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杠杆效应与波动率之波动的经验特征函数估计及可行推断AI 解读

We develop jump-robust estimators of the leverage effect and volatility of volatility using high-frequency data. Our construction begins with a spot volatility estimator based on the empirical characteristic function of high-frequency increments. This method can mitigate the contamination from jumps, which can be of infinite variation. We then construct estimators of the leverage effect and volatility of volatility and correct for the bias induced by spot volatility…

波动率的已实现 CopulaAI 解读

We study a new measure of codependency in the second moment of a continuous-time multivariate asset price process, which we name the realized copula of volatility. The statistic is based on local volatility estimates constructed from high-frequency asset returns and affords a nonparametric estimator of the empirical copula of the latent stochastic volatility. We show consistency of our estimator with in-fill asymptotic theory, either with a fixed or increasing time span. In…

高维瞬时波动率矩阵的谱分析及应用AI 解读

In random matrix theory, the spectral distribution of the covariance matrix has been well studied under the large dimensional asymptotic regime when the dimensionality and the sample size tend to infinity at the same rate. However, most existing theories are built upon the assumption of independent and identically distributed samples, which may be violated in practice. For example, the observational data of continuous-time processes at discrete time points, namely, the…

即期相关过程日内变化的非参数检验AI 解读

The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upward-sloping and almost piecewise linear relationship at the intraday horizon. There is notably lower-on average less positive-correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such variation in a correlation process. The test statistic has a known…

波动率的已实现联合拉普拉斯变换及其依赖性检验AI 解读

In this paper, we first investigate the estimation of the empirical joint Laplace transform of volatilities of two semi-martingales within a fixed time interval [0, T] by using overlapped increments of high-frequency data. The proposed estimator is robust to the presence of finite variation jumps in price processes. The related functional central limit theorem for the proposed estimator has been established. Compared with the estimator with non-overlapped increments, the…

无限变差跳跃与相关市场微观结构噪声下的瞬时波动率估计AI 解读

Jumps and market microstructure noise are stylized features of high-frequency financial data. It is well known that they introduce bias in the estimation of volatility (including integrated and spot volatilities) of assets, and many methods have been proposed to deal with this problem. When the jumps are intensive with infinite variation, the efficient estimation of spot volatility under serially dependent noise is not available and is thus in need. For this purpose, we…