This paper investigates the contribution of futures market information to enhancing the predictive accuracy of macroeconomic forecasts, using data from China. We employ three cat-egories of predictors: monthly macroeconomic factors, daily commodity futures factors, and daily financial futures variables. Principal component analysis is applied to extract key fac-tors from large data sets of monthly macroeconomic indicators and daily commodity futures contracts. To address the challenge of mixed sampling frequencies, these predictors are incor-porated into factor-MIDAS models for both nowcasting and long-term forecasting of critical macroeconomic variables. The empirical results indicate that financial futures data provide modest improvements in forecasting secondary and tertiary GDP, whereas commodity futures factors significantly improve the accuracy of PPI forecasts. Interestingly, for PMI forecast-ing, models relying exclusively on futures market data, without incorporating macroeconomic factors, achieve superior predictive performance. Our findings underscore the significance of futures market information as a valuable input to macroeconomic forecasting.

It is diffffcult to understand the risk-return trade-off in option market with observable factormodels. In this paper, we employ a latent factor model for delta-hedge option returns over a varietyof important exchange traded options in China, based on the instrumented principal componentanalysis (IPCA). This model incorporates conditional betas instrumented by option characteristics,to tackle the diffffculty caused by short lifespans and rapidly migrating characteristics of options. Ourresults show that a three-factor IPCA model can explain 19.30% variance in returns of individualoptions and 99.23% for managed portfolios. An asset pricing test with bootstrap shows that there isno unexplained alpha term with such a model. Comparison with observable factor model indicatesthe necessity of including characteristics. We also provide subsample analysis and characteristicimportance.

This paper proposes a conditional factor model embedded with heterogeneous group structure, called grouped Instrumented Principal Component Analysis (Grouped IPCA) model, to study the enhancement of industry classifcations on the pricing power of frm characteristics. We derive an inferential theory on the alternating least square (ALS) estimators of the grouped IPCA model under an unbalanced panel data. Based on this, we use two BIC-type information criteria to determine the number of latent factors. We further examine the group heterogeneity with a bootstrap test statistics. Simulations are conducted to evaluate both our asymptotic theory and test statistics. In the empirical study, we show that the in-sample performance of Grouped IPCA model excels the IPCA model, and fnd a strong evidence on the incremental pricing power of industries.

We apply various test portfolios and alternative statistical methodologies to evaluate the performance of eleven prominent asset pricing models. To compile the test portfolios, we construct 105 anomalies in China and apply the 23 significant anomalies as test assets for model comparison. The results indicate that in the time-series test and anomalies explanation, the Hou et al. (2019) five-factor q model exhibits the best overall performance. The pairwise cross-sectional R^2s and the multiple model comparison tests affirm that the Hou et al. (2019) five-factor q model, the Fama and French (2018) six-factor (FF6) model and the Kelly et al. (2019) five-factor Instrumented Principal Component Analysis (IPCA5) model stand out as the top performers. Notably, the performance of the five-factor q model is insensitive to variations in experimental design.

This paper studies the Level, Slope, and Curve factor model under different tests in the Chinese stock market. Empirical asset pricing tests reveal that the slope factor in the model represents either reversal or momentum effect for the Chinese stocks. Further tests on individual stocks demonstrate that the Level, Slope, and Curve model using effective predictor variables outperforms other common factor models, thus a filter in virtue of multiple hypothesis testing is designed to identify the effective predictor variables. In the filter models, the cross-section anomaly factors perform better than the time-series anomaly factors under different tests, and trading frictions, momentum, and growth categories are potential drivers of Chinese stock returns.

We analyze the latent factors for the Chinese market through the recently proposed risk premium principal component analysis (RP-PCA). Our empirical research covers 95 firm characteristics. We demonstrate that the RP-PCA on the Chinese market can identify factors that capture co-movements and explain pricing. Compared to the traditional PCA approach, it explains a larger proportion of return variation in both double-sorted and single-sorted portfolios. The Sharpe ratios of the tangency portfolios are significantly higher than those of the standard PCA. Additionally, we show that the RP-PCA loadings are more closely associated with factor returns.

Regional financial development has greatly supported the rapid growth of Chinese municipal corporate bonds. This study introduces the concept of regional financial resources and constructs an informative measure of regional financial development by using principal component analysis (PCA), incorporating 13 indicators from three primary financial industries, including bank, security and insurance. Using a sample of municipal corporate bonds (MCBs) issued in China from 2009 to 2019, we find that an increase in regional financial development is associated with significant MCB credit spreads narrowing. This effect can be realized by improving fiscal stability and debt sustainability. Additionally, this narrowing varies among cities and provinces with different fiscal conditions and economic development. The results are also verified through a series of robustness tests. This study proposes possible policy suggestions for improving the Chinese fiscal management and MCBs market.

The macroeconomic risk associated with climate change potentially results in a risk premium on asset prices. Using a sample of 11,468 Chinese quasi-municipal bonds from 2014-2021 in 267 cities, this research investigates the impact of climate risk on the credit spreads of quasi-municipal bonds. We employ principal component analysis (PCA) to construct a climate risk index and find that climate risk significantly increases credit spreads by increasing the local government fiscal gap and debt burden. The effect of climate risk is more remarkable for bonds that have shorter maturity and lower corporate ratings, issued by smaller city investment companies and corporations located in regions with stronger environmental regulation, stronger climate risk perception, and better green financial development. A significant relationship is also observed in the eastern regions but not the western regions. This study broadens the scope of quasi-municipal bond credit spread determinants from traditional financial to climate indicators.

We establish a framework to study the factor structure in stock variance under a high-frequency and high-dimensional setup. We prove the consistency of conducting principal component analysis on realized variances in estimating the factor structure. Moreover, based on strong empirical evidence, we propose a multiplicative volatility factor (MVF) model, where stock variance is represented by a common variance factor and a multiplicative lognormal idiosyncratic component. We further show that our MVF model leads to significantly improved volatility prediction. The favorable performance of the proposed MVF model is seen in both US stocks and global equity indices.