Traditional sparse factor models (e.g., Fama–French) struggle to explain cross-sectional returns in high-dimensional settings due to the ‘factor zoo’—a proliferation of anomalies with overlapping or noisy signals. We show that a principal component (PC)-based stochastic discount factor (SDF) using regularization techniques can aggregate characteristics into dominant risk sources, balancing parsimony and robustness. First, the SDF is estimated using a small sample of 25 portfolios double-sorted by size/book-to-market ratio, and it is found that only 2 principal component factors are needed to predict the cross-sectional returns well, which is consistent with the classical size premium and value premium. Then, the sample is further extended to 72 anomalous characteristics. The results show that the sparse PC-based SDF predicts the cross-sectional returns better than the sparse original characteristic-based SDF. We verify that sparse PC-based models outperform traditional sparse factor models even in emerging markets like China, where retail-driven trading and regulatory shifts amplify idiosyncratic risks.

We investigate the extreme return connectedness between the food, fossil energy, and clean energy markets using the quantile connectedness approach, which combines the traditional spillover index with quantile regression. Our results show that return connectedness at the tails (57.91 and 61.47 (23.02 over time, with notable increases during extreme events. Among these markets, fossil energy market consistently acts as the net receiver, while clean energy market primarily serves as the net transmitter. Additionally, we use linear and nonlinear ARDL models to examine the role of external uncertainties on return connectedness. We find that climate policy uncertainty (CPU), geopolitical risk (GPR), and the COVID-19pandemic significantly impact median connectedness, while economic policy uncertainty (EPU),GPR, and trade policy uncertainty (TPU) are crucial drivers of extreme connectedness. Our findings provide valuable insights for investors and policymakers on risk spillover effects between food and energy markets under both normal and extreme market conditions.

This study leverages the Log-Periodic Power Law Singularity (LPPLS) confidence indicator to effectively identify bubbles in agricultural commodity markets. We analyze five major grain price indices reported by the International Grains Council (IGC) from January 2000 to April 2023, successfully identifying several notable bubble periods. These include a positive bubble in 2004 driven by decreased food production, a substantial positive bubble during the 2008 global financial crisis, a negative bubble in 2016, and positive bubbles triggered by the COVID-19 pandemic and the Russo-Ukrainian conflict since 2020. As the critical point is approached, the LPPLS confidence indicator exhibits strong early warning capabilities. To verify the model's robustness, we employ the Bai-Perron test for multiple structural breaks, detecting five such breaks in each agricultural commodity price series. LPPLS indicators provide strong early-warning signals prior to these break dates. Finally, we investigate the predictability of price bubbles in the agricultural commodity market. Using a Markov regime-switching model, our findings confirm that the geopolitical risk index possesses significant predictive power for bubble formation.

Assessing the impact of climate risks on the financial system is one of the most urgent issues currently. We build a network-based climate risk model to explain how a shock from climate policies translates into shocks in the banking system. Then, we conduct macroprudential stress tests on the Chinese banking system under various climate policy scenarios. We show that under the policy target of peaking the carbon in 2030 and CO2 concentration no more than 500 ppm in 2100, individual banks in China will face equity losses ranging from 1.93% to 14.03%, equivalent to an overall loss of 6.94% in 2025. When considering the electric power sector's adoption of green energy technologies, the adverse effects will be slightly mitigated. Our stress tests suggest that the implementation of climate policies should be gradual and consider potential economic impacts so that climate goals can be achieved without undue shocks to the economy.

The energy commodity plays a significant role in economic development and national security. It is important for various countries to capture the linkages of the international energy trade network and avoid the occurrence of systemic risk. However, the granular data on trade network is often lacking, and instead, the network has to be reconstructed from partial data. In this paper, we compare seven network reconstruction methods by applying them to 16 types of energy trade data extracted from UN-Comtrade. To this end, first we present the topological structures and compute the dependency characteristics for these energy trade networks. Then, we conduct a horse race among different network reconstruction methods. The methods are ranked by comparing their abilities to reproduce the network structures and bilateral weights. Our findings show that Cimi is the winner across all 16 energy trade networks in terms of link-based similarity measures. In addition, the fitness models (Cimi and Musm) tend to perform best at reconstructing the weight matrix. These findings will aid in the accurate disclosure of latent international energy trade relations when only aggregated export and import figures are available, allowing systemic risks to be appropriately assessed.

This paper studies the time-varying tail dependence among the European stock markets and assesses the influence of individual financial markets. By utilizing two generalized autoregressive score (GAS) copulas, we compute the tail dependence for 11 European stock indexes and 1 American stock index over the last 16 years. Notably, it is found that the dependencies among European stock indexes are generally stable, even during the crisis periods. Then an influence measure is proposed for each individual market based on the tail dependence. Interestingly, the influence ranking is also stable for both the whole period and two big drawdown days in crisis periods. To be specific, AEX (Netherlands), FCHI (France), and GDAXI (Germany) always show the most significant influences, while SPX (USA) is always the least influential one. Finally, an equal-weighted portfolio is built to measure the systemic risk in the European stock markets. It is found that both patterns of risk (VaR) and expected shortfall (ES) are different from the time-varying tail dependence. There exist obvious inverted spikes on 16 October 2008 and 16 March 2020. The results indicate that the extreme portfolio risk does not come from the increase of dependence among European stock markets, but from the individual jumps.

