论文检索

大规模面板高频数据中全局与局部主成分分析的差异AI 解读

In this article, we study the discrepancy between the global principal component analysis (GPCA) and local principal component analysis (LPCA) in recovering the common components of a large-panel high-frequency data. We measure the discrepancy by the total sum of squared differences between common components reconstructed from GPCA and LPCA. The asymptotic distribution of the discrepancy measure is provided when the factor space is time invariant as the dimension p and…

使用高频数据检验因子数量的秩检验AI 解读

In the literature, consistency of the estimates of the number of factors for large-dimensional factor models had been extensively studied recently. But the second-order property of the estimator has long been unsolved due to lack of limiting distribution of the estimators. In this paper, we propose a rank test of the number of factors using large panel high-frequency data contaminated with microstructure noise. The rank test is realized by forming a fixed number of…

波动率泛函的局部非参数 bootstrapAI 解读

Volatility functionals are widely used in financial econometrics. In the literature, they are estimated with realized volatility functionals using high-frequency data. In this paper we introduce a nonparametric local bootstrap method that resamples the high-frequency returns with replacement in local windows shrinking to zero. While the block bootstrap in time series (Hall et al., 1995) aims to reduce correlation, the local bootstrap is intended to eliminate the…

大规模面板高频数据中的系统性与特质性波动率AI 解读

In this paper, we separate the integrated (spot) volatility of an individual Itô process into integrated (spot) systematic and idiosyncratic volatilities, and estimate them by aggregation of local factor analysis (localization) with large-dimensional high-frequency data. We show that, when both the sampling frequency $n$ and the dimensionality $p$ go to infinity and $p\geq C\sqrt{n}$ for some constant $C$, our estimators of High dimensional Itô process; common driving…

利用大面板高频数据检验因子载荷矩阵的时不变性AI 解读

In this paper, we introduce a nonparametric test against the constancy of the factor loading matrix of a high-dimensional continuous-time factor model using high-frequency data. The central limit theorems on the test statistics with and without perturbation are established under the null hypothesis that the factor loading matrix is constant as time evolves. The tests perform well in size and power. Interestingly, the test statistic without perturbation converges at a rate…

存在零时间间隔的高频数据下积分波动率估计AI 解读

In estimating integrated volatility using high-frequency data, it is well documented that the presence of microstructure noise presents a major challenge. Recent literature has shown that the presence of multiple observations, a common feature in datasets, brings additional difficulty. In this study, we show that the preaveraging estimator is still consistent under multiple observations, and the related asymptotic distribution of the estimator is established. We also show…

基于高频金融数据的大维波动率矩阵自适应阈值估计AI 解读

Universal thresholding methods have been developed to estimate the large sparse integrated volatility matrix of underlying assets based on high-frequency financial data. Since the integrated volatility matrix often has entries with a wide range of variability, universal thresholding estimators do not take the varying entries into consideration and may have unsatisfactory performances. This paper investigates adaptive thresholding estimation of the large integrated volatility…

高频数据中的共同因子数量AI 解读

In this paper, we introduce a local principal component analysis approach to determining the number of common factors of a continuous-time factor model with time-varying factor loadings using high-frequency data. The model is approximated locally on shrinking blocks using discrete-time factor models. The number of common factors is estimated by minimizing the penalized aggregated mean squared residual error over all shrinking blocks. While the local mean squared residual…

同一记录时点存在多笔交易时的波动率泛函估计AI 解读

The phenomenon of multiple transactions at each recording time is a common occurrence for high-frequency financial data because of the heavy trading of the market and limitation of the recording mechanism. This situation has existed for many years, but has become more common in recent years because of heavier trading. Surprisingly, there have been few studies on this important issue, in spite of some ad hoc approaches to treat multiple transactions. In this paper we…

高频数据中的纯跳过程检验AI 解读

Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the validity of using pure-jump models. However, these tests suffer from several drawbacks, such as requiring rather stringent conditions and having slow rates of convergence. In this paper, we propose a different test to check whether the…

含跳跃与微观结构噪声的积分波动率估计AI 解读

In this article, we propose a nonparametric procedure to estimate the integrated volatility of an Itô semimartingale in the presence of jumps and microstructure noise. The estimator is based on a combination of the preaveraging method and threshold technique, which serves to remove microstructure noise and jumps, respectively. The estimator is shown to work for both finite and infinite activity jumps. Furthermore, asymptotic properties of the proposed estimator, such as…

用纯跳跃过程建模高频金融数据AI 解读

It is generally accepted that the asset price processes contain jumps. In fact, pure jump models have been widely used to model asset prices and/or stochastic volatilities. The question is: is there any statistical evidence from the high-frequency financial data to support using pure jump models alone? The purpose of this paper is to develop such a statistical test against the necessity of a diffusion component. The test is very simple to use and yet effective. Asymptotic…

半鞅的跳跃活动指数AI 解读

Empirical evidence of asset price discontinuities or “jumps” in financial markets has been well documented in the literature. Recently, Aït-Sahalia and Jacod (2009b) defined a general “jump activity index” to describe the degree of jump activities for asset price semimartingales, and provided a consistent estimator when the underlying process contains both a continuous and a jump component. However, only large increments were used in their estimator so that the effective…

利用含噪高频观测估计跳跃活跃指数AI 解读

It is widely accepted that the high-frequency data are contaminated by microstructure noise, whose effect on the statistical inference has been of increasing interest in the literature. Much of it, however, has focused on the integrated volatility. In this article, we investigate another important characteristic, namely, the jump activity index (JAI) of a discretely sampled semi-martingale corrupted by microstructure noise. We point out that ignoring the microstructure noise…