Social pensions and risky financial asset holding in China

2021 ◽  
pp. 1-14
Author(s):  
Qing Xu ◽  
Wanglin Ma ◽  
Fang Wang ◽  
Qing Yang ◽  
Jin Liu
1997 ◽  
Vol 57 (4) ◽  
pp. 907-934 ◽  
Author(s):  
Livio Di Matteo

Wealth and asset holding in late-nineteenth-century Ontario are examined using a new data set of census-linked probated decedents. Hump-shaped wealth-age profiles are found, supporting the importance of demographic and life cycle forces in late-nineteenth-century financial asset accumulation. With financial asset holding more pronounced in Ontario than Quebec, the implication for Canadian economic development is that the differences in capital formation and industrialization across Ontario and Quebec are partly rooted in saving behavior. The results show that urbanization, occupational status, literacy, the number of children, and region of residence are important determinants of wealth and asset holding.


2021 ◽  
pp. 102072
Author(s):  
Youssef El-Khatib ◽  
Stephane Goutte ◽  
Zororo S. Makumbe ◽  
Josep Vives

2021 ◽  
Vol 14 (7) ◽  
pp. 308
Author(s):  
Usha Rekha Chinthapalli

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.


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