We consider a dynamic portfolio optimization problem when expected returns follow a linear factor model and transaction costs are quadratic. A closed-form solution is derived for the optimal portfolio policy, which is a gradual trading towards a moving aim portfolio. To pursue the short-horizon investment opportunities, the optimal portfolio policy requires frequent rebalancing with trade-off between expected returns, transaction costs and estimation errors. We introduce two powerful short-horizon factors to predict the subsequent daily returns. We implement the optimal trading strategy for 18 international assets. The dynamic portfolio strategy is tested both in-sample and out-of-sample. For both tests, the optimal dynamic strategy achieves superior net Sharp ratio and substantial cumulative profits relative to more naive benchmarks.

In the past year, we have witnessed that the U.S. inflation rate hit the highest peak in over 40 years and is still close to its multi-decade high now. The overall change in consumer prices has a significant impact on the nation's economic activity, product manufacturing, consumer behavior, and stock market. In this work, I develop a semiparametric forecasting approach using factor models with a large number of macroeconomic or financial time series predictors. The proposed method deals with the complex temporal and cross-sectional dependence of macroeconomic or financial time series predictors. Also, the proposed method does not need the prior knowledge of forecasting directions and forecasting function. I will examine the performance of the proposed method in simulation studies and a real-world application for predicting the consumer price index.

Financial risk is spread and amplified through the interconnectedness among financial institutions. We apply a time-varying parameter vector autoregression model to analyze the dynamic spillover effects in the Chinese financial system. We find that the 2017 house price control policies have significantly increased the risk of China’s financial system. Before 2017, with the prosperity of the real estate market, the interconnectedness of the Chinese financial system continued to decline, while after 2017, with the slowdown of house price growth and the downturn of the real estate market, the interconnectedness turned to increase. For different sectors, the trends and the magnitudes of the spillover effects are diverse, and any sector can contribute to systemic risk in a dynamic way. Finally, we rank 20 systemically important financial institutions according to two centrality measures. The stable institution ranking provides less noisy information for regulators to formulate a policy and intervene in the market effectively.

We derive the default cascade model and the fire-sale spillover model in a unified interdependent framework. The interactions among banks include not only direct cross-holding, but also indirect dependency by holding mutual assets outside the banking system. Using data extracted from the European Banking Authority, we present the interdependency network composed of 48 banks and 21 asset classes. For the robustness, we employ three methods, called Anan, Hała and Maxe, to reconstruct the asset/liability cross-holding network. Then we combine the external portfolio holdings of each bank to compute the interdependency matrix. The interdependency network is much denser than the direct cross-holding network, showing the complex latent interaction among banks. Finally, we perform macroprudential stress tests for the European banking system, using the adverse scenario in EBA stress test as the initial shock. For different reconstructed networks, we illustrate the hierarchical cascades and show that the failure hierarchies are roughly the same except for a few banks, reflecting the overlapping portfolio holding accounts for the majority of defaults. We also calculate systemic vulnerability and individual vulnerability, which provide important information for supervision and relevant management actions.

We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order directions, the long memory in relative prices, the asymmetric distribution of relative prices, and the nonlinear dependence of the average order size and its standard deviation on the relative price. Order cancellation follows a Poisson process with the arrival rate determined from real data and the cancelled order is determined according to the empirical distributions of relative price level and relative position at the same price level. All these ingredients of the model are derived based on the empirical microscopic regularities in the order flows of stocks on the Shenzhen Stock Exchange. The model is able to produce the main stylized facts in real markets. Computational experiments uncover that asymmetric setting of price limits will cause the stock price diverging exponentially when the up price limit is higher than the down price limit and vanishing vice versus. We also find that asymmetric price limits have influences on stylized facts. Our EBOD model provides a suitable computational experiment platform for academics, market participants and policy makers.

Based on high-frequency data, we study the difference in cryptocurrency market before and during the COVID-19. We analyze the multifractality of three major cryptocurrencies via the multifractal detrended fluctuation analysis (MFDFA). To investigate the source of multifractality, we construct shuffled, surrogated and truncate data. The results show that market efficiency of cryptocurrency has decreased during COVID-19. The cryptocurrency multifractal characteristics mainly come from non-Gaussian distribution. Additionally, the components of multifractal nature have changed during the pandemic. The results provide evidence for the impact of COVID-19 on cryptocurrency market.

We investigate the news coverage effect in explaining and predicting the portfolio returns. We find that stocks with more news coverage yield higher abnormal returns. The news coverage effect is still robust even after controlling for firm characteristics and industry sectors. Furthermore, the return premium on news coverage is particularly large in small-cap stocks due to the information dissemination role of news coverage. Then we construct a news coverage factor to explain the abnormal returns. We also confirm the predictability of news coverage. This indicates news coverage has a daily momentum effect. Finally, we propose three investment strategies and verify their profitabilities.

