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Investor Sentiment and Crash Risk in Safe Havens

Author

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  • Adnen Ben Nasr
  • Matteo Bonato
  • Riza Demirer
  • Rangan Gupta

Abstract

This study examines the relationship between investor sentiment and intraday return dynamics for safe haven assets, with a particular focus on crash risk in these assets. Examining intraday returns for a wide range of safe havens proposed in the literature, we find that shocks to investor sentiment have a significant effect on safest havens, while the sentiment is heterogeneous both in terms of its size and direction. While the strongest effects of sentiment shocks are observed in the case of Gold, Swiss Francs and Japanese Yen, interestingly, we find that oil stands out from the rest of the pack, responding negatively to sentiment shocks, suggesting that positive shocks to sentiment (i.e. high fear) increase crash risk for this asset. Our findings also point to intra-safe haven spillover effects, with oil exhibiting a markedly different pattern. Investment and hedging implications are discussed next.

Suggested Citation

  • Adnen Ben Nasr & Matteo Bonato & Riza Demirer & Rangan Gupta, 2019. "Investor Sentiment and Crash Risk in Safe Havens," Journal of Economics and Behavioral Studies, AMH International, vol. 10(6), pages 97-108.
    • Handle: RePEc:rnd:arjebs:v:10:y:2019:i:6:p:97-108
    DOI: 10.22610/jebs.v10i6A.2666
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    Cited by:

    1. Bonato, Matteo & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2018. "Gold futures returns and realized moments: A forecasting experiment using a quantile-boosting approach," Resources Policy, Elsevier, vol. 57(C), pages 196-212.
    2. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    3. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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