论文检索

弱 σ 收敛：理论与应用AI 解读

The concept of relative convergence, which requires the ratio of two time series to converge to unity in the long run, explains convergent behavior when series share commonly divergent stochastic or deterministic trend components. Relative convergence of this type does not necessarily hold when series share common time decay patterns measured by evaporating rather than divergent trend behavior. To capture convergent behavior in panel data that do not involve stochastic or…

限价订单簿变量能否完全解释市场微观结构噪声的检验AI 解读

In this paper, we build tests for the presence of residual noise in a model where the market microstructure noise is a known parametric function of some variables from the limit order book. The tests compare two distinct quasi-maximum likelihood estimators of volatility, where the related model includes a residual noise in the market microstructure noise or not. The limit theory is investigated in a general nonparametric framework. In the presence of residual noise, we…

随机系数自回归模型中系数随机性的检验AI 解读

We propose a test to discern between an ordinary autoregressive model, and a random coefficient one. To this end, we develop a full-fledged estimation theory for the variances of the idiosyncratic innovation and of the random coefficient, based on a two-stage WLS approach. Our results hold irrespective of whether the series is stationary or nonstationary, and, as an immediate result, they afford the construction of a test for ”relevant” randomness. Further, building on these…

随机系数连续时间系统：极端样本路径行为检验AI 解读

This paper studies a continuous time dynamic system with a random persistence parameter. The exact discrete time representation is obtained and related to several discrete time random coefficient models currently in the literature. The model distinguishes various forms of unstable and explosive behavior according to specific regions of the parameter space that open up the potential for testing these forms of extreme behavior. A two-stage approach that employs realized…

不连续杠杆效应的估计：来自纳斯达克订单簿的证据AI 解读

An extensive empirical literature documents a generally negative relation, named the “leverage effect,” between asset returns and changes of volatility. It is more challenging to establish such a return–volatility relationship for jumps in high-frequency data. We propose new nonparametric methods to assess and test for a discontinuous leverage effect — i.e. a covariation between contemporaneous jumps in prices and volatility. The methods are robust to market microstructure…

大规模社交网络中的门户节点筛选AI 解读

Network autoregression model (NAM), as a powerful tool to study user social behaviors on large scale social networks, has drawn great attention in recent years. In this paper, we are interested in identifying the influential users (i.e., portal nodes) in a social network under the framework of NAM. Especially, we consider the autoregression model that allows to have a heterogeneous and sparse network effect coefficients. Therefore, the portal nodes take influential powers…

近似稀疏固定效应高维动态面板模型的一致有效推断AI 解读

We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and exogenous regressors. Separate oracle inequalities are derived for the fixed effects. Next, we show how one can conduct uniformly valid inference on the parameters of the model and construct a uniformly valid estimator of the asymptotic covariance matrix which is robust to conditional…

随机权重矩阵下空间自回归模型的估计AI 解读

We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weight matrices. Allowing a general spatial linear process form for the disturbances that permits many common types of error specifications as well as potential ‘long memory’, we provide sufficient conditions for consistency and asymptotic normality of instrumental variables, ordinary least squares, and pseudo maximum likelihood estimates. The implications of popular weight matrix…

已实现波动率与已实现贝塔的局部高斯自助法AI 解读

This article introduces a local Gaussian bootstrap method useful for the estimation of the asymptotic distribution of high-frequency data-based statistics such as functions of realized multivariate volatility measures as well as their asymptotic variances. The new approach consists of dividing the original data into nonoverlapping blocks of M consecutive returns sampled at frequency h (where h −1 denotes the sample size) and then generating the bootstrap observations at each…

动态因子模型的动态设定检验AI 解读

Summary We derive computationally simple expressions for score tests of misspecification in parametric dynamic factor models using frequency domain techniques. We interpret those diagnostics as time domain moment tests which assess whether certain autocovariances of the smoothed latent variables match their theoretical values under the null of correct model specification. We also reinterpret reduced‐form residual tests as checking specific restrictions on structural…

NETS：时间序列网络估计AI 解读

Summary We model a large panel of time series as a vector autoregression where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to…

自然利率的测度：关于暂时冲击的说明AI 解读

Summary We present evidence that the natural rate of interest is buffeted by both permanent and transitory shocks. We establish this result by estimating a benchmark model with Bayesian methods and loose priors on the unobserved drivers of the natural rate. When subject to transitory shocks, the median estimate for the US economy is more procyclical, displays a less marked secular decline, and is therefore higher following the Great Recession than most estimates in the…

不同波动率状态下的不确定性AI 解读

Summary We propose a nonrecursive identification scheme for uncertainty shocks that exploits breaks in the volatility of macroeconomic variables and is novel in the literature on uncertainty. This approach allows us to simultaneously address two major questions in the empirical literature: Is uncertainty a cause or effect of decline in economic activity? Does the relationship between uncertainty and economic activity change across macroeconomic regimes? Results based on a…

