This relation holds true because i is more risk averse than j in the first stage.Turn to the proof of the converse statement. Fix a set E ⊂ S such that λ = ∫ π(E)dµ P s t(π) ∈s t(0, 1). Suppose (c, δ[l]) ≽ j s(c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]) for l, l ′ , l ′′ ∈ L. Let h t +1 be the one-stepaheadact that gives l ′ if event E happens <strong>and</strong> gives l ′′ , otherwise. Then by definitionwe can show that b(h +1 , µ s t) = λδ[l ′ ] + (1 − λ)δ[l ′′ ]. Using the representation in Theorem3, we can verify that (c, b(h +1 , µ s t)) ∼ j s(c, δ[h t +1 ]) or (c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]) ∼ j s t(c, δ[h +1 ]), which implies (c, δ[l]) ≽ j s(c, δ[h t +1 ]). By comparative ambiguity aversion, wehave (c, δ[l]) ≽ i s(c, δ[h t +1 ]). Since (c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]) ∼ i s(c, δ[h t +1 ]) holds as well, weobtain (c, δ[l]) ≽ i s(c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]). Hence we havet(c, δ[l]) ≽ j s(c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]) =⇒ (c, δ[l]) ≽ i t s (c, t λδ[l′ ] + (1 − λ)δ[l ′′ ]).We can extend this result to all λ ∈ (0, 1) by continuity (Axiom B1) <strong>and</strong> Axiom B4 (FirstStage Independence). We can also extend this result to all finite lotteries over L by repeatedlyapplying the above argument. We finally extend it to all lotteries over L by continuity ofpreferences (Axiom B1).E Appendix: Proofs for Section 5Proof of Proposition 7:Defineφ t (α) = V t (c + αδ) ,for an adapted process (δ t ) . Using equations (24)-(26), we have:φ t (α) = W (c t + αδ t , R t (V t+1 (c + αδ))) .Taking derivatives in the preceding equation yieldsφ ′ t (0) = W 1 (c t , R t (V t+1 )) δ t{+ W 2 (c t , R t (V t+1 )) v ′ ◦ u ( −1 E πz,t [u (V t+1 )] )Ev ′ µt(R t (V t+1 )) u ( ′ u ( −1 E πz,t [u ′ (V t+1 )] ))E [π z,t u ′ (V t+1 ) φ ′ t+1 (0) ]}Define λ t as in (22) <strong>and</strong> E z tas in (23). We obtain:[ ]Eφ ′ zt (0) = λ t δ t + E t+1t φ ′Etz t+1 (0) ,where E t is the conditional expectation operator with respect to the predictive distribution∑z µ t (z) π z (·|s t ) . From this equation <strong>and</strong> the definition in (21), we can derive that ξ z t =E z t λ t .51
Proof of Proposition 8:When the utility function takes the homothetic form, we useProposition 7 <strong>and</strong> the definition of the pricing kernel to derive (27). Alternatively, we maywrite the pricing kernel in terms of the market return as in Epstein <strong>and</strong> Zin (1989). In acomplete market, wealth X t satisfiesX t = E t[ ∞∑s=tThat is, time t wealth is equal to the present value of the consumption stream. By Lemma6.25 in Skiadas (2009), we haveBy equation (22), we have the relation:c tV t=Thus, the consumption-wealth ratio satisfies:c tX t= c tλ tV t=ξszξtzc s].V t = λ t X t . (50)( ) −1/ρ λt.1 − β(λt1 − βEliminating λ t from equations (50) <strong>and</strong> (51) yields:V t = λ t X t = (1 − β) 1= (1 − β) 1−ρ1−ρ c1−ρt X 11−ρ−ρ1−ρ c1−ρt R 11−ρ) −1/ρλ t . (51)t (52)t (X t−1 − c t−1 ) 1where we have used equation (29) to derive the last equality. Note that the second equalityimplies thatX t= 1 ( ) 1−ρ Vt. (53)c t 1 − β c tAs a result, for unitary EIS (ρ = 1), the consumption-wealth ratio is equal to 1 − β.1−ρ ,Now, substituting equation (52) into (27) <strong>and</strong> manipulating, we derive:⎛⎞ ⎛ [ρ−γ −ρ(1−γ) 1−γ−ρ( ) −ρ 1−ρMt+1 z ct+1= β ⎜ct+1 R 11−ρ 1−ρ1−ρ⎜(E πz,t ct+1 Rt+1t+1c t⎝) ⎟ ⎜⎜⎝−ρ ⎠)−ρ1−ρ 1−ρ1−ρ 1−ρR t(cR t(cM z t+1 =β(ct+1c tt+1 R 1t+1t+1 R 1t+1Writing in terms of consumption growth <strong>and</strong> manipulating, we obtain:( ) ) 1−γ⎛ (−ρ 1−ρ⎡ ⎛(× ⎣R t⎝ β1−γ1−ρR−1t+1⎡⎝E πz,t⎣( ) ) 1−ρ 1−ρct+1R t+1c t⎞⎤⎠⎦βη−ρ(ct+1.c t]) 11−γ) −ρR t+1) 1−γ1−ρ⎞⎟⎠⎤⎞⎦⎠−(η−γ)−(η−γ)1−γ.52
- Page 1: Intertemporal Substitution andRecur
- Page 6 and 7: As in KMM (2005, 2009a), we impose
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- Page 56 and 57: Ergin, H. I. and F. Gul (2009): “
- Page 58: Weil, P. (1989): “The Equity Prem