2012 international finance part 1.pdf
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International Finance
Part 1 – Introduction
Global Asset Classes and their Characteristics
Dr. Peter Oertmann
peter.oertmann@vescore.com
2© 2012 Peter Oertmann
Agenda
• Prologue: The asset allocation process
• Brush up: Risk and return
• Market parameters of global asset classes
• Some facts on stock market volatilities and correlations
3© 2012 Peter Oertmann
Agenda
• Prologue: The asset allocation process
• Brush up: Risk and return
• Market parameters of global asset classes
• Some facts on stock market volatilities and correlations
4© 2012 Peter Oertmann
Building blocks of an asset allocation process
Investor’s preferences
Portfolio
construction
Risks
Covariance
Matrix
Expected
returns
Selection of
asset classes
Global
investment
universe
Regulatory
restrictions
10%
15%
20%40%
5%
10%
Investor’s portfolio
5© 2012 Peter Oertmann
Aspects along the process
• Investor’s preferences
– Return target
– Liability structure
– Investment philosophy (benchmark-oriented, absolute return, …)
– Risk budget / capability (reserves, …)
• Regulatory restrictions (for pension plans, insurance companies, …)
– Restrictions on asset classes
– Restrictions on investment products
– Restrictions on risk management
• Selection of asset classes
– Availability of investment opportunities (asset manager, funds, derivatives, …)
– Investment philosophy
6© 2012 Peter Oertmann
Aspects along the process (cont.)
• Asset risks
– How to calculate the covariance matrix (data period, frequency, …)
• Expected asset returns
– Choice of an asset pricing approach
• Portfolio construction
– General structure (traditional, core-satellite, …)
– Choice of an optimization method (Markowitz, Black-Litterman, ...)
7© 2012 Peter Oertmann
Overview – Part 1
Asset returns, volatilities, and correlations
Regulatory
restrictions
Portfolio
construction
Risks
Covariance
Matrix
Expected
returns
Selection of
asset classes
Investor’s
preferences
Content
• Brush-up: Return calculation
• Data for international markets
• Empirical facts
8© 2012 Peter Oertmann
Overview – Part 2-4
IAPM, Multifactor Models, Return Forecasting, and Asset Pricing
Regulatory
restrictions
Portfolio
construction
Risks
Covariance
Matrix
Expected
returns
Selection of
asset classes
Investor’s
preferences
Content
• International parity relations
• Theoretical backgrounds
• Structures of IAPM
• Specification of multifactor models
• Application of multifactor models
• Forecasting returns
• Modeling of risk premiums
• Sentiment and asset returns
• Conditional asset pricing
• Market integration
9© 2012 Peter Oertmann
Overview – Part 5
Global Asset Allocation
Regulatory
restrictions
Portfolio
construction
Risks
Covariance
Matrix
Expected
returns
Selection of
asset classes
Investor’s
preferences
Content
• Strategic asset allocation
• Global tactical asset allocation
• Managing capital market risks
• Overlay management
10© 2012 Peter Oertmann
Overview – Part 6
Institutional Investing in the 2nd Decade
Regulatory
restrictions
Portfolio
construction
Risks
Covariance
Matrix
Expected
returns
