Download Volatility Spillovers and Asymmetry in Real Estate Stock Returns

Volatility Spillovers and Asymmetry in
Real Estate Stock Returns
Kustrim Reka
University of Geneva (Switzerland)
Martin Hoesli
University of Geneva (Switzerland), University of Aberdeen (U.K.),
and Bordeaux Management School (France)
European Real Estate Society Annual Conference
Milano, Italy
23-26 June 2010
1
1 Purpose of Study (1)
• Portfolio diversification with real estate stocks
• Advantages: low unit value and liquidity
• Aims of this study are twofold:
1) National analysis
•
•
Volatility spillovers from the stock market to the real
estate stock market (short-run analysis)
Motivated by the fact that indirect real estate are stocks
by definition
2
1 Purpose of Study (2)
2) International analysis
•
•
•
Linkages between the world securitized real estate
market and selected domestic real estate security
markets (short-run analysis)
Both unhedged and hedged currency risk strategies
Motivated by the fact that the investors increasingly seek
to go international on real estate markets
• Also verify the presence of leverage effects
(asymmetry) in market contagions
3
2 Literature
• Stocks – Real estate stocks:
– Ling and Naranjo (1999)
– Stevenson (2002)
– Cotter and Stevenson (2006)
• World RE stocks – Domestic RE stocks:
–
–
–
–
Liow, Ooi and Gong (2005)
Michayluk, Wilson and Zurbruegg (2006)
Li and Yung (2007)
Liow et al. (2009)
4
3 Data (1)
• Sources: EPRA/NAREIT (real estate stocks)
and Datastream (stocks)
• Daily closing prices and market capitalizations
covering the period 01/01/1990 – 12/31/2009
(5,200 observations)
• Logarithmic returns calculated
• 3 countries: U.S., U.K. and Australia
5
3 Data (2)
• World index: excluding the market studied
• Construction of the international indices:
Ratio = Market Caps Domestic / Market Caps World
World index ex_domestic market =
(World index – Ratio x Domestic index)
6
4 Methods (1)
• Bivariate GARCH framework
• More precisely, we use an asymmetric BEKK
(Baba-Engle-Kraft-Kroner) specification of the
variance-covariance matrix (Engle and Kroner,
1995)
• A leverage term is added according to the model
of Glosten, Jagannathan and Runkle (1993)
• Mean equation modeled as a VAR(1)
7
4 Methods (2)
• Equations:
Rt  K  DRt 1   t
1/ 2
where  t  H t zt , with zt ~ N(0,I)
thus  t t 1 ~ N (0, Ht )
H t  CC' A' t 1 't 1 A  B' H t 1B  N ' t 1't 1 N
where
 a11 a12 
c11 c12 
b11 b12 
 n11 n12 
A
C
B
N 




a21 a22 
 0 c22 
b21 b22 
n21 n22 
8
4 Methods (3)
• Parameters estimated by Quasi-Maximum
Likelihood (under normality assumption)
• Robust standard errors calculated (Bollerslev
and Wooldridge, 1992) in order to obtain
consistent results (misspecification of the
density function)
• Further analysis: conditional correlation from
the estimates of the previous model
9
5 Empirical Results (1)
• National Analysis:
– Volatility spillovers (cross-market impact): U.S. and
Australia (both directions)
– Less obvious for the U.K.
– Asymmetry in the U.K. and Australian cases; less
apparent in the U.S.
– Conditional correlations: high coefficients and
upward trend from 2006 (financial crisis)
10
5 Empirical Results (2)
1
0.8
Coefficient
0.6
0.4
0.2
0
USA
UK
AUS
-0.2
-0.4
1993
1996
1999
2002
2005
2008
Dates
11
5 Empirical Results (3)
• International Analysis (unhedged):
– Volatility spillovers (cross-market impact): U.K.
and Australia (from the local to the worldwide
market); presence of continental factors
– The U.S. market: more isolated
– Asymmetry in the U.K. and Australian cases
– Conditional correlations: high coefficients for the
U.K. and Australia (increase during crisis period)
12
5 Empirical Results (4)
1
0.8
Coefficient
0.6
0.4
0.2
0
USA
UK
AUS
-0.2
-0.4
1993
1996
1999
2002
2005
2008
Dates
13
5 Empirical Results (5)
• International Analysis (hedged):
– Hedging strategy: against pound for the U.S.
investor and U.S. dollar for the U.K. and Australian
investors
– Similar results for the U.S.
– U.K. and Australian results are more sensitive to the
exchange rate; the domestic market impact and the
asymmetry diminish (currency risk: opposite effects)
– Conditional correlations: quite similar patterns
14
5 Empirical Results (6)
1
0.8
Coefficient
0.6
0.4
0.2
0
USA
UK
AUS
-0.2
-0.4
1993
1996
1999
2002
2005
2008
Dates
15
6 Further Analysis: Copulas (1)
• Lower tail dependence based on the Clayton
copula (analysis of extreme negative events):
Cc (u, v, )  (u   v   1) 1 / 
• Lower tail dependence formula:
C ( q, q )
q0
q
 L  lim P(U  q V  q)  lim P(V  q U  q)  lim
q0
q0
• For the entire period and 4 sub-periods
16
6 Further Analysis: Copulas (2)
• Strong lower tail dependence between stocks
and real estate stocks
• Strong lower tail dependence between the
worldwide market and the U.K. and Australian
markets (both strategies)
• More important dependence when there is a
crisis in a sub-period
• Thus, consistent results with the previous
analysis
17
7 Concluding Remarks
• In general, presence of cross-market impacts
(volatility transmission) and leverage effects
(both in the national and international analyses)
• Continental factors for the international analysis
(developments possible)
• Higher connections in periods of financial
turmoil (conditional correlations & copula
analysis)
18