This paper examines the spillover effect between bitcoin, gold, crude oil, and major stock markets by using the MSV model with dynamic correlation and Granger causality. The empirical results of the DC‐GC‐MSV model are logically correct and convergent. The DIC test result has proved that the DC‐GC‐MSV model is better and more accurate. Bitcoin has no significant Granger causality spillover effect than other assets. As a safe haven product for stock assets, gold price has one‐way spillover effect from stock market volatility. Moreover, crude oil has the highest correlation with the stock market. In the recent COVID‐19 epidemic and the sluggish economic environment, investors need to consider a balanced asset allocation among low‐correlation assets, medium‐correlation assets, and high‐correlation assets to reduce risks.

This paper is concerned with the multivariate stochastic volatility modeling of the stock market. We investigate a DGC‐t‐MSV model to find the historical volatility spillovers between nine markets, including S&P, Nasdaq, SSE, SZSE, HSI, FTSE, CAC, DAX, and Nikkei indices. We use the Bayesian network to analyze the spreading of herd behavior between nine markets. The main results are as follows: (1) the DGC‐t‐MSV model we considered is a useful way to estimate the parameter and fit the data well in the stock market; (2) our computational analysis shows that the S&P and Nasdaq have higher volatility spillovers to the Shanghai and Shenzhen stock markets; (3) the results also show that there is a strong correlation between stock markets in the same region.

Based on the data of Beijing‐Tianjin‐Hebei Economic Circle from 2010 to 2019, this paper uses the spatial Durbin model to empirically analyze the impact of financial development and technological innovation on industrial agglomeration. The following are the conclusions of this study: (1) financial development has a positive effect on industrial agglomeration; however, a significant difference exists in the weight effect of the geographic distance matrix compared to the weight of the economic distance matrix; (2) in the spatial Durbin model with two matrix weights, technological innovation has a significant positive effect on industrial agglomeration; and (3) in the spatial Durbin model with two matrix weights, the interaction has a significant negative effect on industrial agglomeration. Therefore, the government should further implement the coordinated development strategy, promoting regional technological innovation for a long time to realize its integration with financial development.

We investigate the spillover effect between crude oil future prices, crude oil spot prices, and stock index by using the multivariate stochastic volatility model. These tests between each market show the significant Granger causes of spillover effect. More and more evidences show that the crude oil price has been affected by other financial markets. The oil future played an important role in the energy market. WTI and Brent oil future have more spillover effect than INE oil future. The result shows that S&P stock market is more sensitive to the oil price than Shanghai stock market. The cross-market spillover effect we found can give some advices for the investor of oil and stock market. DIC test shows that DGC-MSV-t is considered effective and more accurate.