Stock return and cash flow predictability: The role of volatility risk
股票收益与现金流可预测性:波动率风险的作用AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要直接研究收益预测、波动率风险,以及包含潜在波动率因子的结构因子 GARCH 模型。
股票收益与现金流可预测性:波动率风险的作用AI 解读 ↓
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We examine the joint predictability of return and cash flow within a present value framework, by imposing the implications from a long-run risk model that allow for both time-varying volatility and volatility uncertainty. We provide new evidence that the expected return variation and the variance risk premium positively forecast both short-horizon returns and dividend growth rates. We also confirm that dividend yield positively forecasts long-horizon returns, but that it does not help in forecasting dividend growth rates. Our equilibrium-based “structural” factor GARCH model permits much more accurate inference than univariate regression procedures traditionally employed in the literature. The model also allows for the direct estimation of the underlying economic mechanisms, including a new volatility leverage effect, the persistence of the latent long-run growth component and the two latent volatility factors, as well as the contemporaneous impacts of the underlying “structural” shocks.
利用已实现方差改进马尔可夫切换模型
Summary This paper proposes a class of models that jointly model returns and ex post variance measures under a Markov switching framework. Both univariate and multivariate return versions of the model are introduced. Estimation can be conducted under a fixed dimension state space or an infinite one. The proposed models can be seen as nonlinear common factor models subject to Markov switching and are able to exploit the information content in both returns and ex post…
ABSTRACT We use predictions of aggregate stock return variances from daily data to estimate time‐varying monthly variances for size‐ranked portfolios. We propose and estimate a single factor model of heteroskedasticity for portfolio returns. This model implies time‐varying betas. Implications of heteroskedasticity and time‐varying betas for tests of the capital asset pricing model (CAPM) are then documented. Accounting for heteroskedasticity increases the evidence that…
预测 VIX 与波动率风险溢价:短期融资利差波动因子的作用
This paper presents an innovative approach to extract Volatility Factors which predict the VIX, the S&P500 Realized Volatility (RV) and the Variance Risk Premium (VRP). The approach is innovative along two different dimensions, namely: (1) we extract Volatility Factors from panels of filtered volatilities — in particular large panels of univariate ARCH-type models and propose methods to estimate common Volatility Factors in the presence of estimation error and (2) we price…
SUMMARY A new estimator is proposed for linear triangular systems, where identification results from the model errors following a bivariate and diagonal GARCH(1,1) process with potentially time‐varying error covariances. This estimator applies when traditional instruments are unavailable. I demonstrate its usefulness on asset pricing models like the capital asset pricing model and Fama–French three‐factor model. In the context of a standard two‐pass cross‐sectional…
We introduce a new model for time-varying conditional variances as the most general quadratic version possible within the ARCH class. Hence, it encompasses all the existing restricted quadratic variance functions. Its properties are very similar to those of GARCH models, but avoids some of their criticisms. In univariate applications to daily U.S. and monthly U.K. stock market returns, QARCH adequately represents volatility and risk premia. QARCH is easy to incorporate in…
ABSTRACT We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range‐based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range‐based Gaussian quasi‐maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine…