Conditional value-at-risk forecasts of an optimal foreign currency portfolio

Dongwhan Kim, Kyu Ho Kang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This study provides daily conditional value-at-risk (C-VaR) forecasts for a foreign currency portfolio comprising the USD/EUR, USD/JPY, and USD/BRL currencies. To do so, we estimate multivariate stochastic volatility models with time-varying conditional correlations using a Bayesian Markov chain Monte Carlo algorithm. Then, given the model-specific currency return density forecasts, we make the optimal portfolio choice by minimizing the C-VaR through numerical optimization. According to out-of-sample experiment, including emerging markets into the currency basket is essential for downside risk management, and considering model uncertainty as well as the parameter uncertainty can improve the portfolio performance.

Original languageEnglish
Pages (from-to)838-861
Number of pages24
JournalInternational Journal of Forecasting
Issue number2
Publication statusPublished - 2021 Apr 1

Bibliographical note

Funding Information:
We thank two anonymous referees and participants at the Bank of Korea seminar and KEA-APEA 2017 conference for useful feedback. This work is supported by a Korea University Grant ( K2009031 )

Publisher Copyright:
© 2020 International Institute of Forecasters


  • Bayesian MCMC method
  • Conditional correlation
  • Fat tail
  • Stochastic volatility
  • Time-varying

ASJC Scopus subject areas

  • Business and International Management


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