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日内风险价值：非对称自回归条件持续时间方法AI 解读

We propose to compute the Intraday Value-at-Risk (IVaR) for stocks using real-time transaction data. Tick-by-tick data filtered by price duration are modeled using a two-state asymmetric autoregressive conditional duration (AACD) model, and the IVaR is calculated using Monte Carlo simulation based on the estimated AACD model. Backtesting results for the New York Stock Exchange (NYSE) show that the IVaR calculated using the AACD method outperforms those using the Dionne et…

高频波动率估计：自回归条件持续期方法AI 解读

We propose a method to estimate the intraday volatility of a stock by integrating the instantaneous conditional return variance per unit time obtained from the autoregressive conditional duration (ACD) model, called the ACD-ICV method. We compare the daily volatility estimated using the ACD-ICV method against several versions of the realized volatility (RV) method, including the bipower variation RV with subsampling, the realized kernel estimate, and the duration-based RV.…

We present a generalized LM test of heteroscedasticity allowing the presence of data transformation and a generalized LM test of functional form allowing the presence of heteroscedasticity. Both generalizations are meaningful as non-normality and heteroscedasticity are common in economic data. A joint test of functional form and heteroscedasticity is also given. These tests are further "studentized" to account for possible excess skewness and kurtosis of the errors in the…