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Using a dataset that recorded a large number of investment transactions in China from 1991 to 2018, we examine the statistical properties of the Chinese venture capital (VC) syndication network. Our main findings are as follows. First, the number of investment transactions sharply increased after 2014. Second, more than half of the VC firms are located in Beijing, Shanghai, and Shenzhen. Third, the degree distribution becomes roughly straight on a log–log plot. Fourth, the hypothesis that the degree distribution follows a power-law distribution is not rejected for 2015 and 2016. Fifth, better connected VC firms increase their connectivity faster, which suggests the existence of preferential attachment.

This paper argues that interactions of firms account for a sizable part of fluctuations in aggregate investments without exogenous aggregate shocks. We first establish empirically that the fraction of firms that engage in a lumpy investment follows a non-normal, two-sided exponential distribution across region-year with a panel data set of Italian firms. We then present a simple sectoral model that generates the two-sided exponential distribution that arises from the complementarity of the firms’ lumpy investments within a region. Calibrated by the firm-level estimate of complementarity, the model is capable of generating the two-sided exponential fluctuations observed at the aggregate level.

Loan syndication increases bank interconnectedness through co-lending relationships. We study the financial stability implications of such dependency on syndicate partners in the presence of shocks to banks' capital. Model simulations in a network setting show that such shocks can produce rare events in this market when banks have shared loan exposures while also relying on a common risk management tool such as value-at-risk (VaR). This is because a withdrawal of a bank from a syndicate can cause ripple effects through the market, as the loan arranger scrambles to commit more of its own funds by also pulling back from other syndicates or has to dissolve the syndicate it had arranged. However, simulations also show that the core-periphery structure observed in the empirical network may reduce the probability of such contagion. In addition, simulations with tighter VaR constraints show banks taking on less risk ex-ante.

We introduce a stochastic model for order book dynamics in online product markets, where product prices basically follow a random walk, but sometimes exhibit a sharp decline like cascade. In this model, each e-retailer stochastically places a limit sell order, stochastically reviews its quote price, and revise the quote price if necessary. On the other hand, customers stochastically pick a particular e-retailer and purchase its product. We conduct a numerical simulation to show that the model succesfully reproduces some important facts regarding price dynamics in online product markets, including a Hurst exponent around 0.5, non-autocorrelation of price fluctuations, non-trivial complex correlation of the sign of price changes, and the fat tails of price change distributions.

This paper explains the fat-tail distribution of asset transaction volumes and prices by a model of rational herd behavior of traders. Each trader decides whether to buy an asset by observing private information and other traders’ actions. A trader’s buying action reveals his positive private information and affects the other traders’ beliefs in favor of buying, leading to strategic complementarity. A power-law distribution emerges for the number of buying actions in a static Nash equilibrium. This model provides an economic reason as to why the stock market has to exhibit a criticality in the connectivity of the traders’ actions.