Is a correlation-based investment strategy beneficial for long-term international portfolio investors?

Using negative to low-correlated assets to manage short-term portfolio risk is not uncommon among investors, although the long-term benefits of this strategy remain unclear. This study examines the long-term benefits of the correlation strategy for portfolios based on the stock market in Asia, Central and Eastern Europe, the Middle East and North Africa, and Latin America from 2000 to 2016. Our strategy is as follows. We develop five portfolios based on the average unconditional correlation between domestic and foreign assets from 2000 to 2016. This yields five regional portfolios based on low to high correlations. In the presence of selected economic and financial conditions, long-term diversification gains for each regional portfolio are evaluated using a panel cointegration-based testing method. Consistent across all portfolios and regions, our key cointegration results suggest that selecting a low-correlated portfolio to maximize diversification gains does not necessarily result in long-term diversification gains. Our empirical method, which also permits the estimation of cointegrating regressions, provides the opportunity to evaluate the impact of oil prices, U.S. stock market fluctuations, and investor sentiments on regional portfolios, as well as to hedge against these fluctuations. Finally, we extend our data to cover the years 2017-2022 and find that our main findings are robust. Supplementary Information: The online version contains supplementary material available at 10.1186/s40854-023-00471-9.

An input-output structural decomposition analysis of changes in China's renewable energy consumption.

Governments actively encourage renewable energy use to deal with climate change and achieve carbon emission reduction targets. It is crucial to find out the driving factors that affect the utilization of renewable energy. Therefore, based on China's 2010-2016 input-output table, this paper uses the input-output model and structural decomposition analysis (SDA) to analyze the driving factors of renewable energy changes in the production end, household end, and the aggregate economy. The results show that the changes in the consumption structure (F) is the most crucial factor for renewable energy use, followed by technology progress (T) and final demand per capita (V). Sector SEHW (supply of electric power, heat power, and water) and MCRP (manufacture of coke and refined petroleum products) are the two vital sectors to achieve China's energy transition of the production level. However, as for households, the proportion of renewable energy has been declining. Hence, the government should promote renewable energy use and achieve the green transition in production and household levels.

FDI, economic growth, and carbon emissions of the Chinese steel industry: new evidence from a 3SLS model.

Determine the main factors affecting carbon emissions of the Chinese steel industry is indispensable commitments to achieve the sustainable development of China. Hereby, based on the Stochastic Impacts by Regression on Population, Affluence and Technology (STIPRAT) model, this paper combines the economic growth function, carbon emission production function, and the FDI function of the Chinese steel industry, and uses the three-stage least square equation model (3SLS) to analyze the relationship between China's economic growth, carbon emissions in the steel industry, and FDI (foreign direct investment) inflows. The results document a complete two-way causal relationship of three variables in the whole country and the Western region, while the relationship in the Eastern region and the Central region is not complete. Moreover, there are no bidirectional causal relationship between carbon emissions and FDI in the Eastern region, while only bidirectional causality between carbon emissions and FDI in the Central region. These findings are of great significance for the Chinese steel industry to formulate effective emission reduction policies.

Proximal iteratively reweighted algorithm for low-rank matrix recovery.

This paper proposes a proximal iteratively reweighted algorithm to recover a low-rank matrix based on the weighted fixed point method. The weighted singular value thresholding problem gains a closed form solution because of the special properties of nonconvex surrogate functions. Besides, this study also has shown that the proximal iteratively reweighted algorithm lessens the objective function value monotonically, and any limit point is a stationary point theoretically.