非同步高频金融数据的结构化波动率矩阵估计AI 解读

Several large volatility matrix estimation procedures have been recently developed for factor-based Itô processes whose integrated volatility matrix consists of low-rank and sparse matrices. Their performance depends on the accuracy of input volatility matrix estimators. When estimating co-volatilities based on high-frequency data, one of the crucial challenges is non-synchronization for illiquid assets, which makes their co-volatility estimators inaccurate. In this paper,…

近似加权风险最小的无偏估计AI 解读

Consider a small-sample parametric estimation problem, such as the estimation of the coefficient in a Gaussian AR(1). We develop a numerical algorithm that determines an estimator that is nearly (mean or median) unbiased, and among all such estimators, comes close to minimizing a weighted average risk criterion. We also apply our generic approach to the median unbiased estimation of the degree of time variation in a Gaussian local-level model, and to a quantile unbiased…

基于留主体交叉验证的向量自回归模型平均AI 解读

The vector autoregressive (VAR) model is a useful tool for economic evaluation and prediction. This paper develops a leave-subject-out cross-validation model averaging (LsoMA) method to average predictions from VAR models. The approximate unbiasedness of LsoMA and its asymptotic optimality in terms of obtaining the lowest possible quadratic errors are established. The rate of the LsoMA based weights converging to the optimal weights minimizing the expected quadratic errors…

利用信息丰富观测进行动态资产定价模型的贝叶斯估计AI 解读

In dynamic asset pricing models, when the model structure becomes complex and derivatives data are introduced in estimation, traditional MCMC methods converge slowly, are difficult to design efficient proposals for parameters, and have large computational cost. We propose a two-stage sequential Monte Carlo sampler based on common random numbers and a smooth particle filter. This method is robust to potential model misspecification and can deliver almost full-likelihood-based…

从标普500与VIX市场推断波动率动态和风险溢价AI 解读

We estimate a flexible affine model using an unbalanced panel containing S&P 500 and VIX index returns and option prices and analyze the contribution of VIX options to the model’s in- and out-of-sample performance. We find that they contain valuable information on the risk-neutral conditional distributions of volatility at different time horizons, which is not spanned by the S&P 500 market. This information allows enhanced estimation of the variance risk premium. We gain new…

直接与迭代多步条件预测的实证比较AI 解读

Summary When constructing unconditional point forecasts, both direct and iterated multistep (DMS and IMS) approaches are common. However, in the context of producing conditional forecasts, IMS approaches based on vector autoregressions are far more common than simpler DMS models. This is despite the fact that there are theoretical reasons to believe that DMS models are more robust to misspecification than are IMS models. In the context of unconditional forecasts, Marcellino…

因子增强 VAR 中脉冲响应函数的自助法推断AI 解读

Summary In this study, we consider residual‐based bootstrap methods to construct the confidence interval for structural impulse response functions in factor‐augmented vector autoregressions. In particular, we compare the bootstrap with factor estimation (Procedure A) with the bootstrap without factor estimation (Procedure B). Both procedures are asymptotically valid under the condition , where N and T are the cross‐sectional dimension and the time dimension, respectively.…

稳态建模与宏观经济预测质量AI 解读

Summary Vector autoregressions (VARs) with informative steady‐state priors are standard forecasting tools in empirical macroeconomics. This study proposes (i) an adaptive hierarchical normal‐gamma prior on steady states, (ii) a time‐varying steady‐state specification which accounts for structural breaks in the unconditional mean, and (iii) a generalization of steady‐state VARs with fat‐tailed and heteroskedastic error terms. Empirical analysis, based on a real‐time dataset…

多元与非欧氏数据的渐近分布无关变点检测AI 解读

We consider the testing and estimation of change-points, locations where the distribution abruptly changes, in a sequence of multivariate or non-Euclidean observations. We study a nonparametric framework that utilizes similarity information among observations, which can be applied to various data types as long as an informative similarity measure on the sample space can be defined. The existing approach along this line has low power and/or biased estimates for change-points…

通过多尺度刀切法高效估计积分波动率泛函AI 解读

We propose semiparametrically efficient estimators for general integrated volatility functionals of multivariate semimartingale processes. A plug-in method that uses nonparametric estimates of spot volatilities is known to induce high-order biases that need to be corrected to obey a central limit theorem. Such bias terms arise from boundary effects, the diffusive and jump movements of stochastic volatility and the sampling error from the nonparametric spot volatility…

含未观测随机动态效应的非线性时间序列与面板模型的拉普拉斯变换识别AI 解读

We consider nonlinear parametric and semi-parametric models for time series and panel data including unobserved dynamic effects. These regression models have an affine specification with respect to lagged endogenous variables and unobserved dynamic effects. We derive conditional moment restrictions based on suitable Laplace transforms. We show how to deploy these nonlinear moment restrictions to identify the parameters of the affine regression model, and the parametric or…