Working Paper: NBER ID: w11441
Authors: Laurent E. Calvet; Adlai J. Fisher
Abstract: Recent research documents that aggregate stock prices are driven by shocks with persistence levels ranging from daily intervals to several decades. Building on these insights, we introduce a parsimonious equilibrium model in which regime-shifts of heterogeneous durations affect the volatility of dividend news. We estimate tightly parameterized specifications with up to 256 discrete states on daily U.S. equity returns. The multifrequency equilibrium has significantly higher likelihood than the classic Campbell and Hentschel (1992) specification, while generating volatility feedback effects 6 to 12 times larger. We show in an extension that Bayesian learning about stochastic volatility is faster for bad states than good states, providing a novel source of endogenous skewness that complements the "uncertainty" channel considered in previous literature (e.g., Veronesi, 1999). Furthermore, signal precision induces a tradeoff between skewness and kurtosis, and economies with intermediate investor information best match the data.
Keywords: Multifrequency shocks; Stock returns; Volatility feedback; Bayesian learning
JEL Codes: G12; C22
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| multifrequency shocks (C58) | stock returns (G12) |
| volatility feedback effects (E32) | stock returns (G12) |
| higher volatility (G17) | prices (P22) |
| Bayesian learning about stochastic volatility (C58) | skewness in stock returns (C46) |
| bad news about volatility (G17) | prices (P22) |
| good news about volatility (G17) | prices (P22) |
| multifrequency equilibrium model (E19) | volatility feedback effects (E32) |