Changes in the span of systematic risk exposures
系统性风险暴露张成空间的变化AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要研究资产收益的潜在系统性因子暴露,并明确使用日内金融数据和局部时间窗口。
系统性风险暴露张成空间的变化AI 解读 ↓
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We develop a test for deciding whether the linear spaces spanned by the factor exposures of a large cross‐section of assets toward latent systematic risk factors at two distinct points in time are the same. The test uses a panel of asset returns in local windows around the two time points. The asymptotic setup is of joint type: the number of assets and the number of return observations per asset increase asymptotically while the length of both time windows shrinks. We estimate the factor exposures, up to rotation, over the two periods using classical principal component analysis and evaluate their projection discrepancy, which is rotation invariant. This projection discrepancy is then centered with one between factor exposures computed over a partition of the pooled return data into odd and even increments. We derive the limit distribution of the statistic under the null hypothesis and develop an easy‐to‐implement bootstrap for constructing the critical region of the test. The test is applied to intraday financial data to determine whether the linear span of assets' systematic risk exposures differ during a trading day or after a release of important economic information.
高频价格停滞因子与波动率估计
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已实现正则化回归
We develop a continuous-time penalized regression framework for the estimation of time-varying coefficients and variable selection when both the response and covariates are Itô semimartingales with jumps. The coefficient paths are approximated by spline basis expansions and estimated via least squares from truncated high-frequency increments. In a finite-dimensional setting, we establish consistency and derive a feasible asymptotic distribution for the integrated coefficient…
高频价格停滞因子与波动率估计
In this paper, we propose a price staleness factor model that accounts for pervasive market friction across assets and incorporates relevant covariates. Using large-panel high-frequency data, we derive the maximum likelihood estimators of the regression coefficients, the nonstationary factors, and their loading parameters. These estimators recover the time-varying price staleness probabilities. We develop asymptotic theory in which both the dimension $d$ and the sampling…
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高频金融数据的同步化
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系统性因子风险暴露的日内横截面分布
We develop a test for the detection of intraday changes in the cross-sectional distribution of assets’ exposure to observable factors. The test is constructed for a panel of high-frequency asset returns, with the size of the cross-section and the sampling frequency increasing simultaneously. It is based on a comparison of the empirical characteristic functions of estimates of the assets’ factor loadings at different parts of the trading day, formed from local blocks of asset…