Heteroskedasticity in Stock Returns
利用日度数据预测月度股票收益方差，估计规模排序投资组合的时变方差，提出异方差单因子模型并发现时变贝塔，检验CAPM时考虑异方差增加了风险调整收益与公司规模相关的证据。
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.