Selection of
asset classes
Investor’s
preferences
Content
• Investing in a changing world
• BCG report: Trends in institutional
investing
• Structuring the risk-return space
• 1/N for asset allocation
• Commodities as an alternative asset class
11© 2012 Peter Oertmann
Literature
• Zimmermann/Drobetz/Oertmann: Global Asset Allocation, Wiley Finance,
2002, ISBN: 978-0-471-26426-2
• Selected references to books and
journals are given in the lecture
12© 2012 Peter Oertmann
Agenda
• Prologue: The asset allocation process
• Brush up: Risk and return
• Market parameters of global asset classes
• Some facts on stock market volatilities and correlations
13© 2012 Peter Oertmann
Brush-up:
Calculating returns
Time period
t-1 t
Simple return
Continuously compounded return
1t,i
1t,iitit
P
PPR
−
−−=
=
−1t,i
itit
P
PlnR
Example
Monthly returns
S&P 500 (USD) 2008-2010
-20,0%
-15,0%
-10,0%
-5,0%
0,0%
5,0%
10,0%
15,0%
20,0%
Jan
08
Mrz
08
Mai
08
Jul
08
Sep
08
Nov
08
Jan
09
Mrz
09
Mai
09
Jul
09
Sep
09
Nov
09
Jan
10
Mrz
10
Mai
10
Jul
10
Sep
10
Nov
10
14© 2012 Peter Oertmann
Brush-up:
Measuring investment risk
Variance of return
• Measure of dispersion
• Mean squared deviation of
returns form the mean return
Volatility
• Square root of variance
∑ −⋅==σ=
T
1t
2iiti
2i )RR(
T
1)R(Var
∑ −⋅==σ=
T
1t
2iitii )RR(
T
1)R(Std
Example
Monthly returns
S&P 500 (USD) 2008-2010
-20,0%
-15,0%
-10,0%
-5,0%
0,0%
5,0%
10,0%
15,0%
20,0%
Jan
08
Mrz
08
Mai
08
Jul
08
Sep
08
Nov
08
Jan
09
Mrz
09
Mai
09
Jul
09
Sep
09
Nov
09
Jan
10
Mrz
10
Mai
10
Jul
10
Sep
10
Nov
10
Monthly volatility 6.6%
15© 2012 Peter Oertmann
Brush-up:
Measuring investment risk (cont.)
Volatility from different perspectives
Transformation rules
• (daily volatility) × √(22 days)
= (monthly volatility)
• (monthly volatility) × √(12 months)
= (yearly volatility)
Volatility calculations
Yearly Monthly Daily
100.0% 28.9% 6.2%
80.0% 23.1% 4.9%
60.0% 17.3% 3.7%
50.0% 14.4% 3.1%
40.0% 11.5% 2.5%
30.0% 8.7% 1.8%
20.0% 5.8% 1.2%
15.0% 4.3% 0.9%
10.0% 2.9% 0.6%
16© 2012 Peter Oertmann
Brush-up:
Calculating portfolio returns
Approach
• Weighting the returns of portfolio components (single assets)
• Using simple (!) returns, not continuous returns
Two asset case
Mean:
N (many) asset case
Mean:
t22t11pt RwRwR ⋅+⋅=
∑ ⋅==
N
1iitipt RwR
2211p RwRwR ⋅+⋅=
∑ ⋅==
N
1iiip RwR
17© 2012 Peter Oertmann
Brush-up:
Measuring the risk of a portfolio
Starting point
• Simple weighting of the variances / volatilities of the portfolio components is not adequate
• Return covariances have to be taken into account
Portfolio variance
2 asset case
3 asset case
122122
22
21
21
2p ww2ww σ⋅⋅⋅+σ⋅+σ⋅=σ
233213311221
23
23
22
22
21
21
2p
ww2ww2ww2
www
σ⋅⋅⋅+σ⋅⋅⋅+σ⋅⋅⋅+
σ⋅+σ⋅+σ⋅=σ
18© 2012 Peter Oertmann
Brush-up:
Measuring the risk of a portfolio (cont.)
General formula of portfolio variance
Matrix notation
‚Double-sum‘ notation
[ ]
assets two between covariance
variance asset
asset an of weight portfoliowwhere
w
w
w
wwwwV'w
ij
ii
3
2
1
333231
332221
131211
3212p
σ
σ
σσσ
σσσ
σσσ
==σ
MM
L
L
∑ ∑ σ⋅⋅=σ= =
N
1i
N
1jijji
2p ww
19© 2012 Peter Oertmann
Brush-up:
Measuring the risk of a portfolio (cont.)
