the risky capital of emerging marketsinasimonovska.weebly.com/uploads/1/3/2/5/1325220/... · the...

The Risky Capital of Emerging Markets

Joel M. David Espen Henriksen Ina Simonovska

USC UCD UCD & NBER

February 24, 2014

1 / 27

Lucas Paradox Revisited

6 7 8 9 10 11 12

0.05

0.1

0.15

0.2

0.25

0.3

Ret

urn

to U

S In

vest

or

Log GDP per Worker

slope=−0.0178***

Lucas Paradox: Should capital flow to poor countries? Lit Review

Not necessarily... Riskier capital in poor countries ⇒ higher average returns.

But can differences in risk quantitatively account for differences in returns?

Yes!

Risk (measured properly) accounts for > 3/4 of difference in returns between

poorest countries and US.

2 / 27

What we do...

First pass: workhorse CCAPM... works qualitatively; not quantitatively

⇒ Lucas Paradox: just another asset pricing puzzle

Towards a resolution:

Key Building blocks

1 Aguiar and Gopinath (2007): shocks to trend growth key in

poor/emerging market BC’s +

2 Bansal and Yaron (2004): implications of these shocks for asset

prices/returns, i.e., compensation for “long-run risks”

Our Approach

1 Endowment economy in spirit of BY; multiple international “assets”

⇒ assets differ in shocks to trend of cash flows from capital investment

2 Use TS moments in cash flows (“dividends”) to infer key parameters

⇒ Follow portfolio approach—3, 5, 10 “bundles”

3 Use estimated parameters to assess implications for capital returns

3 / 27

What we find...

Investments in poor countries are more exposed to long-run risk

⇒ US investor demands higher average returns

• Risk accounts for > 3/4 of return differential across portfolios

• Results are robust to levels of disaggregation

• LRR key; SRR implies negative risk premia abroad

• Differences in returns driven by differences in volatilities of cash flows

Some evidence of return convergence in mid-late 1990s

• Recalibrating model to this sub-period closes gap as well...(though noisy)

• Deeper explanation?

4 / 27

Measuring Returns to Capital for US Investor

Environment: N countries; two sectors: K is freely traded, C is not.

Representative US agent consumes, buys capital in US, invests it in country j .

Return from investment in j for US investor (builds on Caselli and Feyrer, ’07):

Rjt = α

Pusy,tY

jt

Kjt︸︷︷︸

CF j in Cus

1

Pusi,t

︸︷︷︸dividend yield, D/P

+(1− δjt

) Pusi,t

Pusi,t−1︸︷︷︸

capital gains

• Pusi ,P

usy is US price of K, Y relative to US price of C

5 / 27

Returns Revisited: “Bundles” of countries

Assume common α = 0.3.

Data:

• PWT 8.0, 1950-2009: real K j ,Y j , δj

• BEA 1950-2009:• Pus

i—price index of equipment + structures

• Pusy —price index of output

• Pusc —price index of non-durables + services

Countries bundled according to mean output per worker over period. Portfolios

6 7 8 9 10 11 12

0.05

0.1

0.15

0.2

0.25

0.3

Ret

urn

to C

apita

l

Log GDP per Worker

1 2

3 US

6 / 27

Lucas Paradox: Just Another Asset Pricing Puzzle

With CRRA preferences:

E [r ejt ] ≈ γcov(rjt ,∆ct)

• r ejt ≡ rjt − rft is excess (net) return on portfolio j over 3-month t-bill at t

• ∆ct is US (ND+S) consumption growth during 1950-2009

r̂ e

Portfolio r e cov (r ,∆c) γ = 10 907

1 11.60 0.00015 0.15 13.79

2 10.00 0.00009 0.09 8.33

3 6.93 0.00005 0.05 4.28

US 4.80 0.00004 0.04 3.29

Spread: 1-US 6.80 0.00012 0.12 10.50

Derivation Alternative Aggregation

7 / 27

Toward a Resolution

Key Building blocks:

1 Aguiar and Gopinath (2007): shocks to trend growth key in

poor/emerging market BC’s +

2 Bansal and Yaron (2004): implications of these shocks for asset

prices/returns, i.e., compensation for “long-run risks”

