A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

Jae-Hyun Kim, Chang-Ho An

doi:10.17762/turcomat.v12i5.951

Abstract

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

Highlights

The global economy is on the rise with uncertainty due to the continued trade conflict between the United States and China and the increased possibility of no-deal Brexit in the UK
As COVID-19 spreads around the world and global economic recession is expected, central banks in major countries are turning to monetary policy to cut rates
When a nonstationary time series with unit roots is analyzed, there is a possibility of spurious regression that appears to be correlated between variables even though there is no correlation

Summary

Introduction

As COVID-19 spreads around the world and global economic recession is expected, central banks in major countries are turning to monetary policy to cut rates. The Bank of Korea drastically cut the base rate as a way to alleviate the volatility of the financial market and reduce the impact on growth and prices. The base rate cut decided by the Bank of Korea is initially reflected in the very short-term interest rate (call rate), and subsequently affects the interest rates that we experience directly through credit line or mortgage loans in the market. In the midst of the debate on interest rate cuts at home and abroad, the study of interest rate prediction is very necessary considering the current global and the domestic economy, as one of the monetary policies. We build a vector time series prediction model using time-series data of call rate (1 day), loan interest rates, and Treasury rate (3 years)

Research Model

Granger causality test

Results

Prediction model estimation and diagnosis

Conclusions