Asymmetric Shocks, Long-term Bonds and Sovereign Default

We present a sovereign default model with asymmetric shocks and long-term bonds, and solve the model using discrete state dynamic programming. As result, our model matches the Argentinean economy over period 1993Q1-2001Q4 quite well. We show that our model can match high default frequency, high debt/output ratio and other cyclical features, such as countercyclical interest rate and trade balance in emerging countries. Moreover, with asymmetric shocks we are able to match high sovereign spread level and low spread volatility simultaneously in one model, which is till now not well solved. As another contribution of our paper, we propose a simulation-based approach to approximate transition function of output shocks between finite states, which is an indispensable step in discrete state dynamic programming. Comparing to Tauchen’s method, our approach is very flexible in transforming various econometric models to finite state transition function, so that our approach can be widely used in simulating different kinds of discrete state shocks.Sovereign Default; Asymmetric Shocks; Transition Function; Long-term Bonds

Synthesis of Core-Shell @@ Microspheres and Their Application as Recyclable Photocatalysts

We report the fabrication of core-shell Fe3O4@SiO2@TiO2 microspheres through a wet-chemical approach. The Fe3O4@SiO2@TiO2 microspheres possess both ferromagnetic and photocatalytic properties. The TiO2 nanoparticles on the surfaces of microspheres can degrade organic dyes under the illumination of UV light. Furthermore, the microspheres are easily separated from the solution after the photocatalytic process due to the ferromagnetic Fe3O4 core. The photocatalysts can be recycled for further use with slightly lower photocatalytic efficiency

An effective heuristic for project scheduling with resource availability cost

[EN] The resource constrained project scheduling problem (RCPSP) is widely studied in the literature and has a host of applications in practice. As a variant of the RCPSP, the resource availability cost problem (RACP), which has the aim of minimizing the availability costs of renewable resources in order to complete a project subject to a given deadline, is considered in this paper. We divide the RACP into two sub-problems: the sequencing problem and the resource decision problem, and propose a multi-start iterative search heuristic (MSIS) to solve it. For the sequencing problem, an iterative search framework is constructed to effectively search the activity sequences. A two stage resource adjustment procedure and a backward peak elimination procedure is developed for solving the resource decision problem. MSIS is compared with three existing algorithms on both PSPLib and RanGen data sets involving 1380 instances. A complete calibration of the different parameters and operators of MSIS by means of a design of experiments approach is given. Experimental and statistical results show that MSIS outperforms the other three algorithms in both effectiveness and efficiency by a significant margin. (C) 2016 Published by Elsevier B.V.This work is supported by the National Natural Science Foundation of China (Nos. 61572127, 61272377), the Key Research & Development program in Jiangsu Province (No. BE2015728) and the Collaborative Innovation Center of Wireless Communications Technology. Rubén Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project SCHEYARD - Optimization of Scheduling Problems in Container Yards with reference DPI2015-65895-R co-financed with FEDER funds.Zhu, X.; Ruiz García, R.; Li, S.; Li, X. (2017). An effective heuristic for project scheduling with resource availability cost. European Journal of Operational Research. 257(3):746-762. https://doi.org/10.1016/j.ejor.2016.08.049S746762257

Asymmetric Shocks, Long-term Bonds and Sovereign Default

We present a sovereign default model with asymmetric shocks and long-term bonds, and solve the model using discrete state dynamic programming. As result, our model matches the Argentinean economy over period 1993Q1-2001Q4 quite well. We show that our model can match high default frequency, high debt/output ratio and other cyclical features, such as countercyclical interest rate and trade balance in emerging countries. Moreover, with asymmetric shocks we are able to match high sovereign spread level and low spread volatility simultaneously in one model, which is till now not well solved. As another contribution of our paper, we propose a simulation-based approach to approximate transition function of output shocks between finite states, which is an indispensable step in discrete state dynamic programming. Comparing to Tauchen’s method, our approach is very flexible in transforming various econometric models to finite state transition function, so that our approach can be widely used in simulating different kinds of discrete state shocks

Bayesian Analysis of a Triple-Threshold GARCH Model with Application in Chinese Stock Market

We construct one triple-threshold GARCH model to analyze the asymmetric response of mean and conditional volatility. In parameter estimation, we apply Griddy-Gibbs sampling method, which require less work in selection of starting values and pre-run. As we apply this model in Chinese stock market, we find that 12-days-average return plays an important role in defining different regimes. While the down regime is characterized by negative 12-days-average return, the up regime has positive 12-days-average return. The conditional mean responds differently between down and up regime. In down regime, the return at date t is affected negatively by lag 2 negative return, while in up regime the return responds significantly to both positive and negative lag 1 past return. Moreover, our model shows that volatility reacts asymmetrically to positive and negative innovations, and this asymmetric reaction varies between down and up regimes. In down regime, volatility becomes more volatile when negative innovation impacts the market than when positive one does, while in up regime positive innovation leads to more volatile market than negative one

Asymmetric Shocks, Long-term Bonds and Sovereign Default

We present a sovereign default model with asymmetric shocks and long-term bonds, and solve the model using discrete state dynamic programming. As result, our model matches the Argentinean economy over period 1993Q1-2001Q4 quite well. We show that our model can match high default frequency, high debt/output ratio and other cyclical features, such as countercyclical interest rate and trade balance in emerging countries. Moreover, with asymmetric shocks we are able to match high sovereign spread level and low spread volatility simultaneously in one model, which is till now not well solved. As another contribution of our paper, we propose a simulation-based approach to approximate transition function of output shocks between finite states, which is an indispensable step in discrete state dynamic programming. Comparing to Tauchen’s method, our approach is very flexible in transforming various econometric models to finite state transition function, so that our approach can be widely used in simulating different kinds of discrete state shocks

GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning

In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning. This task is particularly challenging due to the complex dynamics of fluids and the need to achieve multi-modal goals. The policy is required to successfully reach both position goals and water amount goals, which leads to a large convoluted goal state space. To overcome these challenges, we introduce Goal Sampling Adaptation for Scooping (GOATS), a curriculum reinforcement learning method that can learn an effective and generalizable policy for robot scooping tasks. Specifically, we use a goal-factorized reward formulation and interpolate position goal distributions and amount goal distributions to create curriculum throughout the learning process. As a result, our proposed method can outperform the baselines in simulation and achieves 5.46% and 8.71% amount errors on bowl scooping and bucket scooping tasks, respectively, under 1000 variations of initial water states in the tank and a large goal state space. Besides being effective in simulation environments, our method can efficiently adapt to noisy real-robot water-scooping scenarios with diverse physical configurations and unseen settings, demonstrating superior efficacy and generalizability. The videos of this work are available on our project page: https://sites.google.com/view/goatscooping

The Future of ChatGPT-enabled Labor Market: A Preliminary Study

As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raised about the potential ethical issues, especially about whether ChatGPT-like artificial general intelligence (AGI) will replace human jobs. To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation. To be specific, we first conduct an in-depth analysis of large-scale job posting data in BOSS Zhipin, the largest online recruitment platform in China. The results indicate that about 28% of occupations in the current labor market require ChatGPT-related skills. Furthermore, based on a large-scale occupation-centered knowledge graph, we develop a semantic information enhanced collaborative filtering algorithm to predict the future occupation-skill relations in the labor market. As a result, we find that additional 45% occupations in the future will require ChatGPT-related skills. In particular, industries related to technology, products, and operations are expected to have higher proficiency requirements for ChatGPT-related skills, while the manufacturing, services, education, and health science related industries will have lower requirements for ChatGPT-related skills