Crucial insights
• Portfolio risk is based on the variances of its components as well
as on the covariance between its components
• Covariances between the portfolio components contribute the largest
part to portfolio risk
Covariances dominate
Number of ...
Assets VAR‘s COV‘s
2 2 2
3 3 6
4 4 12
5 5 20
10 10 90
100 100 9900
1000 1000 999000
2500 2500 6247500
20© 2012 Peter Oertmann
Agenda
• Prologue: The asset allocation process
• Brush up: Risk and return
• Market parameters of global asset classes
• Some facts on stock market volatilities and correlations
21© 2012 Peter Oertmann
Relative sizes of world stock markets
Data source: Elroy Dimson, Paul Marsh and Mike Staunton, Triumph of the Optimists, 2002; Credit Suisse AG, 2011; own calculations
end-1899 end-2010
UK 30,5% 8,1%
USA 19,3% 41,4%
France 14,3% 3,9%
Germany 6,9% 3,2%
Japan 4,0% 8,2%
Canada 1,8% 4,1%
Russia 3,9%
Belgium 3,8%
Austria-Hungary 3,5%
Netherlands 1,6%
Italy 1,6%
Australia 3,4%
Switzerland 3,0%
Spain 1,4%
Sweden 1,3%
Others 3,7% 16,8%
5,2%
5,1%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
UK USA France Germany Japan Canada
end-1899
end-2010
22© 2012 Peter Oertmann
Sector weightings within US stock market
Data source: Elroy Dimson, Paul Marsh and Mike Staunton, Triumph of the Optimists, 2002
US Sectors
end-1899 end-2000
Railroads 62,8% 0,2%
Banks and Finance 6,7% 12,9%
Mining 0,0% 0,0%
Textiles 0,7% 0,2%
Iron, Coal, Steel 5,2% 0,3%
Breweries and Distillers 0,3% 0,4%
Utilities 4,8% 3,8%
Telegraph and Telephone 3,9% 5,6%
Insurance 0,0% 4,9%
Other Transport 3,7% 0,5%
Chemicals 0,5% 1,2%
Food Manufacturing 2,5% 1,2%
Retailers 0,1% 5,6%
Tobacco 4,0% 0,8%
Sectors that were small in 1900 4,8% 62,4%
Weight
0%
10%
20%
30%
40%
50%
60%
70%
Ra
ilro
ad
s
Ba
nks a
nd
Fin
an
ce
Min
ing
Te
xtile
s
Iro
n,
Co
al, S
tee
l
Bre
we
rie
s a
nd
Dis
tille
rs
Utilit
ies
Te
legra
ph
an
d
Te
lep
hon
e
Insu
ran
ce
Oth
er
Tra
nsp
ort
Che
mic
als
Fo
od
Ma
nu
factu
rin
g
Re
taile
rs
To
ba
cco
Se
cto
rs t
hat
we
re
sm
all
in 1
90
0
end-1899
end-2000
23© 2012 Peter Oertmann
Returns and risk
of international stock and fixed income markets from 1900 to 2010
Equities Bonds Bills Equities Bonds Bills Equities Bonds Bills
USA New York Stock Exchange, est. 1792 6,3% 1,8% 1,0% 9,4% 4,8% 3,9% 20,3% 10,2% 4,7%
Canada Toronto Stock Exchange, est. 1861 5,9% 2,1% 1,6% 9,1% 5,2% 4,7% 17,2% 10,4% 4,9%
Belgium Brussels stock exchange, est. 1801 2,5% -0,1% -0,3% 8,0% 5,2% 5,0% 23,6% 12,0% 8,0%
Finland Helsinki Stock Exchange, est. 1912 5,4% -0,2% -0,5% 13,1% 7,1% 6,8% 30,3% 13,7% 11,9%
France Paris stock exchange dates back to 1724 3,1% -0,1% -2,8% 10,5% 7,1% 4,2% 23,5% 13.0% 9,6%
Germany German stock exchange dates back to 1685 3,1% -1,9% -2,4% 8,3% 2,8% 2,3% 32,2% 15,5% 13,2%
Ireland Stock exchanges in Dublin and Cork date back to 1793 3,8% 0,9% 0,7% 8,2% 5,2% 5,0% 23,2% 14,9% 6,7%
Italy Stock exchange in Milan dates back to 1808 2,0% -1,7% -3,6% 10,6% 6,7% 4,5% 29,0% 14,1% 11,5%
Netherlands Amsterdam stock exchange dates back to 1611 5,0% 1,4% 0,7% 8,0% 4,4% 3,6% 21,8% 9,4% 5,0%
Spain Madrid Stock Exchange, est. 1831 3,6% 1,3% 0,3% 9,6% 7,2% 6,2% 22,3% 11,8% 5,9%
Denmark Copenhagen Stock Exchange, est. 1808 5,1% 3,0% 2,3% 9,2% 7,1% 6,2% 20,9% 11,7% 6,0%
Norway Oslo Stock Exchange, est. 1819 4,2% 1,7% 1,2% 8,1% 5,5% 4,9% 27,4% 12,2% 7,2%
Sweden Stockholm Stock Exchange, est. 1863 6,3% 2,4% 1,9% 10,1% 6,1% 5,5% 22,9% 12,4% 6,8%