1950 1960 1970 1980 1990 2000 2010

100

200

300

400

600

log

Cas

h F

low

s, 1

951=

100

United States

1950 1960 1970 1980 1990 2000 2010

100

200

300

400

600

log

Cas

h F

low

s, 1

951=

100

India

0.00 0.02 0.04 0.06

010

2030

4050

6070

10−Year Avg. Cash−Flow Growth Rates

Den

sity

United StatesIndia

8 / 27

Model

1 Endowment economy in spirit of BY; multiple international “assets”

⇒ assets differ in shocks to trend of cash flows from capital investment

2 Use TS moments in cash flows (“dividends”) to infer key parameters

⇒ Follow portfolio approach—3, 5, 10 “bundles”

3 Use estimated parameters to assess implications for capital returns

In other words:

Is US investor’s SDF w LRR consistent with cross-section of returns?

9 / 27

Model: Processes

Representative US-based investor endowed with c and dj , j indexes asset:

∆ct+1 = µc + xt + σηt+1

xt+1 = ρxt + ϕeσet+1

∆dj,t+1 = µj + φjxt + πjσηt+1 + ϕjσuj,t+1

ηt+1, et+1, uj,t+1 ∼ NIID (0, 1)

Portfolio-specific variables:

• µj : mean growth rate

• φj : exposure to trend shock in US consumption growth

• πj : exposure to transitory shock in US consumption growth

• ϕj : volatility of dividend shock

10 / 27

Model: US Investor’s Preferences

Representative US-based investor’s preferences

Vt =

[(1− β)C

ψ−1ψ

t + βνt (Vt+1)ψ−1ψ

] ψψ−1

νt (Vt+1) =[Et

(V

1−γ

t+1

)] 11−γ

• νt (Vt+1) is certainty equivalent function

• ψ is intertemporal elasticity of substitution

• γ ≥ 0 is risk aversion coefficient

• β < 1 is discount rate

• CRRA special case iff γ = 1/ψ

11 / 27

Risks in the Short and Long Run

Key asset-pricing equation for any asset j :

Et [Must+1R

jt+1],

Must+1 is US investor’s SDF. SDF

Risk premium for asset j :

E [Rejt ] ≈

(φj −

1

ψ

)(γ −

1

ψ

)κj

1− κjρ

κc

1− κcρϕ2

eσ2

︸︷︷︸long-run risk

+ γπjσ2

(1

1

)

︸︷︷︸short-run risk

where:

• κj = fd (θj , θc)

• κc = fc (θc)

Solving Model

12 / 27

Model: Calibration of Consumption Growth Parameters

Recall:

∆ct+1 = µc + xt + σηt+1


ρ = 0.93 (Ferson, 2013; Bansal, Kiku, and Yaron, 2012)

E(∆ct) = µc

cov (∆ct ,∆ct+1) = ρϕ2

eσ2

1− ρ2

var(∆ct) =

(ϕ2

e

1− ρ2+ 1

)σ2

13 / 27

Model: Calibration of Dividend Growth Parameters

Recall:



E(∆dj,t) = µj

φj =

√cov (∆dj,t ,∆dj,t+1)

cov (∆ct ,∆ct+1)

cov (∆dj,t ,∆cj,t) = φjϕ2

eσ2

1− ρ2+ πjσ

2

var(∆dj,t) = φ2j

ϕ2eσ

2

1− ρ2+(π2j + ϕ2

j

)σ2

14 / 27

An Example: US VS India

Data: E(Rus) = 6.0; E(R in) = 9.0

acov(∆dt ,∆dt+1) =

acorr(∆dt ,∆dt+1) · var(∆dt)

−0.1 −0.05 0 0.05 0.1 0.15−0.1

−0.08

−0.06

−0.04

−0.02

0

0.02

0.04

0.06

0.08

0.1

∆ d t+

1

∆ dt

IndiaUnited States

corr=0.282**

corr=0.279**

1960 1970 1980 1990 2000 2010

−0.1

−0.05

0

0.05

0.1

∆ d t

IndiaUnited States

var(∆ dus

)=0.00067

var(∆ dind

)=0.00211

⇒ Model: E(Rus) = 6.0; E(R in) = 8.3

15 / 27

Data: Moments

Consumption E [∆ct ] cov (∆ct+1,∆ct) std (∆ct)

