Modeling different kinds of spatial dependence in stock returns

被引:36
作者
Arnold, Matthias [1 ]
Stahlberg, Sebastian [1 ]
Wied, Dominik [1 ]
机构
[1] TU Dortmund, Fak Stat, D-44221 Dortmund, Germany
关键词
GMM estimation; Heteroscedasticity; Spatial dependence; Stock returns; Value at Risk;
D O I
10.1007/s00181-011-0528-2
中图分类号
F [经济];
学科分类号
02 ;
摘要
The paper modifies previously suggested GMM approaches to spatial autoregression in stock returns. Our model incorporates global dependencies, dependencies inside industrial branches and local dependencies. As can be seen from Euro Stoxx 50 returns, this combination of spatial modeling and finance allows for superior risk forecasts in portfolio management.
引用
收藏
页码:761 / 774
页数:14
相关论文
共 23 条
[11]   Measuring the effects of geographical distance on stock market correlation [J].
Eckel, Stefanie ;
Loeffler, Gunter ;
Maurer, Alina ;
Schmidt, Volker .
JOURNAL OF EMPIRICAL FINANCE, 2011, 18 (02) :237-247
[12]   Spatial linkages in international financial markets [J].
Fernandez, Viviana .
QUANTITATIVE FINANCE, 2011, 11 (02) :237-245
[13]   LARGE SAMPLE PROPERTIES OF GENERALIZED-METHOD OF MOMENTS ESTIMATORS [J].
HANSEN, LP .
ECONOMETRICA, 1982, 50 (04) :1029-1054
[14]   ASYMPTOTIC PROPERTIES OF NON-LINEAR LEAST SQUARES ESTIMATORS [J].
JENNRICH, RI .
ANNALS OF MATHEMATICAL STATISTICS, 1969, 40 (02) :633-&
[15]  
Jorion P, 2001, VALUE RISK NEW BENCH, P2
[16]   Panel data models with spatially correlated error components [J].
Kapoor, Mudit ;
Kelejian, Harry H. ;
Prucha, Ingmar R. .
JOURNAL OF ECONOMETRICS, 2007, 140 (01) :97-130
[17]  
Kelejian Harry H, 2010, J Econom, V157, P53
[18]   A generalized moments estimator for the autoregressive parameter in a spatial model [J].
Kelejian, HH ;
Prucha, IR .
INTERNATIONAL ECONOMIC REVIEW, 1999, 40 (02) :509-533
[19]   EFFICIENT GMM ESTIMATION OF HIGH ORDER SPATIAL AUTOREGRESSIVE MODELS WITH AUTOREGRESSIVE DISTURBANCES [J].
Lee, Lung-fei ;
Liu, Xiaodong .
ECONOMETRIC THEORY, 2010, 26 (01) :187-230
[20]  
LeSage J, 2009, STAT TEXTB MONOGR, P1