Switzerland Swiss stock markets date back to 1850 4,2% 2,1% 0,8% 6,6% 4,5% 3,1% 19,8% 9,3% 5,0%
UK Stock trading dates back to 1698 5,3% 1,4% 1,0% 9,5% 5,4% 5,0% 20,0% 13,7% 6,4%
Australia Australian Securities Exchange (ASX), est. 1861 7,4% 1,5% 0,7% 11,6% 5,4% 4,6% 18,2% 13,2% 5,4%
Japan Tokyo stock exchange, est. 1878 3,8% -1,1% -1,9% 11,1% 5,8% 5,0% 29,8% 20,1% 13,9%
New Zealand New Zealand Exchange dates back to 1870 5,8% 2,0% 1,7% 9,8% 5,8% 5,5% 19,7% 9,0% 4,7%
South Africa Johannesburg stock exchange (JSE), est. 1887 7,3% 1,8% 1,0% 12,6% 6,8% 6,0% 22,6% 10,4% 6,2%
World GDP-weighted indices, denominated in USD 5,5% 1,6% 1,0% 8,6% 4,7% 3,9% 17,7% 10,4% 4,7%
Asia-Pacific
Africa
North America
Europe (Eurozone)
Europa (Others)
Real return Nominal return Standard deviation
Data source: Elroy Dimson, Paul Marsh and Mike Staunton, Triumph of the Optimists, 2002; Credit Suisse AG, 2011; Wikipedia; own calculations
24© 2012 Peter Oertmann
Real returns
of international stock and fixed income markets from 1900 to 2010
Data source: Elroy Dimson, Paul Marsh and Mike Staunton, Triumph of the Optimists, 2002; Credit Suisse AG, 2011; own calculations
-4%
-2%
0%
2%
4%
6%
8%
USA CAN BEL FIN FRA GER IRE ITA NET SPA DEN NOR SWE SWI UKI AUS JAP NZE SAF WRL
Equities Bonds Bills
25© 2012 Peter Oertmann
Standard deviations
of international stock and fixed income markets from 1900 to 2010
Data source: Elroy Dimson, Paul Marsh and Mike Staunton, Triumph of the Optimists, 2002; Credit Suisse AG, 2011; own calculations
0%
5%
10%
15%
20%
25%
30%
35%
USA CAN BEL FIN FRA GER IRE ITA NET SPA DEN NOR SWE SWI UKI AUS JAP NZE SAF WRL
Equities Bonds Bills
26© 2012 Peter Oertmann
Equity risk premiums of international stock markets
1900-2010 1961-2010 2001-2010
USA 4,4% 2,6% -3,9%
Canada 3,7% 1,7% -0,9%
Belgium 2,6% 1,0% -4,7%
Finland 5,6% 4,6% -7,3%
France 3,2% -0,9% -6,0%
Germany 5,4% -0,1% -4,3%
Ireland 2,9% 3,5% -6,2%
Italy 3,7% -1,9% -7,3%
Netherlands 3,5% 3,3% -7,1%
Spain 2,3% 3,4% 1,3%
Denmark 2,0% 1,2% 0,9%
Norway 2,5% 2,8% 3,1%
Sweden 3,8% 4,8% 0,3%
Switzerland 2,1% 2,1% -4,2%
UK 3,9% 3,4% -1,3%
Australia 5,9% 3,5% 2,7%
Japan 5,0% -1,4% -5,2%
New Zealand 3,8% 2,2% 1,1%
South Africa 5,5% 6,9% 5,8%
World 3,8% 1,2% -4,0%
Asia-Pacific
Africa
Equity risk premium vs. bonds
North America
Europe (Eurozone)
Europa (Others)
0,0%
1,0%
2,0%
3,0%
4,0%
5,0%
6,0%
7,0%
US
A
Ca
nad
a
Belg
ium
Fin
lan
d
Fra
nce
Ge
rmany
Ire
lan
d
Italy
Neth
erlands
Spain
Denm
ark
Norw
ay
Sw
eden
Sw
itze
rla
nd
UK
Au
str
alia
Japa
n
New
Zeala
nd
Sou
th A
fric
a
1900-2010
Data source: Elroy Dimson, Paul Marsh and Mike Staunton, Triumph of the Optimists, 2002; Credit Suisse AG, 2011; own calculations
27© 2012 Peter Oertmann
Stock-bond correlations
rolling over a window of 60 months for real returns
Inflation accelerating
Bonds = save haven
Bonds = save haven
Data source: Credit Suisse Global Investment Returns Yearbook 2011, Credit Suisse AG
28© 2012 Peter Oertmann
Stock-bond correlations
over various time horizons for real returns
Data source: Credit Suisse Global Investment Returns Yearbook 2011, Credit Suisse AG (and Antti Ilmanen)
29© 2012 Peter Oertmann
Correlation “heat map”
of international asset classes – 2001-2011
Asset Class 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
1 MSCI World ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
2 MSCI EMU ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
3 MSCI Switzerland ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