0.019 0.00024 0.022

Portfolio E [∆dt ]√

cov(∆dt+1,∆dt )

cov(∆ct+1,∆ct )cov (∆dt ,∆ct) std (∆dt)

1 −0.017 5.94 0.00013 0.085

2 −0.015 6.12 0.00011 0.075

3 −0.012 4.22 0.00016 0.062

US −0.006 2.19 0.00018 0.026

• US Consumption TS: 1929-2009

• Portfolio TS: vary by portfolio/country based on PWT availability

• Portfolio moment computed as mean of country-specific moments

Observe:

• cov(∆d ,∆c) ≈ across portfolios

• std(∆d) falling in income/worker

• acov(∆d) high for portfs 1/2 compared to 3/US

Rebalanced Portfolios16 / 27

Calibrated Model: Parameters

∆ct+1 = µc + xt + σηt+1



Preferences: γ = 10 ψ = 1.5 β = 0.99

Consumption: ρ = 0.93 µc = 0.019 σ = 0.015 ϕe = 0.39

Portfolio µd φ ϕd π

1 −0.017 5.94 7.75 −1.92

2 −0.015 6.12 6.45 −1.96

3 −0.012 4.22 5.97 −0.34

US −0.006 2.19 2.12 0.98

Observe: φj high for portfs 1/2 compared to 3/US.

Question: What are the implications for returns to capital?

Rebalanced Portfolios 17 / 27

Predicted VS Actual Returns: 3 Bundles

Portfolio r̂ r

1 11.75 12.82

2 12.00 11.22

3 9.47 8.15

US 5.94 6.02

Average: 9.79 9.55

Spread: 1-US 5.81 6.80

Percent of actual 85

• Predicted returns ≈ actual returns

• Returns for portf 1 ≈ 2 > returns for portf 3, US

PDR VARR

18 / 27

Predicted VS Actual Returns: 3, 5, 10 Bundles

3 Portfolios 5 Portfolios 10 Portfolios

r̂ r r̂ r r̂ r

1 11.75 12.82 1 12.06 14.16 1 12.95 15.59

2 12.00 11.22 2 12.42 11.49 2 11.33 13.26

3 9.47 8.15 3 10.87 10.84 3 10.87 11.22

US 5.94 6.02 4 11.16 10.57 4 13.60 11.64

5 8.53 6.64 5 11.04 9.95

US 5.94 6.02 6 10.56 12.08

7 11.81 10.11

8 10.37 11.03

9 9.39 7.31

10 7.34 5.99

US 5.94 6.02

Average: 9.79 9.55 10.16 9.95 10.47 10.38

Spread: 1-US 5.81 6.80 6.13 8.14 7.01 9.57

Percent of actual 85 75 73

corr (̂r , r) 0.94 0.91 0.83

Corr btwn predicted and actual returns high at different levels of aggregation.

Rebalanced Portfolios

19 / 27

Decomposition: Long VS Short Run Risk

Actual Predicted

Portfolio r r r f r esr r elr

1 12.82 11.75 = 1.27 + −0.43 + 10.91

2 11.22 12.00 = 1.27 + −0.44 + 11.17

3 8.15 9.47 = 1.27 + −0.08 + 8.27

US 6.02 5.94 = 1.27 + 0.22 + 4.44

High returns mainly driven by long-run risk.

20 / 27

High Returns Driven by High Volatility of Cash Flows

Actual Predicted r

Portfolio r acorr(∆d) std(∆d) baseline acorr(∆d) = US std(∆d) = US

1 12.82 0.22 0.085 11.75 12.68 4.57

2 11.22 0.28 0.075 12.00 12.06 5.32

3 8.15 0.19 0.062 9.47 10.84 4.64

US 6.02 0.28 0.026 5.94 5.94 5.94

Recall that φj =

√cov(∆dj,t ,∆dj,t+1)cov(∆ct ,∆ct+1)

and has first-order impact on returns.

Counterfactuals:

• Column 6: If acorr(∆d j ) = acorr(∆dus), returns remain high.

• Column 7: If std(∆d j) = std(∆dus), returns fall.

⇒ High returns driven by high volatility of cash flows.

