On the Long-Run Volatility of Stocks

• 成果类型: Article
• 署名作者: Carvalho, Carlos M.; Lopes, Hedibert F.; McCulloch, Robert E.
• 署名单位: University of Texas System; University of Texas Austin; Insper; University of Chicago; Arizona State University; Arizona State University-Tempe
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1407769
• 发表日期: 2018
• 页码: 1050-1069
• 关键词: bayesian-analysis; models; returns

摘要

In this article, we investigate whether or not the volatility per period of stocks is lower over longer horizons. Taking the perspective of an investor, we evaluate the predictive variance of k-period returns under different model and prior specifications. We adopt the state-space framework of Pastor and Stambaugh to model the dynamics of expected returns and evaluate the effects of prior elicitation in the resulting volatility estimates. Part of the developments includes an extension that incorporates time-varying volatilities and covariances in a constrained prior information set-up. Our conclusion for the U.S. market, under plausible prior specifications, is that stocks are less volatile in the long run. Model assessment exercises demonstrate the models and priors supporting our main conclusions are in accordance with the data. To assess the generality of the results, we extend our analysis to a number of international equity indices. Supplementary materials for this article are available online.