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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

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Volatility Spillovers and Asymmetry in Real Estate Stock Returns

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  1. Volatility Spillovers and Asymmetry in Real Estate Stock Returns KustrimReka 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

  2. 1 Purpose of Study (1) • Portfolio diversification with real estate stocks • Advantages: low unit value and liquidity • Aims of this study are twofold: • 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

  3. 1 Purpose of Study (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

  4. 2Literature • 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)

  5. 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

  6. 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)

  7. 4Methods (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)

  8. 4Methods (2) • Equations: where , with ~ N(0,I) thus where

  9. 4Methods (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

  10. 5Empirical 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)

  11. 5Empirical Results (2)

  12. 5Empirical 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)

  13. 5Empirical Results (4)

  14. 5Empirical 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

  15. 5Empirical Results (6)

  16. 6Further Analysis: Copulas (1) • Lower tail dependence based on the Clayton copula (analysis of extreme negative events): • Lower tail dependence formula: • For the entire period and 4 sub-periods

  17. 6Further 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

  18. 7Concluding 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)

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