Time Variation in Cash Flows and Discount Rates.

This article focuses on the decomposition of the conditional variance of market returns into cash flow (CF) and discount rate (DR) news components using an extension of the Campbell–Shiller approach combined with a multivariate conditional variance model (corrected dynamic conditional correlations, cDCC). The study finds that the relative importance of CF and DR news in explaining conditional market variance varies significantly over time and is influenced by macroeconomic conditions, notably inflation, which increases the relative importance of CF news. Empirically, the components of conditional market variance provide superior in-sample and out-of-sample forecasts of future market returns and realized variance compared to aggregate conditional variance or realized variance alone. These improved forecasts translate into economically meaningful benefits in portfolio allocation exercises for investors with varying risk aversion and investment horizons, highlighting the value of decomposing conditional variance in financial modeling and investment decisions.