论文检索

融合低频与高频金融数据的统一离散时间和连续时间模型AI 解读

This paper introduces a unified model, which can accommodate both continuous-time Itô processes used to model high-frequency stock prices and GARCH processes employed to model low-frequency stock prices, by embedding a discrete-time GARCH volatility in its continuous-time instantaneous volatility. This model is called a unified GARCH-Itô model. We adopt realized volatility estimators based on high-frequency financial data and the quasi-likelihood function for the…

基于k近邻估计的一致模型设定检验AI 解读

We propose a simple consistent test for a parametric regression functional form based on k-nearest-neighbor (k-nn) method. We derive the null distribution of the test statistic and show that the test achieves the minimax rate optimality against smooth alternatives. A wild bootstrap method is used to better approximate the null distribution of the test statistic. We also propose a k-nn statistic which tests for omitted variables nonparametrically. Simulations and an empirical…

强杠杆效应假说的非参数检验AI 解读

The so-called leverage hypothesis is that negative shocks to prices/returns affect volatility more than equal positive shocks. Whether this is attributable to changing financial leverage is still subject to dispute but the terminology is in wide use. There are many tests of the leverage hypothesis using discrete time data. These typically involve fitting of a general parametric or semiparametric model to conditional volatility and then testing the implied restrictions on…

允许与禁止卖空时最大夏普比率的大样本分布AI 解读

In the Markowitz paradigm the portfolio having maximum Sharpe ratio is optimal. Previously the large sample distribution of this statistic has been calculated when short sales are allowed and sample returns and covariance matrix are asymptotically normally distributed. This paper considers the more complex situation when short sales are not allowed, and provides conditions under which the maximum Sharpe ratio is asymptotically normal. This is not always the case, as we show,…

利用混频宏观与金融数据估计动态均衡模型AI 解读

We provide a framework for inference in dynamic equilibrium models including financial market data at daily frequency, along with macro series at standard lower frequency. Our formulation of the macro-finance model in continuous time conveniently accounts for the difference in observation frequency. We suggest the use of martingale estimating functions (MEF) to infer the structural parameters of the model directly through a nonlinear scheme. This method is compared to…

函数型时间序列的白噪声检验与模型诊断AI 解读

This paper is concerned with white noise testing and model diagnostic checking for stationary functional time series. To test for the functional white noise null hypothesis, we propose a Cramér–von Mises type test based on the functional periodogram introduced by Panaretos and Tavakoli (2013a). Using the Hilbert space approach, we derive the asymptotic distribution of the test statistic under suitable assumptions. A new block bootstrap procedure is introduced to obtain the…

矩不等式模型的简便检验及其英式拍卖应用AI 解读

Testable predictions of many economic models involve inequality comparisons between transformations of nonparametric functionals. We introduce an econometric test for these types of restrictions based on one-sided Lp-statistics that adapt asymptotically to the contact sets without having to directly estimate them. Monte Carlo experiments show that our test is less conservative than procedures based on least-favorable configurations and has power comparable to other…

多元 GARCH 中协方差结构突变的建模AI 解读

This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH models through a stochastic component that allows for changes in the conditional variances, covariances and implied correlation coefficients. Different breakdown periods will have different impacts on the conditional covariance matrix and are estimated from the data. We propose an efficient Bayesian posterior sampling procedure and show how to compute the marginal…

Harris 常返马尔可夫过程的局部复合分位数回归平滑AI 解读

In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general…

条件矩不等式的多尺度自适应推断AI 解读

This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula, and to prove the asymptotic validity of a modified bootstrap procedure. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. The test detects local…

内生删失面板数据模型的识别AI 解读

We study inference on parameters in linear panel data models when outcomes are censored. We allow the censoring to depend on both observable and unobservable variables in arbitrary ways. Generally, these models are set identified and the main contribution of this paper is to derive and characterize the identified sets under general conditions. Our main characterization theorems show that every parameter in the sharp set–and only those parameters–can generate the observed…

MIDAS 回归中高频波动率度量的使用AI 解读

Many empirical studies link mixed data frequency variables such as low frequency macroeconomic or financial variables with high frequency financial indicators’ volatilities, especially within a predictive regression model context. The objective of this paper is threefold: First, we relate the standard Least Squares (LS) regression model with high frequency volatility predictors, with the corresponding Mixed Data Sampling Nonlinear LS (MIDAS-NLS) regression model (Ghysels et…

