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非高斯结构向量自回归模型的设定检验AI 解读

We propose specification tests for independent component analysis and structural vector autoregressions that assess the cross-sectional independence of non-Gaussian shocks by comparing their joint cumulative distribution with the product of their marginals at both discrete and continuous grids of argument values, the latter yielding a consistent test. We explicitly consider the sampling variability from computing the shocks using consistent estimators. We study the finite…

结构向量自回归的离散正态混合伪极大似然估计AI 解读

Likelihood inference in structural vector autoregressions with independent non-Gaussian shocks leads to parametric identification and efficient estimation at the risk of inconsistencies under distributional misspecification. We prove that autoregressive coefficients and (scaled) impact multipliers remain consistent, but the drifts and shocks’ standard deviations are generally inconsistent. Nevertheless, we show consistency when the non-Gaussian log-likelihood uses a discrete…

Empirical asset pricing models with possibly unnecessary risk factors are increasingly common. Unfortunately, they can yield misleading statistical inferences. Unlike previous studies, we estimate the identified set of SDFs and risk prices compatible with a given model’s asset pricing restrictions. We also propose tests that detect problematic situations with economically meaningless SDFs unrelated to the test assets. Empirically, we estimate linear subspaces of SDFs…

均值与方差可预测性的新检验方法AI 解读

We propose parametric tests for serial correlation in levels and squares that exploit the non-normality of financial returns. Our tests are robust to distributional misspecification. Furthermore, our mean predictability tests can be robustified against time-varying volatility. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises assess their finite sample reliability. We apply our tests to quarterly returns on the five Fama–French…

非高斯极大似然估计量的模型设定检验AI 解读

We propose generalized DWH specification tests which simultaneously compare three or more likelihood‐based estimators in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Var s and multivariate regressions. We determine the rank of the differences between the estimators' asymptotic covariance matrices under correct specification, and take into…

高斯 Copula 是否合适？AI 解读

We derive computationally simple and intuitive expressions for score tests of Gaussian copulas against generalized hyperbolic alternatives, including symmetric and asymmetric Student t, and many other examples. We decompose our tests into third and fourth moment components, and obtain one-sided Likelihood Ratio analogs, whose standard asymptotic distribution we provide. Our Monte Carlo exercises confirm the reliable size of parametric bootstrap versions of our tests, and…

具有一致性的非高斯伪最大似然估计量AI 解读

We characterise the mean and variance parameters that distributionally misspecified maximum likelihood estimators can consistently estimate in location-scale models, and provide simple closed-form consistent estimators for the rest. Including means and a multivariate coverage make our procedures useful for Garch-M models and empirically relevant macro and finance applications involving Vars and multivariate regressions. We study the statistical properties of our proposed…

波动率相关交易所交易资产：计量经济学研究AI 解读

We develop a theoretical framework for covariance stationary but persistent positively valued processes which combines a semi-nonparametric expansion of the Gamma distribution with a component version of the multiplicative error model. Our conditional mean assumption allows for slow, possibly nonmonotonic mean-reversion, while our distribution assumption provides more flexibility than a traditional Laguerre expansion while preserving positivity of the density. We apply our…

多元动态模型中分布形状参数的序贯估计AI 解读

Sequential maximum likelihood and GMM estimators of distributional parameters obtained from the standardised innovations of multivariate conditionally heteroskedastic dynamic regression models evaluated at Gaussian PML estimators preserve the consistency of mean and variance parameters while allowing for realistic distributions. We assess their efficiency, and obtain moment conditions leading to sequential estimators as efficient as their joint ML counterparts. We also…

收益与随机贴现因子均值—方差前沿的张成检验：统一方法AI 解读

We propose new spanning tests that assess if the initial and additional assets share the economically meaningful cost and mean representing portfolios. We prove their asymptotic equivalence to existing tests under local alternatives. We also show that unlike two-step or iterated procedures, single-step methods such as continuously updated GMM yield numerically identical overidentifying restrictions test, so there is arguably a single spanning test. To prove these results, we…

