具有结构突变的CAPM的阶段异方差和自相关性的调整LM检验

由于金融市场是动荡不定的,资产定价模型CAPM往往会出现结构突变,异方差,序列相关,因此需要对CAPM的随机误差进行齐性检验。对于具有单个结构突变点的CAPM,本文得到了检验阶段异方差和自相关性的调整LM检验统计量。Monte Carlo模拟的结果显示,该调整LM检验统计量具有比普通LM检验统计量更好的检验功效。最后,我们用一个具体的实例论证了方法的有效性。     

A study of several new and existing tests for heteroscedasticity in the general linear model1

Several optimum non-parametric tests for heteroscedasticity are proposed and studied along with the tests introduced in the literature in terms of power and robustness properties. It is found that all tests are reasonably robust to the Ordinary Least Squares (OLS) residual estimates, number and character of the regressors. Only a few are robust to both the distributional and independence assumptions about the errors. The power of tests can be improved with the OLS residual estimates, the increased sample size and the variability of the regressors. It can be substantially reduced if the observations are not normally distributed, and may increase or decrease if the errors are dependent. Each test is optimum to detect a specific form of heteroscedasticity and a serious power loss may occur if the underlying heteroscedasticity assumption in the data generation deviates from it.     

A point optimal test for heteroscedastic disturbances

A new test for additive heteroscedasticity in the disturbances of the linear regression model is proposed. Power functions of various forms of the new test are compared empirically with those of currently favoured tests for a range of heteroscedastic models. The results highlight the power advantage of a test which is MP1 at a central point in the alternative hypothesis parameter space. The main conclusion is that the recommended version of the new test is generally more powerful than existing tests against medium and severe heteroscedasticity, whereas the King and Szroeter tests perform better against weak heteroscedasticity.     

A heteroscedasticity-consistent covariance matrix estimator for time series regressions

This paper provides a covariance matrix estimator for the ordinary least squares coefficients of a linear time series model which is consistent even when the disturbances are heteroscedastic. This estimator does not require a formal model of the heteroscedasticity. One can also obtain a direct test of heteroscedasticity, although Monte Carlo experiments show that it may have low power.     

非参数统计视角下的异方差检验设计及其应用

以数据生成过程为导向,探讨了异方差来源的基本类型;依据非参数统计的基本思想,设计了切实可行的Mood方差检验方法与平方秩检验方法,并针对异方差来源类型,分析了相应的检验思路。蒙特卡罗模拟表明,Mood方差检验方法在异方差检验方面具有很高的检验效力;平方秩检验方法在异常经济现象情形下检验效力较低,而在其他情形下检验效力很高。同时,进一步阐释了纠正异方差的基本逻辑。     

Measuring the effects of fiscal policy

Measuring the effects of discretionary fiscal policy is both difficult and controversial, as some explicit or implicit identifying assumptions need to be made to isolate exogenous and unanticipated changes in taxes and government spending. Studies based on structural vector autoregressions typically achieve identification by restricting the contemporaneous interaction of fiscal and non-fiscal variables in a rather arbitrary way. In this paper, we relax those restrictions and identify fiscal policy shocks by exploiting the conditional heteroscedasticity of the structural disturbances. We use this methodology to evaluate the macroeconomic effects of fiscal policy shocks in the U.S. before and after 1979. Our results show substantive differences in the economy׳s response to government spending and tax shocks across the two periods. Importantly, we find that increases in public spending are, in general, more effective than tax cuts in stimulating economic activity. A key contribution of this study is to provide a formal test of the identifying restrictions commonly used in the literature.     

Note: Heteroscedasticity in the market model: Some evidence from the athens stock exchange

Over the last three decades a substantial amount of empirical research has been undertaken to investigate the behaviour of major securities markets. While some work has been done with data from the markets of developing countries, considerable testing must be undertaken for the world's emerging securities market. The objective of this paper is to present some Greek evidence of the heteroscedasticity in the market model. The paper examined 43 companies quoted on the Athens Stock Exchange and found substantial evidence of heteroscedasticity.     

