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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…

预测回归的新稳健推断方法AI 解读

We propose a robust inference method for predictive regression models under heterogeneously persistent volatility as well as endogeneity, persistence, or heavy-tailedness of regressors. This approach relies on two methodologies, nonlinear instrumental variable estimation and volatility correction, which are used to deal with the aforementioned characteristics of regressors and volatility, respectively. Our method is simple to implement and is applicable both in the case of…

含动态潜在因子的小维度 VAR 模型中的工具变量推断AI 解读

We study semiparametric inference in a small-dimensional vector autoregressive (VAR) model of order p augmented by unobservable common factors with a dynamic described by a VAR process of order q . This state-space specification is useful to measure separately the direct causality effects and the responses to dynamic common factors. We show that the state-space parameters are identifiable from the autocovariance function of the observed process. We estimate the model by…

线性模型的欠识别检验及其动态面板与资产定价应用AI 解读

This paper develops the links between overidentification tests, underidentification tests, score tests and the Cragg and Donald (1993, 1997) and Kleibergen and Paap (2006) rank tests in linear instrumental variable (IV) models. For the structural linear model y=Xβ+u, with the endogenous explanatory variables partitioned as X=x1X2, this general framework shows that standard underidentification tests are tests for overidentification in an auxiliary linear model, x1=X2δ+ɛ,…

持续性预测回归中的参数不稳定与结构变化检验AI 解读

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and…

多资产因子模型：强因子、弱因子与两步估计AI 解读

This paper re-examines the problem of estimating risk premia in unconditional linear factor pricing models. Typically, the data used in the empirical literature are characterized by weakness of some pricing factors, strong cross-sectional dependence in the errors, and (moderately) high cross-sectional dimensionality. Using an asymptotic framework where the number of assets/portfolios grows with the time span of the data while the risk exposures of weak factors are…

残差增广的 IVX 预测回归AI 解读

Bias correction in predictive regressions is known to reduce the empirical size problems of OLS-based predictability tests with persistent predictors. This paper shows that bias correction is also achieved in the context of the extended instrumental variable (IVX) predictability testing framework introduced by Kostakis et al. (2015). To be specific, new IVX-based statistics subject to a bias correction analogous to that proposed by Amihud and Hurvich (2004) are introduced.…

股票收益阶段性可预测性的检验AI 解读

Standard tests based on predictive regressions estimated over the full available sample data have tended to find little evidence of predictability in stock returns. Recent approaches based on the analysis of subsamples of the data suggest in fact that predictability where it occurs might exist only within so-called “pockets of predictability” rather than across the entire sample. However, these methods are prone to the criticism that the subsample dates are endogenously…

股票收益的非线性可预测性？预测回归中的参数与非参数推断AI 解读

Nonparametric test procedures in predictive regressions have χ2 limiting null distributions under both low and high regressor persistence, but low local power compared to misspecified linear predictive regressions. We argue that IV inference is better suited (in terms of local power) for analyzing additive predictive models with uncertain predictor persistence. Then, a two-step procedure is proposed for out-of-sample predictions. For the current estimation window, one first…

欧拉方程的非参数识别与估计AI 解读

We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption-based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric estimator based on our identification analysis, which combines standard…

动态预测回归的统一检验AI 解读

Testing for predictability of asset returns has been a long history in economics and finance. Recently, based on a simple predictive regression, Kostakis, Magdalinos, and Stamatogiannis derived a Wald type test based on the context of the extended instrumental variable (IVX) methodology for testing predictability of stock returns, and Demetrescu showed that the local power of the standard IVX-based test could be improved for some range of alternative hypotheses and the…

多元广义双曲随机向量的二次型AI 解读

Summary This article presents exact and approximate expressions for tail probabilities and partial moments of quadratic forms in multivariate generalized hyperbolic random vectors. The derivations involve a generalization of the classic inversion formula for distribution functions (Gil-Pelaez, 1951). Two numerical applications are considered: the distribution of the two-stage least squares estimator and the expected shortfall of a quadratic portfolio.

