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Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection

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  • Bessler, Wolfgang
  • Leonhardt, Alexander
  • Wolff, Dominik

Abstract

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 changing return properties and higher levels of uncertainty. In this 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 test innovative hedging strategies for EMU bond portfolios for non-crisis and crisis periods. We analyze single and composite hedges with the German Bund and the Italian BTP futures contracts and evaluate the hedging effectiveness in an out-of-sample setting. The empirical analysis includes OLS, constant conditional correlation (CCC), and dynamic conditional correlation (DCC) multivariate GARCH models. We also 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. Our empirical results demonstrate that the Bayesian composite hedging strategy achieves the highest hedging effectiveness and compares particularly favorable to OLS during the recent sovereign debt crisis. However, capturing these benefits requires low transactions cost and efficiently functioning futures markets.

Suggested Citation

  • Bessler, Wolfgang & Leonhardt, Alexander & Wolff, Dominik, 2016. "Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 239-256.
  • Handle: RePEc:eee:finana:v:46:y:2016:i:c:p:239-256
    DOI: 10.1016/j.irfa.2015.11.013
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    2. Mustafa Demirel & Gazanfer Unal, 2020. "Applying multivariate-fractionally integrated volatility analysis on emerging market bond portfolios," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-29, December.
    3. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2017. "The kidnapping of Europe: High-order moments' transmission between developed and emerging markets," Emerging Markets Review, Elsevier, vol. 31(C), pages 96-115.
    4. Bessler, Wolfgang & Conlon, Thomas & Huan, Xing, 2019. "Does corporate hedging enhance shareholder value? A meta-analysis," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 222-232.
    5. Mo, Guoli & Tan, Chunzhi & Zhang, Weiguo & Liu, Fang, 2019. "International portfolio of stock indices with spatiotemporal correlations: Can investors still benefit from portfolio, when and where?," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 168-183.
    6. Clancy, Daragh & Dunne, Peter G. & Filiani, Pasquale, 2019. "Liquidity and tail-risk interdependencies in the euro area sovereign bond market," Research Technical Papers 11/RT/19, Central Bank of Ireland.

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    More about this item

    Keywords

    Sovereign debt crisis; Bond portfolio management; Interest rate futures; MGARCH; Bayesian composite hedging; Hedge ratio and hedging effectiveness;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G19 - Financial Economics - - General Financial Markets - - - Other

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