4 MSCI UK ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
5 MSCI USA ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
6 MSCI Japan ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
7 MSCI EM East Europe ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
8 MSCI EM Latin America ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
9 MSCI EM Asia ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
10 iboxx € Sovereigns ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
11 iboxx € Corporates ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
12 BarCap Inflation Linked Bond Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
13 JPM EMBI ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
14 ML Global High Yield BB-B Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
15 Global Convertibles ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
16 DJ UBS Commodities Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
17 CYD MarketNeutral+ Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
18 HFRI Fund of Fund Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
19 EPRA/NAREIT Global Real Estate Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
20 Macquarie Global Infrastructure Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
21 LPX Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
Source: Vescore
30© 2012 Peter Oertmann
Correlation “heat map”
of international asset classes – various time periods
Source: Vescore
Asset Class 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
1 MSCI World ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
2 MSCI EMU ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
3 MSCI Switzerland ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
4 MSCI UK ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
5 MSCI USA ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
6 MSCI Japan ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
7 MSCI EM East Europe ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
8 MSCI EM Latin America ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
9 MSCI EM Asia ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
10 iboxx € Sovereigns ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
11 iboxx € Corporates ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
12 BarCap Inflation Linked Bond Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
13 JPM EMBI ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
14 ML Global High Yield BB-B Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
15 Global Convertibles ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
16 DJ UBS Commodities Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
17 CYD MarketNeutral+ Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
18 HFRI Fund of Fund Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
19 EPRA/NAREIT Global Real Estate Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
20 Macquarie Global Infrastructure Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
21 LPX Index ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
2001-2006
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
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## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
2006-2011
31© 2012 Peter Oertmann
Summary of facts
• Stock markets considerably changed since 1900
• International stock markets returned 2.5 to 7.4% on a yearly basis in real terms
