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纳入交易信息的积分波动率有效估计AI 解读

We consider a setting where market microstructure noise is a parametric function of trading information, possibly with a remaining noise component. Assuming that the remaining noise is Op(1/n), allowing irregular times and jumps, we show that we can estimate the parameters at rate n, and propose a volatility estimator which enjoys n convergence rate. Simulation studies show that our method performs well even with model misspecification and rounding. Empirical studies…

用于估计风险价值的变系数期望分位数模型AI 解读

This article develops a nonparametric varying-coefficient approach for modeling the expectile-based value at risk (EVaR). EVaR has an advantage over the conventional quantile-based VaR (QVaR) of being more sensitive to the magnitude of extreme losses. EVaR can also be used for calculating QVaR and expected shortfall (ES) by exploiting the one-to-one mapping from expectiles to quantiles, and the relationship between VaR and ES. Previous studies on conditional EVaR estimation…

异方差非参数回归中变点的小波分析AI 解读

In this paper we develop wavelet methods for detecting and estimating jumps and cusps in the mean function of a non-parametric regression model. An important characteristic of the model considered here is that it allows for conditional heteroscedastic variance, a feature frequently encountered with economic and financial data. Wavelet analysis of change-points in this model has been considered in a limited way in a recent study by Chen et al. (2008) with a focus on jumps…