Price changes are induced by aggressive market orders in stock market. We introduce a bivariate marked Hawkes process to model aggressive market order arrivals at the microstructural level. The order arrival intensity is marked by an exogenous part and two endogenous processes reflecting the self-excitation and cross-excitation respectively. We calibrate the model for a Shenzhen Stock Exchange stock. We find that the exponential kernel with a smooth cut-off (i.e. the subtraction of two exponentials) produces much better calibration than the monotonous exponential kernel (i.e. the sum of two exponentials). The exogenous baseline intensity explains the U-shaped intraday pattern. Our empirical results show that the endogenous submission clustering is mainly caused by self-excitation rather than cross-excitation.

We propose a global clock model to achieve time synchronization for consortium blockchains. Based on the existing consortium blockchain framework, a global clock service node is added. We use the Byzantine fault-tolerant algorithm to ensure the stability of the global clock node services. In addition, Cristian and Berkeley time synchronization algorithms are used to improve the confirmation of timestamp information, so as to achieve strong consistency of consensus time. This method can strike a balance between the transaction performance and the timestamp consistency requirements. This method meets the time accuracy requirements of practical business applications, and effectively benefits the promotion of blockchain technology in time-sensitive business scenarios.

In this paper, we study how agents' social learning behavior influences market volatility and how the flash crash emerges. We build a model of order-driven market, in which agents use a combination of four components to form their return anticipations: a social learning component, a chartist component, a fundamentalist component and a noise induced component. By numerical simulations, we find that social learning plays a doubleedged role in market volatilities. On the one hand, social learning plays a role in reducing total price volatilities and stabilizing the market. On the other hand, in some excitable regimes, social learning instead acts as the critical factor contributing to a flash crash. More interestingly, the lower volatility associated with social learning in the stable regime is crucial to give birth to a flash crash. In addition, we do some robust analyses on the roles of social learning by running the model under many different parameter settings. With the increase of the social learning innate parameter, both the average draw-down and draw-up, the average spread and the average gap show a downward trend. Meanwhile, the tail exponents for the draw-downs and draw-ups also show a downward trend, confirming that social learning plays a double-edged role. The chartist belief can stabilize the market when the social influence is not so trusted, while the chartist belief transfers to contribute to the market instability when the social influence is sufficiently trusted. The fundamentalist belief shows quite opposite impacts in respect to the chartist belief. Finally, we summarize typical return and volatility patterns before a flash crash, which will give some inspirations to regulators and investors. (c) 2019 Elsevier B.V. All rights reserved.

Together with the traditional sentiment proxies (closed-end fund discount,turnover and number of IPOs) in Baker and Wurgler (2006,2007),the Chinese volatility index (iVX) is used as a new sentiment proxy to build a weekly composite sentiment index for the Chinese A-share market.The dependent relationship between the sentiment index and the market return and the forecasting effect of the sentiment index for the market return are analyzed.It is found that sentiment index and market return are negatively related.Their concurrent dependence relationship is not obvious,however.The sentiment index has a significant forecasting power for the market return three weeks ahead.The inclusion of iVX can significantly improve the forecasting ability,while the number of IPOs is not an effective sentiment proxy.In addition,when constructing the senti ment index using PCA,the performance of the first two principal components is worse than that of the first principal component.The asymmetry of sentiment effect is analyzed and it is found that a positive sentiment in dex has a much greater impact on future market returns than a negative sentiment index.

Using the partial least squares approach, we construct an aligned sentiment index at weekly frequency. We investigate the predictive power of short-term investor sentiment on the characteristic-sorted portfolio returns. We find that sentiment changes have a positive impact on future stock returns in the Chinese A-share market. We further uncover that the predictive power of the sentiment index is the most significant for the small-size portfolio.

In the canonical framework, we propose an alternative approach for the multifractal analysis based on the detrending moving average method (MF-DMA). We define a canonical measure such that the multifractal mass exponent τ(q) is related to the partition function and the multifractal spectrum f(α) can be directly determined. The performances of the direct determination approach and the traditional approach of the MF-DMA are compared based on three synthetic multifractal and monofractal measures generated from the one-dimensional p-model, the two-dimensional p-model, and the fractional Brownian motions. We find that both approaches have comparable performances to unveil the fractal and multifractal nature. In other words, without loss of accuracy, the multifractal spectrum f(α) can be directly determined using the new approach with less computation cost. We also apply the new MF-DMA approach to the volatility time series of stock prices and confirm the presence of multifractality.

Based on the order flow data of a stock and its warrant, the immediate price impacts of market orders are estimated by two competitive models, the power-law model (PL model) and the logarithmic model (LG model). We find that the PL model is overwhelmingly superior to the LG model, regarding the robustness of the estimated parameters and the accuracy of out-of-sample forecasting. We also find that the price impacts of ask and bid orders are consistent with each other for filled trades, since significant positive correlations are observed between the model parameters of both types of orders. Our findings may provide valuable insights for optimal trade execution.