Simultaneous stochastic volatility transmission across American equity markets

Information flows across international financial markets typically occur within hours, making volatility spillovers appear contemporaneously in daily data. Such simultaneous transmission of variances is featured by the stochastic volatility model developed in this paper, in contrast to usually employed multivariate ARCH processes. The arising identification problem is solved by considering heteroscedasticity of the structural volatility innovations. Estimation takes place in an appropriately specified state space setup. In the empirical application, unidirectional volatility spillovers from the US stock market to three American countries are revealed. The impact is strongest for Canada, followed by Mexico and Brazil, which are subject to idiosyncratic crisis effects.     

金融资产配置、金融杠杆与现金流风险

金融资产会计安排是企业执行金融工具准则的重要环节,具有显著的经济后果。基于实体企业金融化现象,研究金融资产配置与现金流风险关系,分析金融杠杆的调节功能,探讨非效率资本配置的传导作用,研究发现：金融资产配置与现金流风险之间存在U型关系;金融杠杆能够调节金融资产配置与现金流风险的关系,使关系曲线拐点右移与扁平化。区分金融资产配置类型后发现：交易类金融资产与现金流风险呈U型关系;委托贷款等新兴金融资产负向影响现金流风险;投资性房地产和长期金融股权投资未显著影响现金流风险。考虑企业生命周期后发现,成长期与衰退期企业金融资产配置与现金流风险呈U型关系,成熟期企业金融资产配置负向影响现金流风险;按照产权性质分组检验发现,金融资产配置与现金流风险的关系以及金融杠杆的调节效应在非国有企业中更显著;机制检验发现,非效率资本配置在金融资产配置影响现金流风险的过程中发挥中介作用。     

HETEROSCEDASTICITY OR PRODUCTION RISK? A SYNTHETIC VIEW

Two veins of literature, namely, production risk literature and stochastic frontier analysis, are examined. Both fields are concerned of output variation; the former due to exogenous shocks, the latter due inefficiency. By covering the literature from both the fields, this review suggests that the concept of heteroscedasticity can be utilized to build a synthesis between these mainly separate branches of literature. However, the synthetic approach brings a challenge how to differentiate between different sources of output variation. This challenge is identified as the main obstacle to meaningfully combine the two approaches.     

A semiparametric model for binary response and continuous outcomes under index heteroscedasticity

This paper formulates a likelihood‐based estimator for a double‐index, semiparametric binary response equation. A novel feature of this estimator is that it is based on density estimation under local smoothing. While the proofs differ from those based on alternative density estimators, the finite sample performance of the estimator is significantly improved. As binary responses often appear as endogenous regressors in continuous outcome equations, we also develop an optimal instrumental variables estimator in this context. For this purpose, we specialize the double‐index model for binary response to one with heteroscedasticity that depends on an index different from that underlying the ‘mean response’. We show that such (multiplicative) heteroscedasticity, whose form is not parametrically specified, effectively induces exclusion restrictions on the outcomes equation. The estimator developed exploits such identifying information. We provide simulation evidence on the favorable performance of the estimators and illustrate their use through an empirical application on the determinants, and affect, of attendance at a government‐financed school. Copyright © 2009 John Wiley & Sons, Ltd.     

投资者情绪对封闭式基金折价的影响研究述评

封闭式基金折价问题由来已久,本文对国内外文献进行梳理,从基本面因素、情绪因素等角度分别对封闭式基金折价问题进行剖析,本文的亮点在于追根溯源从心理学角度寻找投资者情绪的特征,从心理学文献中寻找折价的原因,并根据国内外文献,提出封闭式基金折价问题研究的切入点。     

Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances

Abstract

This paper develops a unified framework for fixed effects (FE) and random effects (RE) estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroscedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define both an RE and an FE spatial generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators and derive their joint asymptotic distribution, which is robust to heteroscedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman test of the spatial random against the spatial FE model.     

Surprise,surprise – Measuring firm-level investment innovations

Using the IFO Investment Survey for the German manufacturing sector, we construct a forty-year panel of firm-level investment innovations (surprises). We document the cross-sectional and time-series properties of this panel, and our main findings are: the cross-sectional dispersion of investment surprises is countercyclical, as is their average within-firm time series volatility, and both are highly correlated. This justifies, in part, strategies in the literature to use cross-sectional moments for the calibration of heteroscedasticity. At the same time, the cross-sectional dispersion of investment is procyclical, suggesting a nonsmooth capital adjustment friction at the micro level. There is substantial dispersion in within-firm volatility, a feature consistent with a recent literature on information frictions at the firm-level. Finally, the aforementioned second moments of investment innovations are Granger-caused by recessions, but not vice versa, rendering simple exogenous uncertainty shocks less plausible as drivers of business cycles.     

