Heteroskedasticity in Stock Returns
利用日度数据预测月度股票组合方差，提出异方差单因子模型，发现考虑异方差后风险调整收益与公司规模的关系更显著。
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 risk‐adjusted returns are related to firm size. We also estimate a constant correlation model. Portfolio volatilities predicted by this model are similar to those predicted by more complex multivariate generalized‐autoregressive‐conditional‐heteroskedasticity (GARCH) procedures.