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具有共同因子与空间相关的大型面板AI 解读

This paper considers methods for estimating the slope coefficients in large panel data models that are robust to the presence of various forms of error cross-section dependence. It introduces a general framework where error cross-section dependence may arise because of unobserved common effects and/or error spill-over effects due to spatial or other forms of local dependencies. Initially, this paper focuses on a panel regression model where the idiosyncratic errors are…

非参数部分前沿建模中的稳健性与推断AI 解读

A major aim in recent nonparametric frontier modeling is to estimate a partial frontier well inside the sample of production units but near the optimal boundary. Two concepts of partial boundaries of the production set have been proposed: an expected maximum output frontier of order m=1,2,... and a conditional quantile-type frontier of order [alpha][set membership, variant]]0,1]. In this paper, we answer the important question of how the two families are linked. For each m,…

三角系统中更紧的识别界AI 解读

We study a nonparametric triangular system with (potentially discrete) endogenous regressors and nonseparable errors. Like in other work in this area, the parameter of interest is the structural function evaluated at particular values. We impose a global exclusion and exogeneity condition, in contrast to Chesher (2005), but develop a rank condition which is weaker than Chesher's. The alternative rank condition can be satisfied for binary endogenous regressors, and it often…

恢复连续线性泛函的工具变量方法AI 解读

This paper develops methods for estimating continuous linear functionals in nonparametric instrumental variable problems. Examples of such functionals include consumer surplus and weighted average derivatives. The estimation procedure is robust to a setting where the underlying model is not identified but the linear functional is. In order to attain such robustness, it is necessary to employ a partially identified nuisance parameter. We address this problem by consistently…

经验似然区块自助法AI 解读

Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the problem, including Hall and Horowitz (1996) and Inoue and Shintani (2006). We propose an empirical likelihood block bootstrap procedure to improve inference where models are characterized…

财富积累与成功的影响因素AI 解读

We use detailed income, balance sheet, and cash flow statements constructed for households in a long monthly panel in an emerging market economy, and some recent contributions in economic theory, to document and better understand the factors underlying success in achieving upward mobility in the distribution of net worth. Wealth inequality is decreasing over time, and many households work their way out of poverty and lower wealth over the seven year period. The accounts…

空间异方差与自相关一致的协方差矩阵估计AI 解读

This paper considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimator introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared error (MSE). Based on the asymptotic truncated MSE criterion, we derive the optimal…

具有非平稳多因子误差结构的面板AI 解读

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently, work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference in the case of stationary panel regressions with a multifactor error structure. This paper extends this work and examines the important case where the unobservable common factors follow unit root processes. The extension to I(1)…

多元当期阈值自回归模型AI 解读

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex-ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific noise covariance matrix, the model can…

单向误差分量模型中异方差的稳健检验AI 解读

This paper constructs tests for heteroskedasticity in one-way error components models, in line with Baltagi et al. [Baltagi, B.H., Bresson, G., Pirotte, A., 2006. Joint LM test for homoskedasticity in a one-way error component model. Journal of Econometrics 134, 401-417]. Our tests have two additional robustness properties. First, standard tests for heteroskedasticity in the individual component are shown to be negatively affected by heteroskedasticity in the remainder…

Hausman 检验与弱工具变量AI 解读

We consider the following problem. There is a structural equation of interest that contains an explanatory variable that theory predicts is endogenous. There are one or more instrumental variables that credibly are exogenous with regard to this structural equation, but which have limited explanatory power for the endogenous variable. Further, there is one or more potentially 'strong' instruments, which has much more explanatory power but which may not be exogenous. Hausman…

方差动态：期权与高频收益的联合证据AI 解读

This paper analyzes the S&P 500 index return variance dynamics and the variance risk premium by combining information in variance swap rates constructed from options and quadratic variation estimators constructed from tick data on S&P 500 index futures. Estimation shows that the index return variance jumps. The jump arrival rate is not constant over time, but is proportional to the variance rate level. The variance jumps are not rare events but arrive frequently. Estimation…

