The Internet of Things (IoT) suffers from various security vulnerabilities. The use of blockchain technology can help resolve these vulnerabilities, but some practical problems in terms of scalability continue to hinder the adaption of blockchain for application in the IoT. The directed acyclic graph (DAG)-based Tangle model proposed by the IOTA Foundation aims to avoid transaction fees by employing a different protocol from that used in the blockchain. This model uses the Markov chain Monte Carlo (MCMC) algorithm to update a distributed ledger. However, concerns about centralization by the coordinator nodes remain. Additionally, the economic incentive to choose the algorithm is insufficient. The present study proposes a light and efficient distributed ledger update algorithm that regards only the subtangle of each step by considering the Bayesian inference. Experimental results have confirmed that the performance of the proposed methodology is similar to that of the existing methodology, and the proposed methodology enables a faster computation time. It also provides the same resistance to possible attacks, and for the same reasons, as does the MCMC algorithm.

No abstract

No abstract

The shocks on certain market spread to other markets due to the financial linkages of global economy, which is known as volatility spillover effect. In this study, we propose a volatility forecasting model for global market indices using the spatial‐temporal graph neural network (GNN). The volatility spillover between markets are reflected in the model by estimating the linkage between markets, which is the input of GNN, using the volatility spillover index. An empirical analysis is conducted on eight representative global market indices. From the out‐of‐sample results, we found the following features. First, the proposed spatial‐temporal GNN spillover model outperforms the benchmark models in short‐ and mid‐term forecasting. Second, the forecasting accuracy highly depends on the inclusion of the market index with a high volatility spillover effect. Including S&P500, which contains the highest net spillover index, effectively helps to forecast the volatilities of other markets. Third, the investor can gain economic gain by using predicted volatility from proposed model in the mean‐variance framework.

No abstract