Abstract
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.
Highlights
Price volatility in the stock market is the result of a combination of factors in the whole economy. rough the observation of historical data, we can analyze the fluctuation over a period of time. e ARCH model [1] and stochastic volatility model [2] are two main models for studying the volatility of the time series. e SV model adds a random factor to the ARCH model, which makes the SV model better to fit the real stock fluctuations. e SV model is widely used to analyze the high-frequency financial time series [3, 4]
Research on herd behavior has focused on a single market, such as the study of herd behavior in Chinese stock markets [13]
More and more herd behavior research studies began to pay attention to the spillover between markets. e authors in [14] find the herd behavior caused by the unexpected impact from both China and US markets and have proved that the Chinese market has a weaker response to the impact from the US market
Summary
Price volatility in the stock market is the result of a combination of factors in the whole economy. rough the observation of historical data, we can analyze the fluctuation over a period of time. e ARCH model [1] and stochastic volatility model [2] are two main models for studying the volatility of the time series. e SV model adds a random factor to the ARCH model, which makes the SV model better to fit the real stock fluctuations. e SV model is widely used to analyze the high-frequency financial time series [3, 4]. E study of the investor’s behavior shows that the herd behavior does appear in a certain market but widely exists in various stock markets [11]. Research on herd behavior has focused on a single market, such as the study of herd behavior in Chinese stock markets [13]. More and more herd behavior research studies began to pay attention to the spillover between markets. It is necessary to research on cross-market effects of the stock market volatility and herd behavior. En, we use the volatility spillover correlation parameters between nine markets to build a Bayesian network.
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