Research on Interest Spread Risk of Commercial Pension Insurance in the Context of Negative Interest Rates
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v5i4.2566
Abstract
In the current low-interest-rate environment, the high-quality development of the life insurance industry faces many challenges. Among them, interest rate spread risk has a significant impact on the stable operation and long-term healthy development of the life insurance industry. Interest rate spread risk has characteristics such as long-term nature, hidden potential, and systematic impact. When severe, it can lead to the bankruptcy of insurance companies and bring about systemic crises in the industry. This article redefines negative interest rates and the risk of interest rate spread loss in commercial pension insurance, and reviews related literature.
Keywords
negative interest rates, interest spread losses, commercial pension insurance
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[1]Cheng Qiyun, Sun Caixin, Zhang Xiaoxing, et al. Short-Term load forecasting model and method for power system based on complementation of neural network and fuzzy logic. Transactions of China Electrotechnical Society, 2004, 19(10): 53-58.
[2]Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
[3]Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
[4]Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
[5]SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
[2]Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
[3]Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
[4]Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
[5]SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
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