21 / 27

Predicted VS Actual Returns by Country

7.5 8 8.5 9 9.5 10 10.5 11

0.05

0.1

0.15

0.2

0.25

Log GDP per Worker

Act

ual R

etur

n slope=−0.023***

7.5 8 8.5 9 9.5 10 10.5 11

0.05

0.1

0.15

0.2

0.25

Log GDP per Worker

Pre

dict

ed R

etur

n

slope=−0.011**

0.05 0.1 0.15 0.2 0.25

0.05

0.1

0.15

0.2

0.25

Predicted Return

Act

ual R

etur

n

corr=0.26**

69 countries s.t.: (i) in PWT in or prior to 1961; (ii) autocov(∆d ,∆d+1) ≥ 0

Portfolios22 / 27

Predicted VS Actual Returns: 69 Countries Bundled Up


r̂ r r̂ r r̂ r

1 11.50 12.20 1 13.03 13.93 1 13.03 15.37

2 9.58 9.36 2 10.18 10.47 2 13.03 13.32

3 9.50 7.48 3 9.14 9.29 3 9.38 10.34

US 5.94 6.02 4 10.63 9.31 4 10.97 10.59

5 8.34 5.74 5 8.72 8.96

US 5.94 6.02 6 9.52 9.59

7 9.71 9.21

8 11.68 9.42

9 8.83 6.41

10 7.69 4.86

US 5.94 6.02

Average: 9.13 8.76 9.54 9.13 9.86 9.46

Spread: 1-US 5.56 6.18 7.09 7.91 7.09 9.35


corr (̂r , r) 0.90 0.91 0.88

23 / 27

TS Returns on Capital: 69 Countries

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22R

etur

n on

cap

ital

Portfolio 1Portfolio 2Portfolio 3United States

Open, Fixed Distribution Open, New Distribution Very Open, Fixed Distribution

24 / 27

Calibration of Model to Post-1995 Data, 69 Countries

1950-2009

Portfolio√

acov(∆d)acov(∆c)

acorr(∆d) std(∆d) cov(∆d,∆c) corr(∆d,∆c)

1 5.94 0.22 0.085 0.00013 0.13

2 6.12 0.28 0.075 0.00011 0.12

3 4.22 0.19 0.062 0.00016 0.22

US 2.19 0.28 0.026 0.00018 0.60

1995-2009√acov(∆d)acov(∆c) acorr(∆d) std(∆d) cov(∆d,∆c) corr(∆d,∆c)

1 2.82 0.10 0.060 0.00004 0.06

2 3.21 0.18 0.051 −0.00003 −0.05

3 6.19 0.47 0.061 0.00026 0.41

US 1.61 0.48 0.016 0.00007 0.40

• acov(∆d) for portfolios 1, 2, and US falls...

• acov(∆d) for portfolio 3 rises (driven by Eurozone countries)

Question: What are the implications for returns to capital?

25 / 27

Predicted VS Actual Returns After 1995, 69 Countries

1950-2009 1995-2009

Portfolio r r̂ r r̂ r̂acorrd r̂stdd r̂corrcd

1 12.82 11.75 7.34 6.26 8.43 8.40 6.37

2 11.22 12.00 6.09 6.74 8.00 9.10 6.97

3 8.15 9.47 5.26 12.11 9.09 12.28 11.87

US 6.02 5.94 4.79 4.07 3.16 6.34 4.17

Answer:

Returns for portfolios 1, 2, US fall in model/data... But returns for 3 rise.

Counterfactuals: Focus on portfolio 3

• Column 6: If acorr(∆d j ) = acorr(∆dj51−08), returns < re-calibrated model.

• 7/8: If std(∆d j) = std(∆dj51−08) or corr(∆c,∆d j ) = corr(∆c,∆d

j51−08),

returns ≈ re-calibrated model.

⇒ High returns for portfolio 3 post 1995 driven by high autocorr of cash flows.

Any idea why?...

26 / 27

Conclusion

Can differences in risk quantitatively account for differences in returns?

Yes!

Risk (measured properly) accounts for > 3/4 of difference in returns between

poorest countries and US.

Key intuition: US investor does not invest in poor countries because shocks to

trend growth of dividends are larger there.

Ongoing work:

• Explore return volatility and co-movement in model/data

• Implications about K/Y

Future work:

• Time-varying volatility

• Country/portfolio-specific permanent components of cash flow growth

• Time-series analysis

27 / 27

Closely Related Literature

Lucas Paradox

Lucas (1990), Caselli and Feyrer (2007)

Asset-pricing puzzle

Mehra and Prescott (1985)

Solving AP puzzles with long-run risk

Bansal and Yaron (2004), Colacito and Croce (2011), Lustig and Verdelhan

(2007), Borri and Verdelhan (2011)

Long-run risk around the world

Aguiar and Gopinath (2007), Naoussi and Tripier (2013), Nakamura, Sergeyev,

and Steinsson (2012), Lewis and Liu (2012)

Intro

– / 27

Measuring Returns to Capital for US Investor

Environment: N countries; two sectors: K is freely traded, C is not.

Representative US consumer supplies unit of labor inelastically and maximizes

E0

∞∑

t=0

βtU (C us

t ) s.t.

Cust + P

usi,t

K us,ust+1 +

∑

j 6=us

Kus,jt+1

= Wust +

[αPus

y,tYust

K ust

+ Pusi,t (1− δust )

]K

us,ust +

∑

j 6=us

[αPus

y,tYjt

Kjt

+ Pusi,t

(1− δjt

)]

Kus,jt

Euler equation for capital invested in any country j (including US):

1 = Et

[

βU ′ (C us

t+1)

U ′ (C ust )

(

αPusy,t+1Y

ust+1

K ust+1P

usi,t

+ (1− δust )Pusi,t+1

Pusi,t

)]

= Et [Must+1R

ust+1]

= Et

[βU ′ (C us

t+1)

U ′ (C ust )

(αPusy,t+1Y

jt+1

Kjt+1P

usi,t

+(1− δjt

) Pusi,t+1

Pusi,t

)]= Et

[M

ust+1R

jt+1

]

Intro

– / 27

Derivation: Risk Measure under CRRA Preferences

• Rep. US investor can invest in domestic and international capital markets.

• CRRA preferences: U(c) = c1−γ−11−γ

, γ ≥ 0 is risk aversion coefficient.

• Stochastic Discount Factor (SDF) is Mt+1 = β(

ct+1

ct

)−γ

.

• Return on (risky) investment in market j is Rj ; risk-free rate is Rf .

No-arbitrage Euler equation for any asset j (incl. risk-free asset):

1 = Et [Mt+1Rjt+1] (1)

1 = Rft+1Et [Mt+1] (2)

(1) and (2) ⇒ Risk premium:

EtRjt+1 − Rft+1 = −covt (Mt+1,Rjt+1)

EtMt+1(3)

Intuition: Asset that pays off when MU is low demands higher risk premium.

Linearize SDF around uncond. mean and use (3) to derive testable prediction.

CRRA

–/ 27


5 10 15x 10

−5

0.06

0.08

0.1

0.12

cov(r,∆ c)

re

γ=907

0.5 1 1.5 2x 10

−4

0.06

0.08

0.1

0.12

0.14

cov(r,∆ c)

re

γ=873

0 1 2x 10

−4

0.06

0.08

0.1

0.12

0.14

cov(r,∆ c)

re

γ=868

−5 0 5x 10

−4

0

0.1

0.2

cov(r,∆ c)re

γ=493

CRRA

–/ 27


What if ∆c is iid and prefs are CRRA (simple version of our model)?

∆ct+1 = µc + σηt+1

∆dj,t+1 = µj + ϕjσuj,t+1 + πjσηt+1

⇒ E[Re

jt

]= γπjσ

2 and πjσ2 = cov (∆d ,∆c)

r̂ e

Portfolio r e cov (∆d ,∆c) γ = 10 535

1 11.60 0.00013 0.13 6.76

2 10.00 0.00011 0.11 5.63

3 6.93 0.00016 0.16 8.38

US 4.80 0.00018 0.18 9.54

Spread: 1-US 6.80 −0.00005 −0.05 −2.78

CRRA

–/ 27

Data: Moments—Annually Rebalanced Portfolios

Portfolio E [∆dt ]√

cov(∆dt+1,∆dt )

cov(∆ct+1,∆ct )cov (∆dt ,∆ct) std (∆dt)

1 −0.019 6.16 0.00010 0.082

2 −0.017 5.69 0.00012 0.072

3 −0.011 3.92 0.00015 0.058

US −0.006 2.19 0.00018 0.026

Observe:

• cov(∆d ,∆c) ≈ across portfolios

• std(∆d) falling in income/worker

• acov(∆d) falling in income/worker

Benchmark Moments

– / 27

Calibrated Model: Parameters—Annually Rebalanced Portfolios

Portfolio µd φ ϕd π

1 −0.019 6.16 7.12 −2.53

2 −0.017 5.69 6.36 −1.76

3 −0.011 3.92 5.59 −0.32

US −0.006 2.19 2.12 0.98

Observe: φj falling in income/worker.

Benchmark Parameters

– / 27

Predicted VS Actual Returns—Annually Rebalanced Portfolios


r̂ r r̂ r r̂ r

1 11.86 12.27 1 11.62 13.57 1 11.32 14.92

2 11.33 10.45 2 12.07 11.00 2 11.77 12.65

3 8.98 7.58 3 11.39 10.26 3 12.44 10.58

US 5.94 6.02 4 10.05 8.65 4 11.69 11.47

5 8.23 7.17 5 11.71 10.29

US 5.94 6.02 6 11.04 10.23

7 10.79 9.24

8 9.10 8.06

9 8.93 6.88

10 7.17 7.60

US 5.94 6.02

Average: 9.53 9.08 9.88 9.44 10.17 9.81

Spread: 1-US 5.92 6.25 5.68 7.54 5.38 8.90


corr (̂r , r) 0.95 0.88 0.79

Results

– / 27

Mean p-d ratios


p − d p − d p − d

1 2.11 1 2.09 1 2.03

2 2.10 2 2.06 2 2.14

3 2.31 3 2.19 3 2.19

US 2.73 4 2.17 4 1.96

5 2.40 5 2.17

US 2.73 6 2.21

7 2.10

8 2.25

9 2.30

10 2.56

US 2.73

Spread: 1-US: −0.62 −0.64 −0.70

Results

– / 27

Solving the Model

Standard approach; approximations to make exercise computationally feasible

Note that zt = log(

Pt

Dt

)is enough to characterize returns

• For each asset j , and asset that pays off aggregate consumption

To solve:

• Conjecture: zjt = A0j + A1jxt

• Approximate log returns: rjt+1 = κ0j + κ1jzjt+1 +∆djt+1 − zjt

where κ’s depend on z j

• Restrictions from Euler equation gives excess return (as in text) and rf rate

• Solve numerically for z j = A0j (z j ) and for A1j (z j )

