<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we first sort out the formula of the premium principle and the algorithm of the diffusion model and then study the strategy problem about optimal investment consumption and insurance purchase when investors invest in new developing industries under the risk diffusion model. In real financial markets, there are two types of uncertainty regarding asset prices: normal fluctuations and abnormal shocks. The risk diffusion model is used to plan the optimal investment strategy based on this basis. In the end, three tests are executed, including two numerical simulations and one investment analysis that determines the investor’s age. The computational results show that the optimal strategy in the first set of simulations is the 56% increase in investment volume <italic>A</italic>(<italic>x</italic>) at the parameter σ = 0.1. The standard deviation of the investor’s objective in the second set of simulations is 9.287%, and the investor’s assets invested in risky securities should be 1.071. In the third set of tests, as the investor’s age increases, the value of the investor’s investment in risky assets continues to decline from 2.0 after 30 years, and by the time it reaches 40 years, it is already close to 0.25, and there is a continued decline, converging to 0. Investors can invest in providing effective reference data by investing in the portfolio optimization strategy in this paper, which predicts stock market volatility and vibration.</p>
</abstract>

In this paper, we first sort out the formula of the premium principle and the algorithm of the diffusion model and then study the strategy problem about optimal investment consumption and insurance purchase when investors invest in new developing industries under the risk diffusion model. In real financial markets, there are two types of uncertainty regarding asset prices: normal fluctuations and abnormal shocks. The risk diffusion model is used to plan the optimal investment strategy based on this basis. In the end, three tests are executed, including two numerical simulations and one investment analysis that determines the investor’s age. The computational results show that the optimal strategy in the first set of simulations is the 56% increase in investment volume A(x) at the parameter σ = 0.1. The standard deviation of the investor’s objective in the second set of simulations is 9.287%, and the investor’s assets invested in risky securities should be 1.071. In the third set of tests, as the investor’s age increases, the value of the investor’s investment in risky assets continues to decline from 2.0 after 30 years, and by the time it reaches 40 years, it is already close to 0.25, and there is a continued decline, converging to 0. Investors can invest in providing effective reference data by investing in the portfolio optimization strategy in this paper, which predicts stock market volatility and vibration.

Portfolio Optimization Strategy Based on Risk Diffusion Model in Emerging Industry Development

Applied Mathematics and Nonlinear Sciences

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

In this paper, we first sort out the formula of the premium principle and the algorithm of the diffusion model and then study the strategy problem about...

Portfolio Optimization Strategy Based on Risk Diffusion Model in Emerging Industry Development

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 15 gru 2023

Przyjęty: 21 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0110

Słowa kluczowe
Emerging Industries, Risk Diffusion Model, Optimal Portfolio, Optimization Strategy

© 2024 Shuangqin Ni et al., published by Sciendo

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

Portfolio Optimization Strategy Based on Risk Diffusion Model in Emerging Industry Development

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 15 gru 2023

Przyjęty: 21 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0110

Słowa kluczoweEmerging Industries, Risk Diffusion Model, Optimal Portfolio, Optimization Strategy

© 2024 Shuangqin Ni et al., published by Sciendo

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

Słowa kluczowe
Emerging Industries, Risk Diffusion Model, Optimal Portfolio, Optimization Strategy