Evidence map›Paper›PMID 37313159›Full record

ArticleHeliyon2023

Risk measurement of China's green financial market based on B-spline quantile regression.

Yuexu Zhao, Weiqi Xu

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yuexu ZhaoCollege of Economics, Hangzhou Dianzi University, Hangzhou, 310018, China.
Weiqi XuCollege of Economics, Hangzhou Dianzi University, Hangzhou, 310018, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To accurately measure the spillover effect of China's green financial carbon emission market, a new measurement of conditional value at risk (CoVaR) based on the B-spline quantile methods is proposed. Firstly, the variable coefficient CoVaR model is constructed, and the model coefficients are estimated by the B-spline quantile method. Then, the relationship between Δconditional value at risk (ΔCoVaR) and value at risk (VaR) is considered. In the empirical analysis, we investigate five carbon trading quota risk measurements of the carbon emission projects in China from 2014 to 2022, and verify the B-spline superiority by Monte Carlo simulation. The empirical results show that B-spline method has the highest risk fitting success rate and the smallest error.

Indexed as

B-splineFinancial marketMonte Carlo simulationValue at risk

Identifiers

PMID37313159
PMCPMC10258423

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.