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.

Cooperative management of an emission trading system:a private governance and learned auction for a blockchain approach

Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory,there is little research on using blockchain in carbon emission trading schemes(ETS).This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology.Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security.Considering the augmentation of information interaction during the auction process brought about by blockchain,the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction.In particular,implementing smart contracts can provide a swift and automatic settlement.The simulation results of the proposed system demonstrate the following:(1)fine-grained access is possible with a second delay;(2)the average annual compliance levels increase by 2%when bidders’learning behavior is considered;and(3)the blockchain network can process more than 350 reading operations or 7 writing operations in a second.Novel cooperative management of an ETS platform based on blockchain is proposed.The data access control policy based on CP-ABE is used to solve the contradiction between data privacy on the firm chain and government supervision.A learned auction strategy is proposed to suit the enhancement of information interaction caused by blockchain technology.This study provides a new method for climate change policymakers to consider the blockchain application of the carbon market.