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含时变回归量与固定效应的持续期模型半参数估计AI 解读

In this paper, we consider estimation of an accelerated failure time model with time-varying regressors and fixed effects for duration data. We propose computationally simple profiled estimators for both fixed and random censoring cases. Under regularity conditions, we establish consistency and asymptotic normality of the estimators. Simulation studies demonstrate that our estimators perform well in finite samples. Finally, we use data from the First Malaysian Family Life…

资产收益与期权需求的条件密度比模型估计AI 解读

Option-implied risk-neutral densities are widely used for constructing forward-looking risk measures. Meanwhile, risk aversion introduces a multiplicative pricing kernel between the risk-neutral and true conditional densities of the underlying asset’s return. This paper proposes a simple local estimator of the pricing kernel based on inverse density weighting. We characterize the asymptotic bias and variance of the estimator and its multiplicatively corrected density…

卷积 t 分布AI 解读

We introduce a family of multivariate heavy-tailed distributions, termed convolution-t distributions, constructed as convolutions of heterogeneous multivariate t-distributions. Unlike commonly used heavy-tailed distributions, this family captures nonlinear dependencies, accommodates heterogeneous marginal distributions, and reveals cluster structures prevalent in economic data. Importantly, convolution-t distributions admit simple closed-form densities that facilitate…

张量数据的扩散指数预测AI 解读

In this paper, we consider diffusion index forecasting with both tensor and non-tensor predictors, where the tensor structure is preserved with a Canonical Polyadic (CP) tensor factor model. When the number of non-tensor predictors is small, we study the asymptotic properties of the least squares estimator in this tensor factor-augmented regression, allowing for factors with different strengths. We derive an analytical formula for prediction intervals that accounts for the…

波动率究竟是粗糙性还是长记忆？AI 解读

We develop a framework for composite likelihood estimation of parametric continuous-time stationary Gaussian processes. We derive the asymptotic theory of the associated maximum composite likelihood estimator. We implement our approach on a pair of models that have been proposed to describe the random log-spot variance of financial asset returns. A simulation study shows that it delivers good performance in these settings and improves upon a method-of-moments estimation. In…

高维条件因子模型AI 解读

This paper studies estimation and variable selection in conditional factor models with high-dimensional instruments, where the coefficient matrix exhibits a low-rank and row-sparse structure. We propose a multi-stage estimation procedure that combines nuclear norm regularization and adaptive group LASSO regression to consistently estimate latent factors and row-sparse loading coefficients, while selecting relevant instrumental characteristics. We establish theoretical…

重印：融合交易信息的高频数据非参数估计AI 解读

We propose nonparametric estimators for the explicative part of the noise in a model where the market microstructure noise is an unknown function of the trading information while allowing for the presence of an additional residual noise component. Our method allows for dependence in the observable trading information and accommodates the presence of infinite variation jumps in the efficient price process. We establish the convergence and asymptotic normality of the proposed…

分布目标下最优政策学习的公平性正则化AI 解读

A decision maker typically (i) incorporates training data to learn about the relative effectiveness of treatments, and (ii) chooses an implementation mechanism that implies an “optimal” predicted outcome distribution according to some target functional. Nevertheless, a fairness-aware decision maker may not be satisfied achieving said optimality at the cost of being “unfair” against a subgroup of the population, in the sense that the outcome distribution in that subgroup…

非参数位置—尺度模型的极小极大收敛速率AI 解读

This paper studies minimax rates of convergence for nonparametric location-scale models, which include mean, quantile, expectile and momentile regression settings. Under Hellinger differentiability on the error distribution and other mild conditions, we show that the minimax rate of convergence for estimating the regression function under the squared L2 loss is determined by the metric entropy of the nonparametric function class. Different error distributions, including…

矩阵指数空间模型的 GMM 推断AI 解读

This paper offers hypothesis and specification tests for the best generalized methods of moment estimator (BGMME) of the matrix exponential spatial specification (MESS) developed by Debarsy et al. (2015). First, as the BGMME is a two-step estimator, we formulate corrected standard errors using a modified version of the finite sample correction method in Windmeijer (2005) that accounts for the fact that the BGMME makes more extensive use of the first-stage estimator than the…

时间变换扩散模型中已实现方差估计的高效抽样AI 解读

This paper analyzes the benefits of sampling intraday returns in intrinsic time for the realized variance (RV) estimator. We theoretically show in finite samples that depending on the permitted sampling information, the RV estimator is most efficient under either hitting time sampling that samples whenever the price changes by a pre-determined threshold, or under the new concept of realized business time that samples according to a combination of observed trades and…

内生抽样时点下离散观测价格过程的跳跃检验AI 解读

This paper introduces a novel nonparametric high-frequency jump test for discretely observed Itô semimartingales. Based on observations sampled recursively at first exit times from a symmetric double barrier, our method distinguishes between threshold exceedances caused by the Brownian component and jumps, which enables the construction of a feasible, noise-robust statistical test. Simulation results demonstrate superior finite-sample performance of our test compared to…

