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 language | English |
|---|---|
| Article number | 102668 |
| Journal | Journal of International Money and Finance |
| Volume | 126 |
| DOIs | |
| Publication status | Published - 2022 Sept |
Bibliographical note
Funding Information:We would like to thank the Korea University and the Bank of Korea seminar participants for their valuable comments. We gratefully acknowledge the generous financial support provided by the Bank of Korea. This research was supported by a Korea University Grant (K2209381), the Bank of Korea, and the BK21 FOUR funded by the Ministry of Education and National Research Foundation of Korea. The views expressed herein are those of the authors and do not necessarily reflect the official views of the Bank of Korea. Finally, we would like to thank Sunho Lee and Kitak Kim for their assistance. All the remaining errors are our own.
Publisher Copyright:
© 2022 Elsevier Ltd
Keywords
- Bayesian machine learning
- Out-of-sample prediction
- Policy rate
- Variable selection
ASJC Scopus subject areas
- Finance
- Economics and Econometrics