We develop a pseudo maximum likelihood method for latent factor analysis in short panels without imposing sphericity nor Gaussianity. We derive an asymptotically uniformly most powerful invariant test for the number of factors. On a large panel of monthly U.S. stock returns, we separate month after month systematic and idiosyncratic risks in short subperiods of bear vs. bull market. We observe an uptrend in the paths of total and idiosyncratic volatilities. The systematic risk explains a large part of the cross-sectional total variance in bear markets but is not driven by a single factor and not spanned by observed factors.

翻译：我们提出了一种伪极大似然方法，用于短面板中的潜因子分析，该方法既不要求球形误差假设，也不要求高斯性假设。我们推导出了一种渐近一致最有效的不变检验，用于确定因子数量。在一个包含大量美国月度股票收益数据的面板中，我们逐月分离了熊市与牛市短期子期间的系统性风险和异质性风险。我们观察到总波动率和异质性波动率路径呈现上升趋势。系统性风险在熊市中解释了横截面总方差的很大一部分，但并非由单一因子驱动，且无法由观测到的因子所完全解释。