE), median absolute error (MdE), and maximum absolute error (MAxE). These criteria are commonly used indicators for assessing the predictive performance of regression models (Niu et al, 2023;Sun et al, 2023;Yang et al, 2023). The specific formulas for calculating these criteria are shown below:…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…To evaluate the predictive performance of the regression model, we choose four different criteria, including root mean square error (RMSE), mean absolute error (MAE), median absolute error (MdE), and maximum absolute error (MAxE). These criteria are commonly used indicators for assessing the predictive performance of regression models (Niu et al, 2023; Sun et al, 2023; Yang et al, 2023). The specific formulas for calculating these criteria are shown below:

\sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(R_{i} - {\overset{R}{true}}_{i})}^{2}},

italicMAE goodbreak= \frac{1}{N} \sum_{i = 1}^{N} (||, R_{i} goodbreak- {\overset{truê}{R}}_{i}),

italicMdE goodbreak= median ((), {({}, R_{i} goodbreak- {\overset{truê}{R}}_{i})}_{i = 1}^{N}),

italicMAxE goodbreak= \max ((), {(||, R_{i} goodbreak- {\overset{truê}{R}}_{i})}_{i = 1}^{N}),

where N denotes the number of samples and

R_{i}

and

{\overset{truê}{R}}_{i}

indicate the true value and predicted value, respectively.…”

Section: Empirical Models and Datamentioning

confidence: 99%

See 1 more Smart Citation

The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame

Weng,

Cheng,

Zhuang

et al. 2024

Journal of Forecasting

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This study analyzes the effect of governance quality (six aspects: government effectiveness; control of corruption; voice and accountability; regulatory quality; political stability and absence of violence; and rule of law) on the renewable and nonrenewable energy consumption prediction based on the SHapely Additive exPlanations method for model analysis and interpretability. The empirical findings indicate that the time‐varying contributions of six aspects of governance quality on nonrenewable (renewable) energy consumption predicting vary greatly in E‐7 and G‐7 countries. The time‐varying contribution of governance quality within countries is heterogeneous and asymmetrical, especially India (Germany) in E‐7 countries (G‐7 countries). The prediction contribution distribution of governance quality between countries is more discrete in G‐7 countries than E‐7 countries. Our results are of great importance to policymakers and investors for enhancing the renewable energy consumption level in overcoming environmental challenges based on the country itself through governance quality.

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Section: Evaluation Metricsmentioning

confidence: 99%

“…To evaluate the predictive performance of the regression model, we choose four different criteria, including root mean square error (RMSE), mean absolute error (MAE), median absolute error (MdE), and maximum absolute error (MAxE). These criteria are commonly used indicators for assessing the predictive performance of regression models (Niu et al, 2023; Sun et al, 2023; Yang et al, 2023). The specific formulas for calculating these criteria are shown below: