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Analyzing Contagion and Tail Dependence in Global Real Estate Markets using NonParametric Flexible Copulas
Linköping University, Department of Management and Engineering, Economics. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0002-8145-1000
IPAG Business School, Paris, France.
Linköping University, Department of Management and Engineering, Economics. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Management and Engineering, Economics. Linköping University, Faculty of Arts and Sciences.
2017 (English)Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

The global financial crisis and the collapse of the collateralized debt obligation (CDO) market have brought increased attention to the proper modeling of housing price co-movements worldwide. We aim at detecting possible contagion effects in international real estate markets while accommodating dependence during extreme tail events. We propose a novel copula based approach incorporating second-moment effects that not only accounts for asymmetric tail dependence, but also allows for time-varying correlation in price movements. Unlike previous studies wherein static copula-based models are utilized, we extend our methodology by employing nonparametric copulas with the adjustment of flexible specification. Common Gaussian or mixture copulas lack the required tail features to capture the empirical stylized facts in housing markets. We proved the lack of monotonicity imposed by parametric methods was evidently not supported by our data. Using monthly data in seven major global markets, we confirm that prices do exhibit correlations that change over time, whilst more importantly their tail dependence structure for extreme losses strengthens in the midst of market turmoil.

We indicated that especially during downturns, CDOs do not provide the level of diversification widely assumed before the subprime crisis. Information on tail dependence would better allow policy makers to anticipate real estate prices on a global scale.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Real estate markets, Non-parametric Copula, Co-movement, United States, Emerging Markets
National Category
Economics
Identifiers
URN: urn:nbn:se:liu:diva-142327OAI: oai:DiVA.org:liu-142327DiVA: diva2:1152719
Conference
SEM - The Society for Economic Measurement, 4th Annual Conference July 26-2, 2017, MIT, Boston
Available from: 2017-10-26 Created: 2017-10-26 Last updated: 2017-11-21Bibliographically approved

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Sjö, BoSiverskog, JonathanUddin, Gazi Salah

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  • apa
  • harvard1
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Language
  • de-DE
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