This paper explores the relationship between macro-factors and the realized volatility of commodity futures. Three main commodities—soybeans, gold and crude oil—are investigated using high-frequency data. For macro factors, we select six indicators including economic policy uncertainty (EPU), the economic surprise index (ESI), default spread (DEF), the investor sentiment index (SI), the volatility index (VIX), and the geopolitical risk index (GPR). These indicators represent three dimensions from macroeconomics and capital markets to a broader geopolitical dimension. Through establishing a dynamic connectedness network, we show how these macro factors contribute to the volatility fluctuations in commodity markets. The results demonstrate clearly distinctive features in the reaction to macro shocks across different commodities. Crude oil and gold, for example, are more reactive to market sentiment, whereas DEF contributes the most to the realized volatility of soybeans. Macroeconomic factors and geopolitical risks are more relevant to crude oil volatilities compare to the other two. Our empirical results also reveal the fact that the macro influence on the realized volatility of commodities is time varying.

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Complex network is not only a powerful tool for the analysis of complex system, but also a promising way to analyze time series. The algorithm of horizontal visibility graph (HVG) maps time series into graphs, whose degree distributions are numerically and analytically investigated for certain time series. We derive the degree distributions of HVGs through an iterative construction process of HVGs. The degree distributions of the HVG and the directed HVG for random series are derived to be exponential, which confirms the analytical results from other methods. We also obtained the analytical expressions of degree distributions of HVGs and in-degree and out-degree distributions of directed HVGs transformed from multifractal binomial measures, which agree excellently with numerical simulations.

What can traders learn and how does learning affect the market? When information is asymmetric, short-lived, and uninformed traders learn, we present an artificial limit order market model to examine the effect of learning, information value, and order aggressiveness on information dissemination efficiency, bid-ask spread, order submission, and order profit of traders. We find that learning helps the uninformed traders to acquire private information more effectively and hence improves market information dissemination. Also the informed traders in general consume liquidity while the uninformed traders mainly supply liquidity. More interestingly, due to the learning and short-lived information, the bid-ask spread and its volatility are positively related to the probability of informed trading. The results help us to understand the behavior of uninformed traders and provide substantial insight and intuition into the trading process.

This paper introduces a behavioral sentiment model to explore the stylized facts in limit order markets. Simulation results show that both the noise and sentiment trading can generate the absence of autocorrelation in returns, long memory in the absolute returns and bid-ask spread, and the hump shaped mean depth profile of the order book. However, sentiment trading plays a unique role in explaining the fat tails in the return distribution, long memory in the trading volume, an increasing and non-linear relationship between trade imbalance and mid-price returns, and also the diagonal effect or event clustering in order submission types, all of which cannot be explained by noise trading. Therefore, behavioral sentiment is an important driving force behind some of the well-documented stylized facts in limit order markets.