Household Lifetime Strategies under a Self-Contagious Market

Guo, Liu; Jin, Zhuo; Li, Shuanming

doi:10.1016/j.ejor.2020.05.060

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…Different authors have investigated the consumption patterns that promote sharing and the factors that determine the orientation toward the sharing platforms [35]. Liu, Zhuo, and Shuanming analysed the income process and stated that the earned wage directly affects the level of lifetime consumption [36]. The sharing activity is growing following the level of wages and the economic situation in the country.…”

Section: The Core Elements For the Index Of Visits To Sharing Platformsmentioning

confidence: 99%

“…Using online platforms [2,5,15,31,40,41] Social network site (SNS) [2,8,11,15,32] Personal consumption [3,15,20,22,36] Sustainable consumption [19,28,35] Share time and resources [13,15,21,25,34,39,42] The popularity of sharing platform [1,2,12,13,28,29,34,38] Consumer behaviour [1][2][3]9,15,17,19,22,26,28,31,33,35,37] Collaborative consumption [4,13,16,…”

Section: Societymentioning

confidence: 99%

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Core Elements Affecting Sharing: Evidence from the United States

et al. 2021

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The new phenomenon called sharing or collaborative consumption emerged a decade ago and is continuously growing. It creates new possibilities for society, and especially for business, is beneficial for the environment, makes more efficient use of resources, and presents a new competitive business model. The scientific literature lacks a more in-depth analysis of the factors influencing sharing activity growth; therefore, the paper’s authors attempt to fill this gap. The authors aim to identify the factors affecting the use of sharing platforms. To reach the goal, the authors developed a regression model and constructed a list of 71 variables. The study used monthly United States data from January 2017 to June 2020 from the publicly available Federal Reserve Economic Data (FRED)and Google trends databases. The comparison to other indexes proves that the proposed index, representing the number of visits to sharing platforms (SEP), is a unique one. The first index allowed us to revise the sharing activity monthly. The authors identified that variables such as wage level, social network users, import level, and personal consumption are critical in affecting the number of visits to sharing platforms. The presented framework could be helpful for practitioners and policymakers analysing the stimulation of sharing or collaborative consumption. It includes indicators representing different areas, such as society, technology, and country, and allows for monthly investigations. Such activity was evident for a long time when online platforms contributed to its wider accessibility. The results help to forecast the number of visits monthly. Sharing is still an emerging area for research; thus, the authors tried to explore the phenomenon of sharing to expand the conceptual level of knowledge.

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Section: The Core Elements For the Index Of Visits To Sharing Platformsmentioning

confidence: 99%

Section: Societymentioning

confidence: 99%

Core Elements Affecting Sharing: Evidence from the United States

et al. 2021

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“…Björk et al [9][10][11] derived the optimal investment and reinsurance strategy under mean-variance criterion. Liu et al 12,13 considered the interdependence between the insurance market and financial market by using the Hawkes process to capture the contagion effect between markets.…”

Section: Introductionmentioning

confidence: 99%

A two‐layer stochastic differential investment and reinsurance game with default risk under the bi‐fractional Brownian motion environment

Hao

Qiu

2022

Math Methods in App Sciences

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This paper is concerned with the investment and reinsurance problem between two insurance companies and a reinsurance company by constructing a two‐layer stochastic differential game. Insurance companies invest in a risk‐free asset, a defaultable bond, and a risky asset under the bi‐fractional Brownian motion environment; reinsurance companies invest in a risk‐free asset and a risky asset under the bi‐fractional Brownian motion environment. In order to maximize the expected utility of the insurance companies' relative wealth and the expected utility of the reinsurance company's wealth at the terminal time, we solve the Hamilton–Jacobi–Bellman (HJB) equations by using the differential game theory and stochastic optimal control theory and obtain the equilibrium investment–reinsurance and reinsurance premium strategies. Finally, we investigate the influence of the parameters on the equilibrium strategy through numerical examples and analyze its economic implications.

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