定制已实现波动率：面向波动率预测的风险度量AI 解读

Standard realized volatility (RV) measures estimate the latent volatility of an asset price using high frequency data with no reference to how or where the estimate will subsequently be used. This paper presents methods for “tailoring” the estimate of volatility to the application in which it will be used. For example, if the volatility measure will be used in a specific parametric forecasting model, it may be possible to exploit that knowledge to construct a better measure…

多元已实现 GARCH 模型AI 解读

We propose a novel class of multivariate GARCH models that incorporate realized measures of volatility and correlations. The key innovation is an unconstrained vector parametrization of the conditional correlation matrix, which enables the use of factor models for correlations. This approach elegantly addresses the main challenge faced by multivariate GARCH models in high-dimensional settings. As an illustration, we explore block correlation matrices that naturally simplify…

已实现协方差度量的概率分布AI 解读

Realized covariance measures (RCs) are an essential input for assessing the risks of different investment allocations. Thus, it is useful to model and forecast them. To this end, a realistic distributional assumption is needed. In this paper, we compare all probability distributions that have so far in the literature been applied to time-series of RCs. We derive them in a unified framework based on their stochastic representations using random lower and upper triangular…

一致最小方差无偏分块估计量AI 解读

We provide necessary and sufficient conditions for an (Unbiased) Block estimator to have Uniformly Minimum Variance. Our theory parallels the theory of UMVU estimation, the main novel insight being the focus on the covariance among blocks. We use this theory to derive lower variance bounds for block estimators of functionals of high-frequency volatility when the block size is fixed. We further show the relevance of the new theory for the classical problem of estimation of…

高频数据下的高维回归系数检验AI 解读

This paper presents the first study on high-dimensional regression coefficient tests with high-frequency financial data. These tests allow the number of regressors to be larger than the number of observations within each estimation block and can grow to infinity in asymptotics. In this paper, the sum-type test and max-type test have been proposed, where the former is suitable for the dense alternative (many small betas) and the latter is suitable for the sparse alternative…

稳健的已实现积分贝塔估计及其动态分析AI 解读

In this paper, we develop a robust non-parametric realized integrated beta estimator using high-frequency financial data contaminated by microstructure noise, which is robust to the stylized features, such as the time-varying beta and the price-dependent and autocorrelated microstructure noise. With this robust realized integrated beta estimator, we investigate dynamic structures of integrated betas and find a persistent autoregressive structure. To model this dynamic…

已实现漂移AI 解读

Drift and volatility are two mainsprings of asset price dynamics. While volatilities have been studied extensively in the literature, drifts are commonly believed to be impossible to estimate and largely ignored in the literature. This paper shows how to detect drift using realized autocovariance implemented on high-frequency data. We use a theoretical treatment in which the classical model for the efficient price, an Itō semimartingale possibly contaminated by…

利用短期期权估计日内波动率模式AI 解读

We propose a nonparametric estimator for the deterministic periodic component of volatility from short-dated options within an in-fill asymptotic setting. The estimator uses options with zero and one day to expiration sampled at high-frequency during a trading day. At each point in time, we aggregate the options to form nonparametric estimates of conditional risk-neutral expectations of future integrated return variation for the two available option tenors. A suitable ratio…

积分波动率与瞬时波动率的稳健估计AI 解读

We introduce a new method to estimate the integrated volatility (IV) and the spot volatility (SV) based on noisy high-frequency data. Our method employs the ReMeDI approach introduced by Li and Linton (2022) to estimate the moments of microstructure noise and thereby eliminate their influence, and the pre-averaging method to target the volatility parameter. The method is robust: it can be applied when the efficient price exhibits stochastic volatility and jumps, the…

高频双因子模型中微观结构噪声与有效价格共同因子的估计AI 解读

We develop the Double Principal Component Analysis (DPCA) based on a dual factor structure for high-frequency intraday returns contaminated with microstructure noise. The dual factor structure allows a factor structure for microstructure noise in addition to the factor structure for efficient log-prices. We construct estimators of factors for both efficient log-prices and microstructure noise as well as their common components, and provide uniform consistency of these…

通过一阶条件揭示工厂—产品生产率：对 Orr（2022）的稳健复现AI 解读

ABSTRACT We assess the replicability of Orr's (2022) method for estimating within‐plant productivity across product lines, which combines demand estimation with cost minimization. The original study uses input price shocks in other output markets as instrumental variables, with exclusion restrictions based on downstream purchase shares. Reconstructing the original dataset of Indian machinery producers from 2000–2007, we reproduce the main productivity patterns and…

处理效应估计的 PCDID 方法：进一步考察AI 解读

ABSTRACT In the present paper, we study the so‐called “PCDID” approach to treatment effects estimation in panels with interactive effects where the factors represent trends whose effect need not be parallel across the cross‐sectional units. Our interest in this step‐wise approach originates with the observation that the interactive effects are ignored in the first step, which should not be possible without affecting subsequent steps. We confirm that the estimated second‐step…