On the Long-Run Volatility of Stocks

被引:8
|
作者
Carvalho, Carlos M. [1 ]
Lopes, Hedibert F. [2 ]
McCulloch, Robert E. [3 ,4 ]
机构
[1] Univ Texas Austin, McCombs Sch Business, 2110 Speedway Stop B6500, Austin, TX 78712 USA
[2] INSPER, Sao Paulo, Brazil
[3] Univ Chicago, Booth Sch Business, Chicago, IL 60637 USA
[4] Arizona State Univ, Sch Math & Stat Sci, Tempe, AZ USA
关键词
Covariance matrix; Dynamic models; Long-run investing; Volatility; BAYESIAN-ANALYSIS; MODELS; RETURNS;
D O I
10.1080/01621459.2017.1407769
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
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.
引用
收藏
页码:1050 / 1069
页数:20
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