Risks for the Long Run: Estimation with Time Aggregation

52 Pages Posted: 18 Aug 2012

See all articles by Ravi Bansal

Ravi Bansal

Duke University and NBER

Dana Kiku

University of Illinois at Urbana-Champaign

Amir Yaron

University of Pennsylvania -- Wharton School of Business; National Bureau of Economic Research (NBER)

Date Written: August 2012

Abstract

The long-run risks (LRR) asset pricing model emphasizes the role of low-frequency movements in expected growth and economic uncertainty, along with investor preferences for early resolution of uncertainty, as an important economic-channel that determines asset prices. In this paper, we estimate the LRR model. To accomplish this we develop a method that allows us to estimate models with recursive preferences, latent state variables, and time-aggregated data. Time-aggregation makes the decision interval of the agent an important parameter to estimate. We find that time-aggregation can significantly affect parameter estimates and statistical inference. Imposing the pricing restrictions and explicitly accounting for time-aggregation, we show that the estimated LRR model can account for the joint dynamics of aggregate consumption, asset cash flows and prices, including the equity premia, risk-free rate and volatility puzzles.

Suggested Citation

Bansal, Ravi and Kiku, Dana and Yaron, Amir, Risks for the Long Run: Estimation with Time Aggregation (August 2012). NBER Working Paper No. w18305, Available at SSRN: https://ssrn.com/abstract=2131677

Ravi Bansal (Contact Author)

Duke University and NBER ( email )

Box 90120
Durham, NC 27708-0120
United States
919-660-7758 (Phone)
919-660-8038 (Fax)

Dana Kiku

University of Illinois at Urbana-Champaign ( email )

601 E John St
Champaign, IL 61820
United States

Amir Yaron

University of Pennsylvania -- Wharton School of Business ( email )

The Wharton School
3620 Locust Walk
Philadelphia, PA 19104
United States
215-898-1241 (Phone)
215-898-6200 (Fax)

National Bureau of Economic Research (NBER) ( email )

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Cambridge, MA 02138
United States

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