The U.S. Treasury yields reached a 20-year high under acute inflation pressure in the post-pandemic era amid aggravated geopolitical conflicts. To quantify the underlying effects of regional geopolitical risks (GPRs) of key U.S. strategic interests, we employ an extended affine term structure model with unspanned GPRs and conventional macroeconomic drivers. We find that GPR shocks, particularly those manifesting U.S.-China rivalry, contribute more to expectations and variations of inflation and yields than shocks to U.S. macroeconomic variables. The results warn on the adequacy of monetary policy in curbing inflation in a fragmented global order with escalating GPRs.

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.

Owing to the liberalisation of financial markets, the impact of international capital flows on the Chinese stock market has become substantial. This study investigates the effects of economic policy uncertainty (EPU), geopolitical risk (GPR), consumer sentiment (CCI), macroeconomic fundamentals (MECI), and money supply (M2) on the correlations between the stock and exchange rate markets. The negative correlation between these two markets has become more pronounced in recent years. Moreover, EPU, GPR, CCI, and MECI negatively impact long-term stock-exchange rate correlations, while M2 has a positive impact. Portfolios of stock-exchange rates effectively reduce risk, especially when considering structural breaks.

This paper focuses on how Chinese and global economic policy uncertainties (CNEPU and GEPU) and geopolitical risks (CNGPR and GGPR) affect the growth of inbound tourism in China using ARDL and NARDL models as well as monthly series of Chinese inbound tourism revenue and arrivals. Firstly, we find significant effects of CNGPR and GGPR as well as GEPU on the growth of inbound tourism in Hainan Province and even in China nationwide, while the impact of CNEPU is limited. Among them, GEPU always has a significant long-term negative impact on inbound tourism growth (both inbound tourism revenue and inbound tourism arrivals). However, CNGPR has a significant short-term negative impact on inbound tourism growth in China nationwide but it has a significant long-term negative impact on inbound tourism growth in Hainan Province. Besides, estimation results of NARDL model further show the significant short-term effects of GEPU and GGPR on the growth of inbound tourism arrivals in Hainan Province and even in China nationwide, and such short-term effects are always significantly asymmetric. Among them, the negative components of GGPR can always more influence the growth of inbound tourism arrivals. However, the positive components of GEPU can more influence the growth inbound tourism arrivals in Hainan Province, but the negative components of GEPU can more influence the growth of inbound tourism arrivals in China nationwide.

From an industry perspective, this paper applies the quantile regression to investigate the impact of investor sentiment (IS) and China’s/U.S. geopolitical risks (GPR) on Chinese stock market volatility. Considering the structural break of the stock market for theperiod2003/02-2021/10, we find that the impact of geopolitical risk on stock market volatility is highly heterogeneous, and its significance mostly appears in the upper and lower tails. At the market level, China’s and U.S. GPR/IS and their interaction effects have no significant impact on China’s stock market volatility. However, there has an asymmetric dependence between China’s and U.S. GPR/IS and stock market volatility, and the dependence structure is changing. At the industry level, the current and lagging effects of China’s and U.S. GPR on industry stock market volatility are heterogeneous. Second, for most industries, China’s and U.S. GPR/IS can exacerbate industry stock market volatility both in bullish and bearish markets. In addition, China’s and U.S.GPR/IS and their interaction effects are heterogeneous and asymmetric, and the effects changes with the break point. Finally, compared with China’s GPR, the U.S. GPR has a larger impact on the industry stock market. The interactive effects of the U.S. GPR and IS can influence more industry stock market volatility.