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An R-Based Landscape Validation of a Competing Risk Model
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Rare events and long-run risks.

Robert J Barro1, Tao Jin2

  • 1Harvard University, USA.

Review of Economic Dynamics
|August 27, 2020
PubMed
Summary
This summary is machine-generated.

This study models rare events and long-run risks in macroeconomics using consumption data. Rare events explain most of the equity premium, while long-run risks aid Sharpe ratio fitting.

Keywords:
Asset pricingLong-run risksRare eventsRisk aversion

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Area of Science:

  • Economics
  • Asset Pricing
  • Macroeconomic Analysis

Background:

  • Rare events (RE) and long-run risks (LRR) are key concepts in understanding macroeconomic variables and asset pricing.
  • These two risk frameworks offer complementary perspectives on financial markets.

Purpose of the Study:

  • To simultaneously estimate and differentiate RE and LRR using long-term consumption data from 42 economies.
  • To assess the contribution of RE and LRR to explaining the equity premium and Sharpe ratio.

Main Methods:

  • Estimation of a joint model incorporating both rare events and long-run risks.
  • Utilizing long-term consumption data across multiple economies.
  • Analysis of asset returns, specifically equity and short-term bonds.

Main Results:

  • RE are linked to major historical economic downturns and country-specific crises.
  • LRR are associated with gradual changes affecting long-term growth and volatility.
  • A coefficient of relative risk aversion (γ) around 6 is required for model-data consistency.
  • RE account for the majority of the equity premium, with LRR providing a moderate contribution.
  • LRR are instrumental in fitting the Sharpe ratio, though simultaneous matching of both metrics remains challenging.

Conclusions:

  • RE and LRR are distinct yet complementary risk factors in asset pricing.
  • The model provides insights into the drivers of the equity premium and Sharpe ratio.
  • Further refinement is needed to simultaneously match both equity premium and Sharpe ratio within the model.