Fiscal policy and asset markets: A semiparametric analysis
财政政策与资产市场:半参数分析AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要明确分析财政和货币政策与股票市场、企业债及国债收益率的关系,直接关联资产市场定价方向。
财政政策与资产市场:半参数分析AI 解读 ↓
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Using a flexible semiparametric varying coefficient model specification, this paper examines the role of fiscal policy on the US asset markets (stocks, corporate and treasury bonds). We consider two possible roles of fiscal deficits (or surpluses): as a separate direct information variable and as a (indirect) conditioning information variable indicating binding constraints on monetary policy actions. The results show that the impact of monetary policy on the stock market varies, depending on fiscal expansion or contraction. The impact of fiscal policy on corporate and treasury bond yields follow similar patterns as in the equity market. The results are consistent with the notion of strong interdependence between monetary and fiscal policies.
Fama–French 模型及其扩展的有效半参数估计
This paper develops a new estimation procedure for characteristic-based factor models of stock returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time-varying weights and a set of univariate nonparametric functions relating security characteristic to the associated factor betas. We use a time-series and cross-sectional pooled weighted additive nonparametric regression methodology to simultaneously…
设定 LASSO 与灵活的特征型资产定价模型
This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings. We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification. Theoretically, we establish that S-LASSO possesses oracle properties in terms of selection consistency and asymptotic normality. Monte Carlo simulations…
共同基金 alpha 的异质可预测性:稀疏聚类 GMM 方法
Mutual fund managers' skills, measured by risk-adjusted alphas, are predictable using fund characteristics identified by machine learning (e.g., Kaniel et al., 2023, JFE; DeMiguel et al., 2023, JFE). However, alpha's predictive power varies across funds and periods, with most funds exhibiting negligible alphas. To model this heterogeneous predictability, this paper introduces Sparse Clustering GMM, a nonparametric approach for uncovering latent fund groups. SCGMM clusters…
股票指数间的跳跃传染:来自期权市场的证据
We analyze the contagious propagation of jumps among international stock market indices, using a rich panel of high-frequency stock and options data ( 692,892 option contracts) over the period 2006–2015. We propose a bivariate option pricing model designed to allow for time and space amplification of jumps in option markets. We develop a semi-parametric estimation procedure, which employs a continuum of moment conditions in GMM with implied states and non-parametric…
依赖观测下高维条件时变因子模型的 Alpha 检验
This paper studies alpha testing in a high-dimensional conditional time-varying factor model with temporally dependent observations. Both factor loadings and alpha processes are allowed to vary smoothly over time, and the cross-sectional dimension may be comparable to or larger than the sample size. Using a B-spline sieve method, we develop a sum-type test for dense alternatives, a max-type test for sparse alternatives, and a Cauchy combination test for adaptive inference.…
用核回归估计线性模型中不同光滑度的时变参数
We study kernel-based estimation of nonparametric time-varying parameters (TVPs) in linear models. Our contributions are threefold. First, we establish consistency and asymptotic normality of the kernel-based estimator for a broad class of TVPs including deterministic smooth functions, the rescaled random walk, structural breaks, the threshold model and their mixtures. Our analysis exploits the smoothness of the TVP. Second, we show that the bandwidth rate must be determined…