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Abstract We simulate the asset pricing in the framework of information networks when the number of agents is constant and tends to infinity. When the number of agents is a constant, we find that a higher risk aversion coefficient, a lower information uncertainty, or a higher standard variance of payoff volatility induces a lower asset price; a higher number of agents induces a higher aggregate demand. When the number of agents tends to infinity, we study and simulate the closed form expressions for asset price with risk aversion coefficient. We find that a higher network connectedness or a lower risk aversion coefficient induces a higher information driven volatility component and a lower Sharpe ratio; a higher network connectedness or a lower risk aversion coefficient induces a higher market efficiency. Liquidity driven volatility component, trading profit, price volatility are non-monotonic functions of network connectedness, or risk aversion coefficient. Highlights • We simulate the asset price in the framework of information networks. • A higher risk aversion coefficient, a lower information uncertainty, or a higher standard variance of payoff volatility induces a lower asset price. • A higher number of agents induces a higher aggregate demand. • A higher network connectedness or a lower risk aversion coefficient induces a higher information driven volatility component as well as a higher market efficiency and a lower Sharpe ratio. • Liquidity driven volatility component, trading profit, price volatility are non-monotonic functions of network connectedness or risk aversion coefficient. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of individual rationality of buyers and sellers on continuous double auction market outcomes in terms of the proportion of boundedly-rational buyers and sellers. The individual rationality is discussed in a social network artificial stock market model by embedding network formation and information set. Traders automatically select the most profitable trading strategy based on individual and social learning of the profits and trading strategies of themselves and their neighbors, and submit orders to markets. The results show that (i) a higher proportion of boundedly-rational sellers induces a higher market price, higher sellers’ profits and a higher market efficiency; (ii) a higher proportion of boundedly-rational sellers induces a lower number of trades and lower buyers’ profits; (iii) a higher proportion of boundedly-rational buyers induces a lower market price, a lower number of trades, and lower sellers’ profits; (iv) a higher proportion of boundedly-rational buyers induces higher buyers’ profits and a higher market efficiency. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the systemic risk of China's stock market during crashes in 2008 and 2015 using the 5-minute intraday transaction data. The results show that liquidity contracted significantly after the downtrend. The systemic risk was magnified during the crash in 2008 while the system risk increased to an abnormal level before the crash in 2015. The volatility of systemic risk rose in 2015 compared to the one in 2008. Moreover, the Johansen co-integration test proves that there is a long-run equilibrium relationship between security margin trading and systemic risk volatility. Granger causality test indicates that margin financing is the Granger cause of the volatility of systemic risk in a bear market. This shows that the government response may impose negative effects on the systemic risk of China's stock market. It helps us better to understand features of systemic risk in China's stock market, and offer new ideas on how to reduce and stabilize the systemic risk. Highlights • The 5-minute intraday transaction data is used to study the systemic risk of China's stock market. • The system risk increased to an abnormal level before the crash in 2015. • There is a long-run equilibrium relationship between systemic risk volatility and margin trading. • The most risky industry changed from manufacturing to finance & insurance. • Government response imposes negative effects on the systemic risk of China's stock market. [ABSTRACT FROM AUTHOR]

Abstract The trading volume in stock markets is known as an important variable which reflects the liquidity of the financial markets and therefore is regarded to be greatly important for the measurement of market liquidity risk. In this work, a new concept called recurrence duration is introduced for study of daily trading volumes, which is inspired by idea of the volatility duration that was proposed and studied in our previous work. The recurrence duration is thought as the shortest passing time that the following days' trading volume takes to exceed or go below the current trading volume which is time-varying. Similar to the volatility duration distribution of the price returns, the power-law function could describe the empirical probability distribution of recurrence durations of trading volumes, and their tail distributions can be fitted by two stretched exponential functions. Further, the correlation relationships of trading volumes between Chinese stock indices as well as the correlations of recurrence durations are investigated. One approach employed is a recently proposed method, time-dependent intrinsic correlation (TDIC), which is based on the empirical mode decomposition (EMD) to decompose nonlinear and nonstationary signals into the intrinsic mode functions (IMFs), the instantaneous periods of which are used then in determination of the sizes of sliding windows to compute the running correlation coefficients for the multiscale signals. The empirical results reveal rich patterns of correlations for both trading volumes and recurrence durations at different scales for different modes. Another approach is the widely-used DCCA cross-correlation coefficient, by which the level of cross-correlation is measured for both original series and IMF modes of the stock indices. Highlights • A concept called recurrence duration is introduced for study of stock trading volumes. • Prob-distributions of recurrence durations for Chinese stock indices are analyzed. • Relationships between Chinese stock indices are investigated from trading volumes and recurrence durations aspects. • The TDIC method and DCCA cross-correlation coefficient are observed a well practical application. [ABSTRACT FROM AUTHOR]

The price dynamics of the emerging cryptocurrency market become an important factor affecting the current global investment market. To investigate its microscopic nonlinear dynamics mechanism, we develop a novel financial agent-based price model by combining the stochastic interacting system and two opposite directions of random jump processes, in which the attribute interactions between particles in the interacting system are utilized to imitate the influence of information transmission on investor's investment attitude in the cryptocurrency market. Return series of the model, generated by Monte Carlo simulations, can reproduce a number of "stylized facts" of real cryptocurrency data. Additionally, we apply the composite multiscale fuzzy entropy algorithm and the matching energy method to detecting the complexity of price volatility behaviours in both actual and simulated cryptocurrency markets. The similarity of empirical results indicates that the proposed model can replicate the properties of price volatility in actual cryptocurrency market to some extent. [ABSTRACT FROM AUTHOR]