Artikel

Modelling stock returns and risk management in the shipping industry

We estimate the impact of macroeconomic risk factors on shipping stock returns, using a quantile regression (QR) model. We regress the excess return of a portfolio for the container, dry bulk, chemical/gas, oil tanker, and diversified shipping sectors on the world market portfolio excess return, volatility index, and changes in the oil price, exchange rate, and interest rate. The sensitivities of stock returns to the risk factors differ across quantiles and shipping segments and are found to be significant for the volatility index, world market portfolio return, exchange rate, and changes in long-term interest rate with variation over quantiles. This provides evidence of asymmetric and heterogeneous dependence between stock returns and certain macroeconomic risk variables. The results of the study also suggest that standard OLS regression is inadequate to uncover the risk-return relation.

Sprache
Englisch

Erschienen in
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 4 ; Pages: 1-25 ; Basel: MDPI

Klassifikation
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
General Financial Markets: General (includes Measurement and Data)
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Thema
asymmetric dependence
conditional distribution
ordinary least square
quantile regression
shipping stocks

Ereignis
Geistige Schöpfung
(wer)
Mohanty, Sunil
Aadland, Roar
Westgaard, Sjur
Frydenberg, Stein
Lillienskiold, Hilde
Kristensen, Cecilie
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2021

DOI
doi:10.3390/jrfm14040171
Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Mohanty, Sunil
  • Aadland, Roar
  • Westgaard, Sjur
  • Frydenberg, Stein
  • Lillienskiold, Hilde
  • Kristensen, Cecilie
  • MDPI

Entstanden

  • 2021

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