Using a network approach of variance decompositions, we measure the connectedness of 18 commodity futures and characterize both static and dynamic connectedness. Our results show that metal futures are net transmitters of shocks to other futures, and agricultural futures are vulnerable to shocks from the others. Furthermore, almost two‐thirds of the volatility uncertainty for commodity futures are due to the connectedness of shocks across the futures market. Dynamically, we find connectedness always increases in times of turmoil. An analysis of connectedness networks suggests that investors could be forewarned that the connectedness of various classes of futures could threaten their portfolios.

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We introduce an evolving network model of credit risk contagion in the credit risk transfer (CRT) market. The model considers the spillover effects of infected investors, behaviors of investors and regulators, emotional disturbance of investors, market noise, and CRT network structure on credit risk contagion. We use theoretical analysis and numerical simulation to describe the influence and active mechanism of the same spillover effects in the CRT market. We also assess the reciprocal effects of market noises, risk preference of investors, and supervisor strength of financial market regulators on credit risk contagion. This model contributes to the explicit investigation of the connection between the factors of market behavior and network structure. It also provides a theoretical framework for considering credit risk contagion in an evolving network context, which is greatly relevant for credit risk management.

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I n this study, we characterize the optimal compensation scheme for a firm that sells a single product with a limited stocking quantity through a sales agent. Our focus is on understanding how the supply-demand mismatch costs affect the firm's optimal compensation scheme. There are two main findings. First, under the deterministic demand response, the classical optimality result of the convex increasing compensation scheme breaks with the consideration of supply-demand mismatch costs. Instead, the optimal compensation is S-shaped under certain conditions. Second, under the stochastic demand response, the classical optimality result of the menu of linear compensation schemes fails to hold with the consideration of supply-demand mismatch costs. Instead, the optimal compensation schemes consist of a menu of linear compensation coupled with a penalty of the agent's forecast error.

The rapid growth in the adoption of mobile payments has already begun to reshape bank payment practices. Utilizing a unique data set from a leading bank in Asia that records credit card transactions of its customers before and after the launch of Alipay mobile payment, the largest mobile payment platform in the world, this study aims to understand the impact of mobile payment adoption on bank customer credit card activities and the change of this impact after the mobile payment expansion. To do so, we employ the difference-in-differences method coupled with matching to estimate the effects. We find that mobile payment adoption not only increases customer credit card activities at the focal bank through both off-line and online channels, but also enhances customer loyalty to the bank by reducing churn. Specifically, the total credit card transaction amount and frequency of our focal bank increased by 9.4% and 10.7%, respectively. Moreover, we examine the change in the treatment effect after the mobile payment expansion and find an increase in adopters’ credit card activities and a reduction in their churn after the expansion. Next, we discuss the underlying mechanisms and show that mobile payment acts as a substitute for physical card payment in the off-line channel, and this supports the key underlying mechanism of the reduced transaction cost. However, a certain complementary effect exists between personal computer and mobile payments, likely driven by the coadoption of the two. Finally, we provide empirical evidence on conditions that facilitate the use of mobile payments, following the unified theory of acceptance and use of technology. History: Yong Tan, Senior Editor; Tianshu Sun, Associate Editor. Funding: This work was supported by National Natural Science Foundation of China [Grants U1811462, 72071102]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0156 .

As a star of emerging industries in China, internet-based finance has been developing rapidly. This paper, considers selecting a more suitable valuation model to measure the intrinsic value and price bubble of Internet-based Finance stocks. By comparing the relative valuation accuracy of the Kim et al. model with the Frankel-Lee model and the F-O model applied in the prior studies, this study finds that the Kim et al. model highlights the industry-specific features and outperforms other models in interpreting stocks price variation. Especially, under the circumstance of soaring and slumping stocks price variation (e.g. 2015), it is essential to study the price bubbles of internetbased finance stocks at different points of Shanghai Stock Exchange Composite Index. Surprisingly, our empirical results suggest that the internet-based finance stocks have negative bubbles at the whole average level, and about half of them are undervalued. Moreover, there are positive correlations between the bubble level and three key factors including the trading volume, the price to book ratio and whether to do cross-industry business on internet-based finance. These findings imply that the Kim et al. model contributes to improving valuation accuracy of internet-based finance stocks and explainability of the price bubbles in A-share market.