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动态预测回归的统一检验AI 解读

Testing for predictability of asset returns has been a long history in economics and finance. Recently, based on a simple predictive regression, Kostakis, Magdalinos, and Stamatogiannis derived a Wald type test based on the context of the extended instrumental variable (IVX) methodology for testing predictability of stock returns, and Demetrescu showed that the local power of the standard IVX-based test could be improved for some range of alternative hypotheses and the…

超高维时变协方差模型的估计AI 解读

Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here, we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds. The theory of this new…

高频领先—滞后效应与跨资产联系：多资产滞后调整模型AI 解读

Motivated by the empirical evidence of high-frequency lead-lag effects and cross-asset linkages, we introduce a multi-asset price formation model which generalizes standard univariate microstructure models of lagged price adjustment. Econometric inference on such model provides: (i) a unified statistical test for the presence of lead-lag correlations in the latent price process and for the existence of a multi-asset price formation mechanism; (ii) separate estimation of…

多元跳跃扩散模型的有效估计与滤波AI 解读

This paper develops estimators of the transition density, filters, and parameters of multivariate jump–diffusion models. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. It is not necessary to diagonalize the volatility matrix. Our density and filter estimators converge at the canonical rate typically associated with exact Monte Carlo estimation. Our parameter estimators have the same asymptotic distribution as…

局部平稳模型的间接推断AI 解读

We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of locally stationary models, the resulting indirect inference estimator exhibits nonparametric rates of convergence. We validate our methodology with simulation studies in the confines of a locally stationary moving…

重尾模型中的因果发现AI 解读

Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are not well suited if the causal mechanisms only manifest themselves in extremes. This work aims to connect the two fields of causal inference and extreme value theory. We define the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables. In the population…

通过两阶段非参数惩罚样条求解欧拉方程AI 解读

This study proposes a novel estimation-based approach to solving asset pricing models for both stationary and time-varying observations. Our method is robust to misspecification errors while inheriting a closed-form solution. By representing the Euler equation into a well-posed integral equation of the second kind, we propose a penalized two-stage nonparametric estimation method and establish its optimal convergence under mild conditions. With the merit of penalized splines,…

理解动量与反转AI 解读

Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Using instrumented principal components analysis, we estimate latent factors with time-varying factor loadings that depend on observable firm characteristics. We show that factor loadings vary…

多元广义双曲随机向量的二次型AI 解读

Summary This article presents exact and approximate expressions for tail probabilities and partial moments of quadratic forms in multivariate generalized hyperbolic random vectors. The derivations involve a generalization of the classic inversion formula for distribution functions (Gil-Pelaez, 1951). Two numerical applications are considered: the distribution of the two-stage least squares estimator and the expected shortfall of a quadratic portfolio.

多期灾难风险下的实证资产定价：基于模拟的方法AI 解读

We propose a simulation-based strategy to estimate and empirically assess a class of asset pricing models that account for rare but severe consumption contractions that can extend over multiple periods. Our approach expands the scope of prevalent calibration studies and tackles the inherent sample selection problem associated with measuring the effect of rare disaster risk on asset prices. An analysis based on postwar U.S. and historical multi-country panel data yields…

统计因子模型下的高维最小方差组合估计AI 解读

We propose a high dimensional minimum variance portfolio estimator under statistical factor models, and show that our estimated portfolio enjoys sharp risk consistency. Our approach relies on properly integrating ℓ1 constraint on portfolio weights with an appropriate covariance matrix estimator. In terms of covariance matrix estimation, we extend the theoretical results of POET (Fan et al., 2013) to a setting that is coherent with principal component analysis. Simulation and…

跳跃扩散模型的非参数估计AI 解读

This paper develops the asymptotics for nonparametric kernel estimators of local time, drift and volatilities, and Lévy measure in jump diffusion models. Our asymptotics are developed in a very general set-up, allowing the sample span to increase as the sampling interval decreases, and without assuming stationarity. For drift and volatilities, we analyze both local constant and local linear estimators. We consider not only estimators for instantaneous conditional second…