均值—方差有效性检验的比较AI 解读

We analyse the asymptotic properties of mean-variance efficiency tests based on generalised methods of moments, and parametric and semiparametric likelihood procedures that assume elliptical innovations. We study the trade-off between efficiency and robustness, and prove that the parametric estimators provide asymptotically valid inferences when the conditional distribution of the innovations is elliptical but possibly misspecified and heteroskedastic. We compare the small…

多元正态位置尺度混合与均值—方差—偏度投资组合配置AI 解读

We show that the distribution of any portfolio whose components jointly follow a location-scale mixture of normals can be characterised solely by its mean, variance and skewness. Under this distributional assumption, we derive the mean-variance-skewness frontier in closed form, and show that it can be spanned by three funds. For practical purposes, we derive a standardised distribution, provide analytical expressions for the log-likelihood score and explain how to evaluate…

This paper provides a comprehensive survey of the econometrics of mean-variance efficiency tests. Starting with the classic F-test of Gibbons et al. (1989) and its generalized method of moments version, I analyse the effects of the number of assets and portfolio composition on test power. I then discuss asymptotically equivalent tests based on portfolio weights, and study the trade-offs between efficiency and robustness of using parametric and semi-parametric likelihood…

半非参数分布的参数性质及其在期权估值中的应用AI 解读

We derive the statistical properties of the semi-nonparametric (SNP) densities of Gallant and Nychka (1987). We show that these densities, which are always positive, are more flexible than truncated Gram–Charlier expansions with positivity restrictions. We use the SNP densities for financial derivatives valuation. We relate real and risk-neutral measures, obtain closed-form prices for European options, and analyze the semiparametric properties of our pricing model. In an…

大型条件异方差因子模型的间接估计：道琼斯30只股票应用AI 解读

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters. We also propose alternative indirect estimators for large-scale models, and explain how to apply our procedures to many other dynamic latent variable models. We analyse the small…

We develop generalized indirect estimation procedures that handle equality and inequality constraints on the auxiliary model parameters by extracting information from the relevant multipliers, and compare their asymptotic efficiency to maximum likelihood. We also show that, regardless of the validity of the restrictions, the asymptotic efficiency of such estimators can never decrease by explicitly considering the multipliers associated with additional equality constraints.…

潜在广义 ARCH 结构的似然估计AI 解读

GARCH models are commonly used as latent processes in econometrics, financial economics, and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a Bayesian solution in O(T) computational operations, where T denotes the sample size. We assess the performance of our…

含多元 t 创新的条件异方差动态回归模型：极大似然估计与推断AI 解读

Hessian, and information matrix of conditionally heteroscedastic dynamic regression models when the conditional distribution is multivariate t. We also derive one-sided and two-sided Lagrange multiplier tests for multivariate normality versus multivariate t based on the first two moments of the squared norm of the standardized innovations evaluated at the Gaussian pseudo-maximum likelihood estimators of the conditional mean and variance parameters. Finally, we illustrate our…

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条件异方差因子模型的EM算法AI 解读

This article discusses the application of the EM algorithm to factor models with dynamic heteroscedasticity in the common factors. It demonstrates that the EM algorithm reduces the computational burden so much that researchers can estimate such models with many series. Two empirical applications with 11 and 266 stock returns are presented, confirming that the EM algorithm yields significant speed gains and that it makes unnecessary the computation of good initial values.…

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波动率与各国股票市场之间的联系AI 解读

The authors attempt to account for the covariances between stock markets and to assess their integration. They estimate a factor model for sixteen national stock market returns whose volatility is induced by changing volatility in the factors. Unanticipated returns depend on innovations in economic variables and 'unobservable' factors. Assets risk premia are linear combinations of the factors risk premia. The authors find that idiosyncratic risk is priced and the 'price of…

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