In this paper, a stochastic quasi-Newton algorithm for nonconvex stochastic optimization is presented. It is derived from a classical modified BFGS formula. The update formula can be extended to the framework of limited memory scheme. Numerical experiments on some problems in machine learning are given. The results show that the proposed algorithm has great prospects.
With the recent climate changes, investors and policy-makers are paying close attention to the green bond market. This study intends to analyze the dynamic effects of shock transmission between climate policy uncertainty and the green bond market and to offer some new perspectives on analysis of green bond volatility over the previous years. To investigate time-varying effects of climate policy uncertainty on green bond market volatility, we applied a TVP-VAR model. And the impact of three important time points is tested, which are the Paris Association convening in December 2015, the 2017 annual Report on Policies and Actions of China on Climate Change in October 2017 and the “double carbon” policy in September 2020. The finding is that: (1) This impact of climate policy uncertainty on the volatility of the green bond market is time-varying, with short-term overreactions or underreactions as well as medium and long-term inversions. (2) This impact is also time-varying at different time points and has a certain degree of sustainability.
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