Risk Management in International Real Estate and Capital Markets

The present thesis aims at explaining selected aspects of the quantitative risk management of direct as well as securitized real estate investments. Paper 1: The relationship between domestic and global Economic Political Uncertainty and European Direct Commercial Real Estate Returns The aim of the study is to investigate the impact of domestic as well as global economic political uncertainty on direct real estate returns at the European City-level. The empirical study uses OLS estimation for a European direct real estate panel data set containing 20 cities across 9 European countries, with quarterly observations from Q1/2008 – Q3/2018. After controlling for empirically proven explanatory covariates of total returns, the model is extended by proxies for domestic and global political uncertainty. The study finds c.p., on average a statistically significant lagged negative influence of domestic economic political uncertainty on European direct commercial property total returns. Global economic political uncertainty c.p. positively affects total returns, indicating a “safe haven effect”. Paper 2: Do Cross-Border Investors Benchmark Commercial Real Estate Markets? Evidence from Relative Yields and Risk Premia for a European Investment Horizon The purpose of the study is to introduce a new perspective on determinants of cross-border investments in commercial real estate, namely the relative attractiveness of a target market. So far, the literature has analyzed only absolute measures of investment attractiveness as determinants of cross-border investment flows. The empirical study uses a classic OLS estimation for a European panel data set containing 28 cities in 18 countries, with quarterly observations from Q1/2008 – Q3/2018. After controlling for empirically proven explanatory covariates, the model is extended by the new relative measurement based on relative yields/cap rates and relative risk premia. Additionally, the study applies a generalized additive mixed model, to investigate a potentially nonlinear relationship. The study finds on average a c.p., statistically significant lagged influence of the proxy for relative attractiveness. Nonetheless, a differentiation is needed; relative risk premia are statistically significant, whereas relative yields are not. Moreover, the generalized additive mixed model confirms a nonlinear relationship for relative risk premia and cross-border transaction volumes. Paper 3: Volatility Targeting for US Equity REITs – A strategy for Minimizing Extreme Downside Risk? The study examines the feasibility of the so-called Volatility Targeting investment style to minimize extreme downside risk for US Equity REITs. The empirical study applies a two-stage approach: First, a back test of buy and hold, and VT based on various volatility estimators for each equity REIT security between 01/01/1999 and 01/01/2019 is performed. Subsequently, a mean-CVaR-optimization for the entire data set as well as the different equity REIT subclasses is carried out. The study finds CVaR reductions of the Volatility Targeting strategy in comparison to buy and hold across the majority of subclasses, as well as the entire sample. Interestingly, these improvements differ across the REIT subclasses and volatility estimators. Paper 4: AR-GARCH-EVT-Copula for Securitized Real Estate: An approach to improving risk forecasts? This study presents a quantitative analysis of the so-called AR-GARCH-EVT-Copula model aimed at forecasting risk metrics for multi-asset portfolios, including securitized real estate positions. The model incorporates a non-linear dependence structure and time-varying volatility in asset returns. Accordingly, an empirical study using data from six major global markets is carried out. The approach is applied in order to forecast risk metrics, in comparison to classical methods like historical simulation and variance-covariance models. Forecasts are then compared with realized returns, in order to calculate hit sequences and conduct statistical interference on the respective models. It is empirically shown that, the AR-GARCH-EVT-Copula model provides a superior forecast concerning risk metrics. This is mainly due to the usage of copulas, allowing us to individually model the dependence structure of random variables. Back testing and test results confirm the superiority of our model in comparison with classic methods such as historical simulation and Variance-Covariance approach. The decomposition of the univariate and multivariate models of the target model reveal the necessity to allow for high order and thus long-lasting autoregressive modelling as well as asymmetric tail dependence and rotated copulae across different portfolios