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<p>Identifying the intensity of risk spillover in China’s financial market can provide an important empirical basis and information reference for monitoring and preventing financial risks. This paper uses a multi-objective application of particle swarm optimization algorithm under a multi-objective optimization algorithm to induce the MOPSO algorithm. The algorithm identifies and measures the intensity of risk spillover in China’s financial market in two aspects, including the risk spillover from policy instability to the stock market and the two-way risk spillover between the financial industry and the real estate industry. Regarding policy instability, the risk spillover intensities of downside fiscal policy, monetary policy, trade policy, and foreign exchange policy are 14.83%, 53.88%, 7.54%, and 31.06%, respectively. Regarding the two-way risk spillover intensity, the average risk spillover intensity of real estate to finance is 70.28%, which is 5.57 percentage points higher than that of finance. This indicates that the multi-objective optimization algorithm can identify and measure the risk spillover intensity of China’s financial market, providing data support and information reference for preventing financial risks.</p>
</abstract>

Identifying the intensity of risk spillover in China’s financial market can provide an important empirical basis and information reference for monitoring and preventing financial risks. This paper uses a multi-objective application of particle swarm optimization algorithm under a multi-objective optimization algorithm to induce the MOPSO algorithm. The algorithm identifies and measures the intensity of risk spillover in China’s financial market in two aspects, including the risk spillover from policy instability to the stock market and the two-way risk spillover between the financial industry and the real estate industry. Regarding policy instability, the risk spillover intensities of downside fiscal policy, monetary policy, trade policy, and foreign exchange policy are 14.83%, 53.88%, 7.54%, and 31.06%, respectively. Regarding the two-way risk spillover intensity, the average risk spillover intensity of real estate to finance is 70.28%, which is 5.57 percentage points higher than that of finance. This indicates that the multi-objective optimization algorithm can identify and measure the risk spillover intensity of China’s financial market, providing data support and information reference for preventing financial risks.

A multi-objective optimization algorithm based on the identification of risk spillover intensity measurement in Chinese financial markets

Guilin University of Electronic Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Identifying the intensity of risk spillover in China’s financial market can provide an important empirical basis and information reference for monitoring and...

A multi-objective optimization algorithm based on the identification of risk spillover intensity measurement in Chinese financial markets

Online veröffentlicht: 16. Sept. 2023

Seitenbereich: -

Eingereicht: 30. Okt. 2022

Akzeptiert: 27. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00334

Schlüsselwörter
Multi-objective optimization algorithm, Particle swarm algorithm, MOPSO algorithm, Risk spillover intensity, Chinese financial market

© 2023 Rui Mao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A multi-objective optimization algorithm based on the identification of risk spillover intensity measurement in Chinese financial markets

Online veröffentlicht: 16. Sept. 2023

Seitenbereich: -

Eingereicht: 30. Okt. 2022

Akzeptiert: 27. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00334

SchlüsselwörterMulti-objective optimization algorithm, Particle swarm algorithm, MOPSO algorithm, Risk spillover intensity, Chinese financial market

© 2023 Rui Mao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Multi-objective optimization algorithm, Particle swarm algorithm, MOPSO algorithm, Risk spillover intensity, Chinese financial market