Ordinal-response GARCH models for transaction data: A forecasting exercise

We use numerous high-frequency transaction data sets to evaluate the forecasting performances of several dynamic ordinal-response time series models with generalized autoregressive conditional heteroscedasticity (GARCH). The specifications account for three components: leverage effects, in-mean effects and moving average error terms. We estimate the model parameters by developing Markov chain Monte Carlo algorithms. Our empirical analysis shows that the proposed ordinal-response GARCH models achieve better point and density forecasts than standard benchmarks.     

Optimal Control of Heteroscedastic Macroeconomic Models

This paper analyses the implications of heteroscedasticity for optimal macroeconomic policy and welfare. We find that changes in the variance structure driven by exogenous processes like generalized autoregressive conditional heteroscedasticity (GARCH) affect welfare but not the optimal feedback rule. However, changes in the variance structure driven by state‐dependent processes affect both. We also derive certainty‐equivalent transformations of state‐dependent volatility models that allow standard quadratic dynamic programming algorithms to be employed to study optimal policy. These results are illustrated numerically using a reduced‐form model of the US economy in which changes in volatility are driven by a GARCH process and the rate of inflation. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation in Two-Stage Models with Heteroscedasticity

A surprising number of important problems can be cast in the framework of estimating a mean and variance using data arising from a two-stage structure. The first stage is a random sampling of "units" with some quantity of interest associated with the unit. The second stage produces an estimate of that quantity and usually, but not always, an estimated standard error, which may change considerably across units. Heteroscedasticity in the estimates over different units can arise for a number of reasons, including variation associated with the unit and changing sampling effort over units. This paper presents a broad discussion of the problem of making inferences for the population mean and variance associated with the unobserved true values at the first stage of sampling. A careful discussion of the causes of heteroscedasticity is given, followed by an examination of ways in which inferences can be carried out in a manner that is robust to the nature of the within unit heteroscedasticity. Among the conclusions are that under any type of heteroscedasticity, an unbiased estimate of the mean and the variance of the estimated mean can be obtained by using the estimates as if they were true unobserved values from the first stage. The issue of using the mean versus a weighted average which tries to account for the heteroscedasticity is also discussed. An unbiased estimate of the population variance is given and the variance of this estimate and its covariance with the estimated mean is provided under various types of heteroscedasticity. The two-stage setting arises in many contexts including the one-way random effects models with replication, meta-analysis, multi-stage sampling from finite populations and random coefficients models. We will motivate and illustrate the problem with data arising from these various contexts with the goal of providing a unified framework for addressing such problems.     

The conditional heteroscedasticity of the yen–dollar exchange rate

This paper examines the conditional heteroscedasticity of the yen–dollar exchange rate. A model is constructed by extending the asymmetric power autoregressive conditional heteroscedasticity model to a process that is fractionally integrated. It is found that, unlike the equity markets, the appreciation and depreciation shocks of the yen against the dollar have similar effects on future volatilities. Although the results reject both the stable and the integrated models, our analysis of the response coefficients of the past shocks and the application of the models to the estimation of the capital requirements for trading the currencies show that there are no substantial differences between the fractionally integrated models and the stable models. © 1998 John Wiley & Sons, Ltd.     

A univariate analysis of EMS exchange rates using a target zone model

The models in the literature on exchange-rate target zones imply a non-linear time series model for the exchange rate. We show how the parameters of such models can be estimated and develop Maximum Likelihood and Method of Simulated Moments estimators for the target zone model of Krugman (1991). The Maximum Likelihood estimator is based on a computationally attractive approximation to the exact predictive density of the continuous time model. Monte Carlo experiments are used to assess the properties of this estimator. In the empirical part we estimate the model with data on recent EMS exchange rates. We find that the Krugman (1991) target zone model is not able to explain the full observed kurtosis and conditional heteroscedasticity of the exchange-rate returns.     

基于分组的异方差检验和两阶段估计

本文提出了一种基于分组的异方差检验法，并给出了存在异方差时的两阶段估计。