Based on 5-minute high-frequency trading data, we examine the time-varying causal relationship between herding behavior and multiscale spillovers (return, volatility, skewness, and kurtosis) in the Chinese stock market. We employ the novel time-varying Granger causality test proposed by Shi et al. (2018), which is based on the recursive evolving algorithm developed by Phillips et al. (2015a, 2015b), to identify real-time causal relationships and capture possible changes in the causal direction. Our findings reveal a strong relationship between herding and spillover effects, particularly with odd-moment (return and skewness) spillovers. For most of the study period, a bidirectional causal relationship was found between herding and odd-moment spillovers. These results imply that herding behavior is a key driver of spillover effects, especially return and skewness spillovers, which are primarily transmitted through the information channel. By contrast, volatility and kurtosis spillovers are more strongly driven by real and financial linkages. Furthermore, spillover effects also affect herding behavior, highlighting the intricate feedback loop between investor behavior and risk transmission.

We introduce a deep visualization (DV) framework that turns conventional commodity data into images and extracts predictive signals via convolutional feature learning. Specifically, we encode futures price trajectories and the futures surface as images, then derive four deep‑visualization (DV) predictors, carry ($bs_{DV}$), basis momentum ($bm_{DV}$), momentum ($mom_{DV}$), and skewness ($sk_{DV}$), each of which consistently outperforms its traditional formula‑based counterpart in return predictability. By forming long–short portfolios in the top (bottom) quartile of each DV predictor, we build an image‑based four‑factor model that delivers significant alpha and better explains the cross‑section of commodity returns than existing benchmarks. Further evidence shows that the explanatory power of these image‑based factors is strongly linked to macroeconomic uncertainty and geopolitical risk. Our findings reveal that transforming conventional financial data into images and relying solely on image-derived features suffices to construct a sophisticated asset pricing model at least in commodity markets, pioneering the paradigm of image‑based asset pricing.

Individual institutions marginal contributions to the systemic risk contain predictive power for its potential future exposure and provide early warning signals to regulators and the public. We use higher-order co-skewness and co-kurtosis to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

In this paper, we present an entropy-based approach to measure the asymmetry of stock returns. By applying this approach, we use the Bootstrap method that our asymmetry measure exhibits a significantly enhanced ability to detect asymmetry compared to skewness. Moreover, our empirical findings reveal that stocks characterized by higher upside asymmetries, as determined by our innovative entropy measure, exhibit lower average returns across a crosssection of stocks. This supports the conclusions drawn by Han et al. (2018). In contrast, when employing the three-moment skewness measure, the relationship between asymmetry and stock returns remains inconclusive within the Chinese market.

We propose a Capital Asset Pricing Model where investors exhibit prospect preferences. In equilibrium, we find that agents seek an optimal trade-off between expected returns, variance, and skewness. All assets in the economy are then priced by a three-factor model, which augments the security market line with two factors that respectively capture positive and negative coskewness with the market portfolio. Using U.S. stock market data, we find evidence consistent with these predictions. In additional tests, we find that the results are stronger among stocks traded by less sophisticated investors. Overall, prospect preferences have a substantial effect on stock prices.

We propose a new investor sentiment index by estimating the differences in variance,skewness, and kurtosis from realized stock returns and option implied moments. We show that our index cannot be explained by risk factors such as market risk, firm size, value, or profitability. Furthermore, we present evidence that this correlation can be exploited for momentum strategies, which perform significantly better during high-stimulation periods. In fact, our methodology can be extended to a daily sentiment measure and stock-specific sentiment indices.

Skewness preference, the tendency to overweight the probability of extreme tail events, can affect managerial decision making. We find that Chinese listed firms managed by CEOs who experienced a largely unpredictable rare event, namely the outbreak of Severe Acute Respiratory Syndrome (SARS) in 2003, during their earlier executive careers have lower stock price crash risk measured by negative skewness. This effect especially matters for CEOs whose experienced events are more salient. Furthermore, professional epidemic experience induces CEOs to deter stock price crashes through altering financial reporting strategies. Overall, entrepreneurs’ skewness preference can reduce the negative skewness of stock returns.

We provide empirical evidence and a theoretical explanation for the asymmetries of capital growth rate at the firm level and in the aggregate. Capital growth rate at the firm level is positively skewed, while the average capital growth rate across firms, as well as its slope, is negatively skewed. We develop a model of irreversible corporate investment that can reconcile these opposite patterns. The key to our model is that firms do not observe the true state of economy and have to infer it from noisy signals. The time-varying uncertainty in the learning process leads to variations in the option value of waiting, which causes many firms to react to bad signals arriving in good times, and few firms to react to good signals arriving in bad times. As a result, the capital growth rate at the aggregate level exhibits a negative skewness both in levels and in the slope, even though irreversibility causes positive skewness at the individual firm level.

In this paper, based on Acharya and Pedersen’s [Journal of Financial Eco- nomics (2006)] overlapping generation model, we show that liquidity risk could influence the market risk forecasting through at least two ways. Then we argue that traditional liquidity adjusted VaR measure, the simply adding of the two risk measure, would underestimate the risk. Hence another approach, by modeling the liquidity adjusted returns (LAr) directly, was employed to incorporate liquidity risk in VaR measure in this study. Under such an approach, China’s stock market is specifically studied. We estimate the one-day-ahead “standard” VaR and liquidity adjusted VaR by forming a skewed Student’s t AR-GJR model to capture the asymmetric effect, non-normality and excess skewness of return, illiquidity and LAr. The empirical results support our theoretical arguments very well. We find that for the most illiquidity portfolio, liquidity risk represents more than 22% of total risk. We also find that simply adding of the two risk measure would underestimate the risk. The accuracy testing show that our approach is more accurate than the method of simply adding.

Numerous studies have shown that the simple random walk model outperforms all structural and time series models in forecasting the conditional mean of exchange rate changes. However, in many important applications, such as risk management, forecasts of the probability distribution of exchange rate changes are often needed. In this paper, we develop a nonparametric portmanteau evaluation procedure for out-of-sample density forecast and provide a comprehensive empirical study on the out-of-sample performance of a wide variety of time series models in forecasting the intraday probability density of two major exchange rates－Euro/Dollar and Yen/Dollar. We find that some nonlinear time series models provide better density forecast than the simple random walk model, although they underperform in forecasting the conditional mean. For Euro/Dollar, it is important to model heavy tails through a Student-t innovation and asymmetric time-varying conditional volatility through a regime-switching GARCH model for both in-sample and out-of-sample performance; modeling conditional mean and serial dependence in higher order moments (e.g.,conditional skewness), although important for in-sample performance, does not help out-of-sample density forecast. For Yen/Dollar, it is also important to model heavy tails and volatility clustering, and the best density forecast model is a RiskMetrics model with a Student-t innovation. As a simple application, we Þnd that the models that provide good density forecast generally provide good forecast of Value-at-Risk.