宏观经济学与混频数据的现实AI 解读

Many time series are sampled at different frequencies. When we study co-movements between such series we usually analyze the joint process sampled at a common low frequency. This has consequences in terms of potentially mis-specifying the co-movements and hence the analysis of impulse response functions—a commonly used tool for economic policy analysis. We introduce a class of mixed frequency VAR models that allows us to measure the impact of high frequency data on low…

用 MIDAS 方法建模一阶与二阶矩动态AI 解读

We propose a new approach to predictive density modeling that allows for MIDAS effects in both the first and second moments of the outcome. Specifically, our modeling approach allows for MIDAS stochastic volatility dynamics, generalizing a large literature focusing on MIDAS effects in the conditional mean, and allows the models to be estimated by means of standard Gibbs sampling methods. When applied to monthly time series on growth in industrial production and inflation, we…

货币、财政与石油冲击：混频结构 FAVAR 的证据AI 解读

Large scale factor models have been often adopted both for forecasting and to identify structural shocks and their transmission mechanism. Mixed frequency factor models have been also used in a reduced form context, but not for structural applications, and in this paper we close this gap. First, we adapt a simple technique developed in a small scale mixed frequency VAR and factor context to the large scale case, and compare the resulting model with existing alternatives.…

混频动态因子分析与预测的加权极大似然方法AI 解读

For the purpose of forecasting key macroeconomic or financial variables from a panel of time series variables, we adopt the dynamic factor model and propose a weighted likelihood-based method for parameter estimation. The loglikelihood function is split into two parts that are weighted differently. The first part is associated with the key variables while the second part is associated with the related variables which may contribute to the forecasting of key variables. We…

利用混频数据的高维 Copula 资产收益分布模型AI 解读

This paper proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence, and a new class of copulas designed to capture nonlinear dependence among the resulting uncorrelated, low frequency, residuals. Estimation of the new class of copulas is conducted…

大型混频 VAR 中的格兰杰因果检验AI 解读

We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large, implying parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose several tests based on reduced rank restrictions, including bootstrap versions thereof to account for factor estimation uncertainty and improve the finite sample properties of the tests, and a Bayesian VAR extended to mixed…

混频向量自回归的计算高效方法AI 解读

A linear transformation method is proposed to handle the vector autoregression with mixed frequency time series data. Temporally aggregated observations impose linear constraints on the distribution of latent variables, which are converted such that each observation replaces a latent variable. Full-sample transformation yields a closed-form simulation smoother, while partial-sample transformation leads to a computationally efficient sampler suitable for parallel computing.

混频数据下连续时间模型的估计AI 解读

This paper derives exact representations for discrete time mixed frequency data generated by an underlying multivariate continuous time model. Allowance is made for different combinations of stock and flow variables as well as deterministic trends, and the variables themselves may be stationary or nonstationary (and possibly cointegrated). The resulting discrete time representations allow for the information contained in high frequency data to be utilised alongside the low…

单频或混频数据下 VARMA 模型的扩展 Yule–Walker 识别AI 解读

Chen and Zadrozny (1998) developed the linear extended Yule–Walker (XYW) method for determining the parameters of a vector autoregressive (VAR) model with available covariances of mixed-frequency observations on the variables of the model. If the parameters are determined uniquely for available population covariances, then, the VAR model is identified. The present paper extends the original XYW method to an extended XYW method for determining all ARMA parameters of a vector…

处理效应半参数估计中的模型平均AI 解读

Choosing the covariates and functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over candidate specifications to resolve the specification uncertainty in the propensity score weighting estimation of the ATT. The proposed procedures minimize the estimated MSE of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision…

交叉分位相关图：分位依赖与方向预测性检验AI 解读

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross-quantilogram and the corresponding test statistic. The limiting distributions depend on nuisance parameters. To construct consistent confidence intervals we employ a stationary bootstrap procedure; we…

半参数 Copula 依赖模型的拟合优度检验AI 解读

This paper concerns goodness-of-fit tests for semiparametric copula models. Our contribution is two-fold: we first propose a new test constructed via the comparison between “in-sample” and “out-of-sample” pseudo-likelihoods. Under the null hypothesis that the copula model is correctly specified, we show that the proposed test statistic converges in probability to a constant equal to the dimension of the parameter space. We establish the asymptotic normality and investigate…