Results

– / 27

List of Countries by Portfolio

Portfolio 1:Angola 1971

Bangladesh 1960

Benin 1980

Bhutan 1980

Bolivia 1951

Burkina Faso 1960

Burundi 1980

Cambodia 1971

Cameroon 1961

Cape Verde 1980

Central African Rep. 1980

Chad 1980

China 1953

Comoros 1980

Congo - Brazzaville 1980

Congo - Kinshasa 1971

El Salvador 1975

Gambia 1980

Ghana 1960

Guinea 1980

Honduras 1970

India 1960

Indonesia 1961

Ivory Coast 1961

Kenya 1960

Kyrgyzstan 1991

Laos 1980

Lesotho 1980

Liberia 1980

Madagascar 1961

Mali 1961

Mauritania 1980

Moldova 1991

Mongolia 1980

Nepal 1980

Niger 1961

Nigeria 1960

Pakistan 1960

Sao Tome and Pr. 1980

Senegal 1961

Tajikistan 1991

Tanzania 1961

Thailand 1960

Togo 1980

Uganda 1960

Vietnam 1971

Yemen 1990

Zambia 1960

Portfolio 2:Albania 1971

Argentina 1951

Armenia 1991

Azerbaijan 1991

Belarus 1991

Belize 1980

Bosnia 1991

Botswana 1980

Brazil 1951

Bulgaria 1971

Chile 1952

Colombia 1951

Costa Rica 1951

Djibouti 1980

Dominican Rep. 1960

Ecuador 1952

Fiji 1980

Georgia 1991

Grenada 1988

Guatemala 1951

Iran 1960

Jamaica 1960

Jordan 1960

Kazakhstan 1991

Latvia 1991

Malaysia 1960

Mauritius 1980

Morocco 1960

Namibia 1980

Panama 1969

Paraguay 1970

Peru 1951

Philippines 1960

Poland 1971

Portugal 1951

Romania 1989

Russia 1991

Saint Lucia 1971

Saint Vincent 1980

Serbia 1991

South Africa 1960

South Korea 1960

Sri Lanka 1960

Swaziland 1980

Syria 1961

Tunisia 1961

Turkey 1951

Turkmenistan 1991

Ukraine 1991

Uzbekistan 1991

– / 27

List of Countries by Portfolio

Portfolio 3:Antigua 2001

Australia 1951

Austria 1951

Bahamas 1973

Bahrain 1971

Barbados 1961

Belgium 1951

Brunei 1980

Canada 1951

Croatia 1991

Cyprus 1960

Czech Republic 1991

Denmark 1951

Estonia 1991

Finland 1951

France 1951

Gabon 1980

Germany 1970

Greece 1952

Hong Kong 1961

Hungary 1971

Iceland 1956

Ireland 1951

Israel 1960

Italy 1951

Japan 1951

Lithuania 1991

Luxembourg 1951

Macao 1980

Macedonia 1991

Mexico 1951

Montenegro 2005

Netherlands 1951

New Zealand 1951

Norway 1951

Oman 1971

Qatar 1971

Singapore 1961

Slovakia 1991

Slovenia 1991

Spain 1951

Sweden 1951

Switzerland 1951

Taiwan 1960

Trinidad and Tobago 1960

United Kingdom 1951

Uruguay 1960

Venezuela 1951

Portfolio 4:United States 1951

Data

– / 27

List of 69 Countries by Portfolio

Portfolio 1:Bangladesh 1960

Bolivia 1951

Cameroon 1961

China 1953

Ghana 1960

India 1960

Indonesia 1961

Ivory Coast 1961

Kenya 1960

Madagascar 1961

Nigeria 1960

Pakistan 1960

Philippines 1960

Senegal 1961

Sri Lanka 1960

Tanzania 1961

Thailand 1960

Uganda 1960

Zambia 1960

Portfolio 2:Argentina 1951

Brazil 1951

Chile 1952

Colombia 1951

Costa Rica 1951

Dominican Rep. 1960

Ecuador 1952

Greece 1952

Guatemala 1951

Iran 1960

Jamaica 1960

Japan 1951

Jordan 1960

Malaysia 1960

Mexico 1951

Morocco 1960

Peru 1951

Portugal 1951

South Africa 1960

South Korea 1960

Taiwan 1960

Tunisia 1961

Turkey 1951

Uruguay 1960

Venezuela 1951

Portfolio 3:Australia 1951

Austria 1951

Barbados 1961

Belgium 1951

Canada 1951

Cyprus 1960

Denmark 1951

Finland 1951

France 1951

Hong Kong 1961

Iceland 1956

Ireland 1951

Israel 1960

Italy 1951

Luxembourg 1951

Netherlands 1951

New Zealand 1951

Norway 1951

Singapore 1961

Spain 1951

Sweden 1951

Switzerland 1951

Trinidad and Tobago 1960

United Kingdom 1951

Portfolio 4:United States 1951

Results

– / 27

TS Returns on Capital: 34 Open Countries, Fixed Income Distribution

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18R

etur

n on

cap

ital


TS Plot

– / 27

TS Returns on Capital: 34 Open Countries

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16R

etur

n on

cap

ital


TS Plot

– / 27

TS Returns on Capital: 17 Very Open Countries, Fixed Income Distribution

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2R

etur

n on

cap

ital


TS Plot

– / 27

Return variance

std (∆d) std (r) std (̂r)

1 0.085 0.054 0.125

2 0.075 0.047 0.120

3 0.062 0.039 0.094

US 0.026 0.025 0.046

• Predicted variance > actual variance of returns

• Variance of cash flow growth > variance of returns

Results

– / 27

Details: SDF

In logs,

must+1 = θ log β −

θ

ψ∆ct+1 + (θ − 1) rct+1

where θ = 1−γ

1− 1ψ

and rct+1 is return on asset that pays consumption as dividend.

Model

– / 27

the risky capital of emerging marketsinasimonovska.weebly.com/uploads/1/3/2/5/1325220/... · the...

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