The Bank of Korea watch

Hyerim Kim, Kyu Ho Kang

Research output: Contribution to journalArticlepeer-review

Abstract

Traders closely monitor the Bank of Korea (BOK) base-rate decisions since the short rate is the primary factor in bond and currency valuations. The Survey of Professional Forecasters(SPF) has been widely used and is considered the most reliable BOK base-rate decision forecast. In this study, we investigate whether the SPF's prediction ability can be further improved. To this end, we use a dynamic multinomial ordered probit prediction model of the BOK base rate with a large number of predictors and apply a Bayesian variable selection algorithm. Through an empirical exercise, we show that our approach substantially outperforms the SPF in terms of out-of-sample prediction. The key predictors found are SPF, short-term bond yields, lagged base rate, federal funds rate, and inflation expectation survey data. Furthermore, allowing the prediction ability to change over time is essential for improving predictive accuracy.

Original languageEnglish
Article number102668
JournalJournal of International Money and Finance
Volume126
DOIs
Publication statusPublished - 2022 Sep

Keywords

  • Bayesian machine learning
  • Out-of-sample prediction
  • Policy rate
  • Variable selection

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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