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利用新闻分析预测波动率AI 解读

ABSTRACT This study investigates the potential of news sentiment in predicting stock market volatility. We augment traditional time series models of realized volatility with the sentiment of macroeconomic and firm‐specific news. Our results demonstrate that incorporating the sentiment of domestic macroeconomic news significantly improves volatility predictions for individual stocks and the S&P 500 Index. Notably, we find substantial enhancements in long‐horizon volatility…

连续时间整数值拖曳过程的推断与预测AI 解读

This paper develops likelihood-based methods for estimation, inference, model selection, and forecasting of continuous-time integer-valued trawl processes. The full likelihood of integer-valued trawl processes is, in general, highly intractable, motivating the use of composite likelihood methods, where we consider the pairwise likelihood in lieu of the full likelihood. Maximizing the pairwise likelihood of the data yields an estimator of the parameter vector of the model,…

含测量误差时间序列的持续性与自相关函数估计AI 解读

An economic time series can often be viewed as a noisy proxy for an underlying economic variable. Measurement errors will influence the dynamic properties of the observed process and may conceal the persistence of the underlying time series. In this paper we develop instrumental variable (IV) methods for extracting information about the latent process. Our framework can be used to estimate the autocorrelation function of the latent volatility process and a key persistence…

股票价格的持续期依赖AI 解读

This article studies time series dependence in the direction of stock prices by modeling the (instantaneous) probability that a bull or bear market terminates as a function of its age and a set of underlying state variables, such as interest rates. A random walk model is rejected both for bull and bear markets. Although it fits the data better, a generalized autoregressive conditional heteroscedasticity model is also found to be inconsistent with the very long bull markets…