Analyzing Hedging Strategies for Fixed Income Portfolios: A Bayesian Approach for Model Selection

During the recent European sovereign debt crisis, returns on EMU government bond portfolios experienced substantial volatility clustering, leptokurtosis and skewed returns, as well as correlation spikes. Asset managers invested in European government bonds had to derive new hedging strategies to deal with the changing return properties and the higher level of uncertainty. In this market environment, conditional time series approaches such as GARCH models might be better suited to achieve a superior hedging performance relative to unconditional hedging approaches such as OLS. The aim of this study is to develop and evaluate improved hedging strategies for EMU bond portfolios for non-crises and crises periods. The empirical analysis includes OLS, constant conditional correlation (CCC), and dynamic conditional correlation (DCC) multivariate GARCH models. In addition, we introduce a Bayesian composite hedging strategy, attempting to combine the strengths of OLS and GARCH models, thereby endogenizing the dilemma of selecting the best estimation model. During the European sovereign debt crisis yield spreads among EMU member countries widened and the well established hedging instruments such as the Bund futures suddenly were inapt to minimize the risk exposure of European government bond portfolios. As a consequence, Eurex introduced new future contracts on Italian government securities (BTP). Therefore, in this study we analyze single and composite hedging strategies with the German Bund and the Italian BTP futures contracts empirically and evaluate the hedging effectiveness in an out-of-sample setting. Our empirical results demonstrate that the Bayesian composite hedging strategy was particularly superior during the recent sovereign debt crisis period. Current Version: October 10, 2014