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TESTING FOR STRUCTURAL CHANGE IN THE PRESENCE OF AUXILIARY MODELS

存在辅助模型时的结构变化检验

Several estimation procedures such as the efficient method of moments (EMM) of Gallant and Tauchen (1996, Econometric Theory 12, 657–681) and indirect inference procedure of Gouriéroux, Monfort, and Renault (1993, Journal of Applied Econometrics 8, S85–S118) involve two models, an auxiliary one and a model of interest. The role played by both models poses challenges and provides new opportunities for hypothesis testing beyond the usual Wald-, Lagrange multiplier–, and…

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Detecting multiple breaks in financial market volatility dynamics

金融市场波动率动态中的多重结构突变检测

Abstract The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data‐driven volatility estimators using high‐frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new tests for detecting breaks in the…

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Rolling-Sample Volatility Estimators

滚动样本波动率估计量

We propose extensions of the continuous record asymptotic analysis for rolling sample variance estimators developed for estimating the quadratic variation of asset returns, referred to as integrated or realized volatility. We treat integrated volatility as a continuous time stochastic process sampled at high frequencies and suggest rolling sample estimators which share many features with spot volatility estimators. We discuss asymptotically efficient window lengths and…

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Some Econometric Recipes for High-Frequency Data Cooking

高频数据处理的若干计量经济学方法

According to Ragnar Frisch (1933), econometrics involves the mutual penetration of quantitative economic theory and statistical observation. Today, econometric research rarely has all the ingredients spelled out in Frisch's definition. Most econometric models are either very structural, contrived, and unfortunately typically rejected or else they are statistical summaries of stylized data features and unfortunately difficult to link unambiguously to a single theoretical…

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American options with stochastic dividends and volatility: A nonparametric investigation

随机股息与波动率下的美式期权:非参数研究

In this paper, we consider American option contracts when the underlying asset has stochastic dividends and stochastic volatility. We provide a full discussion of the theoretical foundations of American option valuation and exercise boundaries. We show how they depend on the various sources of uncertainty which drive dividend rates and volatility, and derive equilibrium asset prices, derivative prices and optimal exercise boundaries in a general equilibrium model. The…

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Bayesian inference for periodic regime-switching models

周期性状态转换模型的贝叶斯推断

We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical…

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Seasonal Adjustment and Other Data Transformations

季节调整与其他数据变换

In this paper, it is shown that the case for using optimal signal extraction filters is not all that convincing once it is recognized that seasonal adjustment is typically not the only transformation applied to data. Seasonal adjustment is viewed as any general linear filter. All other data transformations are also assumed to be linear. While optimal filters always dominate uniform filters, their dominance critically depends on performing seasonal adjustment and the other…

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On Periodic Structures and Testing for Seasonal Unit Roots

周期结构与季节单位根检验

The standard testing procedures for seasonal unit roots developed so far have been based mainly on time-invariant autoregressive integrated moving average (ARIMA) processes with AR polynomials involving seasonal differencing. One attractive alternative is to use periodic ARMA models. Convenient procedures are presented for testing for the presence of unit roots at the zero and seasonal frequencies in periodic time series. The limiting distributions are derived and tabulated…

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Dynamic Regression and Filtered Data Series: A Laplace Approximation to the Effects of Filtering in Small Samples

动态回归与滤波数据:小样本滤波效应的拉普拉斯近似

It is common for an applied researcher to use filtered data, like seasonally adjusted series, for instance, to estimate the parameters of a dynamic regression model. In this paper, we study the effect of (linear) filters on the distribution of parameters of a dynamic regression model with a lagged dependent variable and a set of exogenous regressors. So far, only asymptotic results are available. Our main interest is to investigate the effect of filtering on the small sample…

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Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?

季节调整是线性还是非线性的数据滤波过程?

The authors investigate whether seasonal adjustment procedures are linear data transformations. This question was addressed by A. H. Young (1968) and is important for the estimation of regression models with seasonally adjustment data. The authors focus on the X-11 program and rely on simulation evidence, involving linear unobserved component autorgressive integrated moving average models. They define and test a set of properties for the adequacy of a linear approximation to…

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Periodic Autoregressive Conditional Heteroscedasticity

周期性自回归条件异方差

Most high frequency asset returns exhibit seasonal volatility patterns. This paper proposes a new class of periodic ARCH, or P-ARCH, models explicitly designed to capture the repetitive variation in the second order moments. The importance of the informational loss associated with the implicit relation between P-GARCH structures and the corresponding time-invariant seasonal weak GARCH processes are quantified through the use of Monte Carlo simulation methods. Two empirical…

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