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This is a Quantitative Study study.

Nonparametric Inference of Conditional Expectile Functions in Large‐Scale Time Series Data With Improved Efficiency

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Nonparametric inference enhances computational efficiency and reduces error rates in risk management applications.

The proposed estimator improves computational efficiency for large-scale data, outperforming existing methods.
Simulations demonstrate that the new method has a smaller variance and mean square error compared to the IWLS estimator.
Application to S&P500 data shows effective computation for estimating the conditional expectile-based value-at-risk.
The innovative approach avoids iterative algorithms, enabling stable results with large sample sizes.

Zhang et al. (2025) studied this question.

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