厚尾条件下的波动率回归AI 解读

Nowadays, a common method to forecast integrated variance is to use the fitted value of a simple OLS autoregression of the realized variance. However, non-parametric estimates of the tail index of this realized variance process reveal that its second moment is possibly unbounded. In this case, the behavior of the OLS estimators and the corresponding statistics are unclear. We prove that when the second moment of the spot variance is unbounded, the slope of the spot…

相依微观结构噪声与高频数据的积分波动率估计AI 解读

In this paper, we develop econometric tools to analyze the integrated volatility (IV) of the efficient price and the dynamic properties of microstructure noise in high-frequency data under general dependent noise. We first develop consistent estimators of the variance and autocovariances of noise using a variant of realized volatility. Next, we employ these estimators to adapt the pre-averaging method and derive consistent estimators of the IV, which converge stably to a…

基于均方误差与条件均方误差的最优阈值选择AI 解读

We consider a univariate semimartingale model for (the logarithm of) an asset price, containing jumps having possibly infinite activity. The nonparametric threshold estimator IV̂n of the integrated variance IV≔∫0Tσs2ds proposed in Mancini (2009) is constructed using observations on a discrete time grid, and precisely it sums up the squared increments of the process when they are below a threshold, which depends on the observation time step and, sometimes, model parameters or…

股票收益可预测性的再考察AI 解读

We provide a simple and innovative approach to test for predictability in stock returns. Our approach consists of two methodologies, time change and instrumental variable estimation, which are employed respectively to deal effectively with persistent stochastic volatility in stock returns and endogenous nonstationarity in their predictors. These are prominent characteristics of the data used in predictive regressions, which are known to have a substantial impact on the test…

预测回归中基于工具变量与变量增补的推断AI 解读

Valid inference in predictive regressions depends in a crucial manner on the degree of persistence of the predictor variables. The paper studies test procedures that are robust in the sense that their asymptotic null distributions are invariant to the persistence of the predictor, that is, the limiting distribution is the same irrespective of whether the regressors are stationary or (nearly) integrated. Existing procedures are often conservative (e.g. tests based on…

含测量误差时间序列的持续性与自相关函数估计AI 解读

An economic time series can often be viewed as a noisy proxy for an underlying economic variable. Measurement errors will influence the dynamic properties of the observed process and may conceal the persistence of the underlying time series. In this paper we develop instrumental variable (IV) methods for extracting information about the latent process. Our framework can be used to estimate the autocorrelation function of the latent volatility process and a key persistence…

非参数与半参数模型的局部识别AI 解读

In parametric, nonlinear structural models, a classical sufficient condition for local identification, like Fisher (1966) and Rothenberg (1971), is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We derive an analogous result for the nonparametric, nonlinear structural models, establishing conditions under which an infinite dimensional analog of the full rank condition is sufficient for local identification.…

不同持久性程度下的预测回归与稳健长跨度回归AI 解读

The paper proposes a novel inference procedure for long-horizon predictive regression with persistent regressors, allowing the autoregressive roots to lie in a wide vicinity of unity. The invalidity of conventional tests when regressors are persistent has led to a large literature dealing with inference in predictive regressions with local to unity regressors. Magdalinos and Phillips (2009b) recently developed a new framework of extended IV procedures (IVX) that enables…

用于后验与预测模拟的自适应重要性加权 EM 算法AI 解读

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed methods are robust in the sense that they can handle target distributions that exhibit non-elliptical shapes such as multimodality and skewness. The basic method makes use of sequences of importance weighted Expectation Maximization steps in order to efficiently construct a mixture of Student-t densities that approximates accurately the target…

广义可加交互非线性波动模型的局部工具变量估计AI 解读

In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible nonparametric ARCH model that can potentially explain the behavior of the wide variety of financial assets. The model is estimated using the generalized version of the local instrumental variable estimation method first introduced in Kim and Linton (2004, Econometric Theory 20,…

半参数 IGARCH(1,1) 模型的估计AI 解读

We propose a semiparametric IGARCH model that allows for persistence in variance but also allows for more flexible functional form. We assume that the difference of the squared process is weakly stationary. We propose an estimation strategy based on the nonparametric instrumental variable method. We establish the rate of convergence of our estimator.

共同函数主成分AI 解读

Functional principal component analysis (FPCA) based on the Karhunen–Loève decomposition has been successfully applied in many applications, mainly for one sample problems. In this paper we consider common functional principal components for two sample problems. Our research is motivated not only by the theoretical challenge of this data situation, but also by the actual question of dynamics of implied volatility (IV) functions. For different maturities the log-returns of…