over the period 1900 to 2010, the return of the “world market” was 5.5%
• The long-term equity risk premium is positive, the “world market” equity risk premium is 3.8% over the period 1900 to 2010
• Over the first decade of the 21st century (2000-2010) the equity risk premium was significantly negative for many stock markets
• The average long-term correlation between stock and bond markets is positive
with a coefficient of 0.24
• Bonds diversify stock portfolios, especially in times of an increased risk aversion;
but inflation shocks lead to a higher co-movement between stocks and bonds
32© 2012 Peter Oertmann
Agenda
• Prologue: The asset allocation process
• Brush up: Risk and return
• Market parameters of global asset classes
• Some facts on stock market volatilities and correlations
33© 2012 Peter Oertmann
A study for 13 stock markets
Source: Global Asset Allocation, Chapter 3
Research questions
• Does stock market risk depend on market conditions?
• How do volatility and correlation interact?
• Is stock market risk related to economic conditions?
34© 2012 Peter Oertmann
Descriptive statistics for the stock markets in the study
Source: Global Asset Allocation, Chapter 3
35© 2012 Peter Oertmann
Correlations over the sample period
Source: Global Asset Allocation, Chapter 3
36© 2012 Peter Oertmann
Up and down volatility
Source: Global Asset Allocation, Chapter 3
37© 2012 Peter Oertmann
Correlation and volatility
Approach of Solnik, Boucrelle, and Le Fur (1996)
• Regression of 36-month moving correlation between two countries on both countries’ 36-month moving volatilities
• Using monthly innovations to correct for autocorrelation
t,ijt,it,jt,ij ubba +σ∆⋅+σ∆⋅+=ρ∆21
Source: Global Asset Allocation, Chapter 3
38© 2012 Peter Oertmann
Correlation and volatility – U.S. vs. other markets
Source: Global Asset Allocation, Chapter 3
39© 2012 Peter Oertmann
Correlation and volatility – Switzerland vs. other markets
Source: Global Asset Allocation, Chapter 3
40© 2012 Peter Oertmann
Correlation and average volatility
Source: Global Asset Allocation, Chapter 3
41© 2012 Peter Oertmann
Semi-correlations – “up-up” vs. “down-down”
Source: Global Asset Allocation, Chapter 3
42© 2012 Peter Oertmann
Correlations during expansions and recessions
Source: Global Asset Allocation, Chapter 3
43© 2012 Peter Oertmann
Empirical evidence
• Stock market volatility is higher when markets go down
• In periods of high volatility, stock markets become more correlated – and in periods of low volatility, they are less correlated
• Higher correlations are detected when equity market go down simultaneously, as well as when real economic activity is shrinking
Source: Global Asset Allocation, Chapter 3
44© 2012 Peter Oertmann
Implications for investors
• Optimal portfolios are unstable because market parameters are time-varying
• Bad news lead to higher volatility than good news
• International diversification benefits seem to vanish in exactly those market
environments when they are most strongly needed
• Widely used portfolio risk measures such as VaR, or shortfall, are affected by asymmetric parameters
Source: Global Asset Allocation, Chapter 3
45© 2012 Peter Oertmann
Literature
• Global Asset Allocation: Chapter 3
• Dimson, E., P. Marsh, and M. Staunton (2002): Triumph of the Optimists, Princeton University Press
• Dimson, E. et al. (2011): Credit Suisse Global Investment Returns, Credit Suisse AG
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