使用不完美波动率代理变量比较波动率预测AI 解读

The use of a conditionally unbiased, but imperfect, volatility proxy can lead to undesirable outcomes in standard methods for comparing conditional variance forecasts. We motivate our study with analytical results on the distortions caused by some widely used loss functions, when used with standard volatility proxies such as squared returns, the intra-daily range or realised volatility. We then derive necessary and sufficient conditions on the functional form of the loss…

已实现波动率预测与市场微观结构噪声AI 解读

We extend the analytical results for reduced form realized volatility based forecasting in ABM (2004) to allow for market microstructure frictions in the observed high-frequency returns. Our results build on the eigenfunction representation of the general stochastic volatility class of models developed byMeddahi (2001). In addition to traditional realized volatility measures and the role of the underlying sampling frequencies, we also explore the forecasting performance of…

已实现核估计量的子采样AI 解读

In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our analysis, looking at the class of subsampled realised kernels and we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for…

市场微观结构噪声、积分方差估计与渐近近似的准确性AI 解读

A growing literature has been advocating consistent kernel estimation of integrated variance in the presence of financial market microstructure noise. We find that, for realistic sample sizes encountered in practice, the asymptotic results derived for the proposed estimators may provide unsatisfactory representations of their finite sample properties. In addition, the existing asymptotic results might not offer sufficient guidance for practical implementations. We show how…

高频收益、跳跃与正态混合假说AI 解读

Previous empirical studies find both evidence of jumps in asset prices and that returns standardized by 'realized volatility' are approximately standard normal. These findings appear to be contradictory. Using a sample of high-frequency returns for 20 heavily traded US stocks, we show how microstructure noise distorts the standard deviation and kurtosis of returns normalized using realized variance. When returns are standardized using a recently developed realized kernel…

金融市场的已实现跳跃与信用利差预测AI 解读

This paper extends the jump detection method based on bipower variation to identify realized jumps on financial markets and to estimate parametrically the jump intensity, mean, and variance. Finite sample evidence suggests that the jump parameters can be accurately estimated and that the statistical inferences are reliable under the assumption that jumps are rare and large. Applications to equity market, treasury bond, and exchange rate data reveal important differences in…

基于积分波动率矩估计真实世界与风险中性分布AI 解读

In this paper, we present an estimation procedure which uses both option prices and high-frequency spot price feeds to estimate jointly the objective and risk-neutral parameters of stochastic volatility models. The procedure is based on a method of moments that uses analytical expressions for the moments of the integrated volatility and series expansions of option prices and implied volatilities. This results in an easily implementable and rapid estimation technique. An…

随机时间下收益波动率度量中的因果效应AI 解读

We provide a structural approach to identify instantaneous causality effects between durations and stock price volatility. So far, in the literature, instantaneous causality effects have either been excluded or cannot be identified separately from Granger type causality effects. By giving explicit moment conditions for observed returns over (random) duration intervals, we are able to identify an instantaneous causality effect. The documented causality effect has significant…

波动率预测与微观结构噪声AI 解读

It is common practice to use the sum of frequently sampled squared returns to estimate volatility, yielding the so-called realized volatility. Unfortunately, returns are contaminated by market microstructure noise. Several noise-corrected realized volatility measures have been proposed. We assess to what extent correction for microstructure noise improves forecasting future volatility using a MIxed DAta Sampling (MIDAS) regression framework. We study the population…

利用期权隐含与已实现波动率动态估计风险溢价和投资者风险厌恶AI 解读

This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment confirms that the procedure works well in practice. Implementing the procedure with actual S&P500 option-implied volatilities and high-frequency five-minute-based…

已实现波动率及相关估计量的 Edgeworth 展开AI 解读

This paper shows that the asymptotic normal approximation is often insufficiently accurate for volatility estimators based on high frequency data. To remedy this, we derive Edgeworth expansions for such estimators. The expansions are developed in the framework of small-noise asymptotics. The results have application to Cornish-Fisher inversion and help setting intervals more accurately than those relying on normal distribution.

基于已实现变差度量的投机性价格波动率简约式建模框架AI 解读

Building on realized variance and bipower variation measures constructed from high-frequency financial prices, we propose a simple reduced form framework for effectively incorporating intraday data into the modeling of daily return volatility. We decompose the total daily return variability into the continuous sample path variance, the variation arising from discontinuous jumps that occur during the trading day, as well as the overnight return variance. Our empirical…