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

Dongwhan Kim, Kyu Ho Kang

Research output: Contribution to journalArticlepeer-review

Abstract

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
Volume37
Issue number2
DOIs
Publication statusPublished - 2021 Apr 1

Keywords

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

ASJC Scopus subject areas

  • Business and International Management

Fingerprint

Dive into the research topics of 'Conditional value-at-risk forecasts of an optimal foreign currency portfolio'. Together they form a unique fingerprint.

Cite this