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非参数回归的一致显著性检验AI 解读

This article presents a framework for individual and joint tests of significance employing nonparametric estimation procedures. The proposed test is based on nonparametric estimates of partial derivatives, is robust to functional misspecification for general classes of models, and employs nested pivotal bootstrapping procedures. Two simulations and one application are considered to examine size and power relative to misspecified parametric models and to test for the linear…

方差变点的检验与定位及其股票价格应用AI 解读

This article explores testing and locating multiple variance changepoints in a sequence of independent Gaussian random variables (assuming known and common mean). This type of problem is very common in applied economics and finance. A binary procedure combined with the Schwarz information criterion (SIC) is used to search all of the possible variance changepoints existing in the sequence. The simulated power of the proposed procedure is compared to that of the CUSUM…

计量经济学、金融及其他领域中的时间序列模型AI 解读

Statistical Aspects of ARCH and Scholastic Volatility Likelihood-Based Inference for Cointegration of Some Non-Stationary Time Series Forecasting in Macroeconomics Longitudinal Panel Data: An Overview of Current Methodology

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We document extreme bias and dispersion in the small-sample distributions of four standard regression-based tests of the expectations hypothesis of the term structure of interest rates. The biases arise because of the extreme persistence in short interest rates. We derive approximate analytic expressions for the biases under a simple first-order autoregressive data generating process for the short rate. We then conduct Monte Carlo experiments based on a bias-adjusted…

Cox–Ingersoll–Ross 模型的参数估计AI 解读

The Cox-Ingersoll-Ross model is a diffusion process suitable for modeling the term structure of interest rates. In this paper, we consider estimation of the parameters of this process from observations at equidistant time points. We study two estimators based on conditional least squares as well as a one-step improvement of these, two weighted conditional least-squares estimators, and the maximum likelihood estimator. Asymptotic properties of the various estimators are…

条件异方差模型中准极大似然估计量的渐近偏差AI 解读

For conditional heteroskedasticity models, the authors study the identification condition that is required for consistency of a non-Gaussian quasi-maximum-likelihood estimator. They show that, if the conditional mean is zero or if a symmetry condition is satisfied, then the identification condition is satisfied. Without symmetry, an additional parameter, for the location of the innovation density, must be added for identification. For the conditional variance parameters of a…

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一种多元条件异方差模型AI 解读

Tsay (1987) developed the conditional heteroscedastic autoregressive moving-average model, which includes the conditional heteroscedastic autoregressive and random coefficient autoregressive models as special cases. This paper establishes the multivariate conditional heteroscedastic autoregressive moving-average model, and considers its theoretical properties and applications. Maximum likelihood estimation of the model is discussed in detail. A representation of the…

GARCH(l,1) 模型中的渐近展开AI 解读

We develop order T −1 asymptotic expansions for the quasi-maximum likelihood estimator (QMLE) and a two-step approximate QMLE in the GARCH(l,l) model. We calculate the approximate mean and skewness and, hence, the Edgeworth-B distribution function. We suggest several methods of bias reduction based on these approximations.

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区域股票市场的共同可预测成分AI 解读

This paper employs recently developed multivariate methods to study the predictability of international stock market returns. The authors find evidence of significant common predictable components within the Pacific, the European, and the North American stock markets using region-specific instrumental variables. The degree of predictability of these common movements, however, varies across regional markets and across subperiods. Results indicate that only North American…

GARCH 模型中条件波动率的脉冲响应函数AI 解读

This paper defines and analyzes the impulse response function for conditional volatility in GARCH models. It first derives the function from a vector autoregressive and moving average representation of the GARCH models and obtains the corresponding standard errors from the first-order derivatives of the function and the covariance matrix of the estimated parameters. The paper then employs a Monte Carlo study to assess the finite sample properties of the standard errors and…

通过尾部厚度估计稳定指数α：一项批评AI 解读

A generalized Pareto or simple Pareto tail-index estimate above 2 has frequently been cited as evidence against infinite-variance stable distributions. It is demonstrated that this inference is invalid; tail index estimates greater than 2 are to be expected for stable distributions with α as low as 1.65. The nonregular distribution of the likelihood ratio statistic for a null of normality and an alternative of symmetric stability is tabulated by Monte Carlo methods and…

GARCH过程中的马尔可夫转换与股市波动率均值回归AI 解读

This article introduces four models of conditional heteroscedasticity that contain Markov-switching parameters to examine their multiperiod stock-market volatility forecasts as predictions of options-implied volatilities. The volatility model that best predicts the behavior of the options-implied volatilities allows the Student-t degrees-of-freedom parameter to switch such that the conditional variance and kurtosis are subject to discrete shifts. The half-life of the most…

符号切换与波动切换 ARCH 模型：理论及国际股票市场应用AI 解读

This paper develops two conditionally heteroscedastic models which allow an asymmetric reaction of the conditional volatility to the arrival of news. Such a reaction is induced by both the sign of past shocks and the size of past unexpected volatility. The proposed models are shown to converge in distribution to absolutely continuous Ito diffusion processes, as happens for other heteroscedastic formulations. One of the schemes developed in the paper--the Volatility-switching…

ARCH 模型的条件分位数估计与推断AI 解读

Quantile regression methods are suggested for a class of ARCH models. Because conditional quantiles are readily interpretable in semiparametric ARCH models and are inherendy easier to estimate robustly than population moments, they offer some advantages over more familiar methods based on Gaussian likelihoods. Related inference methods, including the construction of prediction intervals, are also briefly discussed.

存在摩擦的经济中的资产定价AI 解读

This paper examines how proportional transaction costs, short-sale constraints, and margin requirements affect inferences based on asset return data about intertemporal marganil rates of substitution (IMRSs). Small transaction costs greatly reduce the required variability of IMRSs, suggesting that the low variability of many parametric, aggregate consumption based IMRSs need not be inconsistent with asset return data. Euler inequalities for a transaction cost economy with…

彩池博彩市场中预期假说的半参数（分布无关）检验AI 解读

The expectations hypothesis maintains that a current forward or futures price should be an unbiased forecast of the expected future price. This paper tests the expectations hypothesis in the parimutuel gambling market for greyhound racing parametric and semiparametric estimators. Parimutuel gambling markets are similar to speculative asset markets in many regards. Conventional maximum likelihood tests of asset pricing require a priori specification of the statistical…

资产收益非对称随机波动率模型的估计AI 解读

A stochastic volatility model may be estimated by a quasi-maximum likelihood procedure by transforming to a linear state space form. The method is extended to handle correlation between the two disturbances in the model and applied to data on stock returns.

随机贴现因子的贝叶斯估计AI 解读

This article provides a Bayesian method of estimating the marginal posterior distributions for stochastic discount factors associated with observed asset returns. These estimates can be used to provide measures of fit for asset pricing models and to identify broad features of the characteristics that should be explained. These measures of fit can be used to supplement model evaluation exercises based on L. P. Hansen-R. Jagannathan bounds.

无需预先确定平稳化方式的股票价格建模：计算更正AI 解读

In “Modeling Stock Prices without Knowing How to Induce Stationarity” (1994, Econometric Theory 10, 701–719), we used posterior-odds calculations to evaluate restrictions imposed by a present-value model of stock prices across the equations of a VAR representation of stock prices and dividends. The results we reported are tainted by the omission of two factors: the Jacobians induced by the mapping of our priors over VAR parameters β into the restricted sample spaces relevant…