观测渐近方差：强边缘效应与回归方法AI 解读

High frequency financial data has become an essential component of the digital economy, yielding an increasing number of estimators. However, it is hard to reliably assess the uncertainty of such estimators. The Observed Asymptotic Variance (observed AVAR) is a non-parametric estimator for (squared) standard error in high frequency data. The device is related to observed information in likelihood theory, but in this case it is non-parametric and uses the high-frequency data…

时变广义动态因子模型与金融关联度测量AI 解读

We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally stationary time series. Estimation is based on dynamic principal component analysis jointly with singular VAR estimation, and extends to the locally stationary case the one-sided estimation method proposed by Forni et al. (2017) for stationary data. We prove consistency of our estimators of time-varying impulse response functions as both the sample size T and the dimension n of the…

半参数分位数因子模型的估计与推断AI 解读

We consider a semiparametric quantile factor panel model that allows observed stock-specific characteristics to affect stock returns in a nonlinear time-varying way, extending Connor, Hagmann, and Linton (2012) to the quantile restriction case. We propose a sieve-based estimation methodology that is easy to implement. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term.…

基于已实现GARCH–Itô模型的波动率分析AI 解读

This paper introduces a unified approach for modeling high-frequency financial data that can accommodate both the continuous-time jump–diffusion and discrete-time realized GARCH model by embedding the discrete realized GARCH structure in the continuous instantaneous volatility process. The key feature of the proposed model is that the corresponding conditional daily integrated volatility adopts an autoregressive structure, where both integrated volatility and jump variation…

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…

多变量半非参数GARCH滤波Copula模型的有效估计AI 解读

This paper considers estimation of semi-nonparametric GARCH filtered copula models in which the individual time series are modeled by semi-nonparametric GARCH and the joint distributions of the multivariate standardized innovations are characterized by parametric copulas with nonparametric marginal distributions. The models extend those of Chen and Fan (2006) to allow for semi-nonparametric conditional means and volatilities, which are estimated via the method of sieves. The…

增广因子模型：市场风险因子验证与债券风险溢价预测AI 解读

We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama–French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the…

误设模型的广义聚合：资产定价应用AI 解读

We propose a generalized aggregation approach for model averaging. The entropy-based optimal criterion is a natural choice for aggregating information from many “globally” misspecified models as it adapts better to the underlying model uncertainty and obtains more robust approximations. Unlike almost all other approaches in the existing literature, we do not require a “reference model,” or a true data generation process contained in the set of models — neither implicitly nor…

日本股票市场的尾部风险与收益可预测性AI 解读

This paper studies the predictability of the Japanese equity market, focusing on the forecasting power of nonparametric volatility and tail risk measures obtained from options data on the S&P 500 and Nikkei 225 market indices. The Japanese market is notoriously difficult to forecast using standard predictive indicators. We confirm that country-specific regressions for Japan – contrary to existing evidence for other national equity indices – produce insignificant…

具有方差线性风险溢价的指数型 GARCH 模型AI 解读

One of the implications of the intertemporal capital asset pricing model is that the risk premium of the market portfolio is a linear function of its variance. Yet, estimation theory of classical GARCH-in-mean models with linear-in-variance risk premium requires strong assumptions and is incomplete. We show that exponential-type GARCH models such as EGARCH or Log-GARCH are more natural in dealing with linear-in-variance risk premia. For the popular and more difficult case of…

基于贝叶斯模型平均的半参数 GARCHAI 解读

As the dynamic structure of financial markets is subject to dramatic change, a model capable of providing consistently accurate volatility estimates should not make rigid assumptions on how prices change over time. Most volatility models impose a particular parametric functional form that relates an observed price change to a volatility forecast (news impact function). Here, a new class of functional coefficient semiparametric volatility models is proposed, where the news…

衡量资产市场联系：非线性依赖与尾部风险AI 解读

Traditional measures of dependence in time series are based on correlations or periodograms. These are adequate in many circumstances but, in others, especially when trying to assess market linkages and tail risk during abnormal times (e.g., financial contagion), they might be inappropriate. In particular, popular tail dependence measures based on exceedance correlations and marginal expected shortfall (MES) have large variances and also contain limited information on tail…