Database providers or managers: who predict best future performance ?

Isabelle Martinez (), Thomas Jeanjean and Grégoire Davrinche ()
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Isabelle Martinez: LGCO - Laboratoire Gouvernance et Contrôle Organisationnel - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse
Grégoire Davrinche: LGCO - Laboratoire Gouvernance et Contrôle Organisationnel - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse

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Abstract: We introduce a new housing consumption-based asset pricing model (Housing CCAPM) that incorporates rare disaster events into the dynamics of non-housing consumption and rare boom/disaster events into housing consumption. Our analytical framework hinges on two key ideas. First, we extend existing Cumulant Generating Function-based pricing formulas to a two-good economy. Second, we develop an exponential affine approximation for the price-dividend ratio. These techniques enable us to derive intuitive closed form solutions for the risk-free rate, risk premia, the volatility of excess returns and the term structure of interest rates. Using monthly U.S. aggregate consumption data and a maximum likelihood estimation approach, we show that, while rare disaster events in non-housing consumption contribute only marginally to explaining the low risk-free rate, the high equity premium and high equity volatility observed in the data, rare boom/disaster events in housing expenditures solve these puzzles for moderate levels of risk aversion and intratemporal elasticity of substitution. Additionally, we show that the Housing CCAPM framework with CRRA utility will consistently produce theoretical upward-sloping real yield curves, and interpret the standard CCAPM risk-free rate as the rate that would prevail during extreme recessions.

Date: 2017-04-24
Note: View the original document on HAL open archive server: https://ut3-toulouseinp.hal.science/hal-04283945
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Published in Séminaire Finance 2017, CREM - Centre de Recherche en Economie et Management, UMR 6211 CNRS, Université Rennes 1, Apr 2017, Rennes, France

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Working Paper: Database providers or managers: who predict best future performance? (2017)
Working Paper: Database providers or managers: who predict best future performance? (2017)
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