Extended Market Drivers of CIP Deviations In Korea’s Cross-Currency Swap Markets
Extended Market Drivers of CIP Deviations In Korea’s Cross-Currency Swap Markets
Favour O. Olarewaju(Baum Tenpers Research Group, Nigeria); Abdulmuttolib B. Salako(Baum Tenpers Research Group, Nigeria)
33권 4호, 541~580쪽
초록
This study seeks to explore the peculiar impact of critical indicators: dollar index, oil price, sovereign credit default swap (CDS), stock market returns and Libor-OIS on the Korean won (KRW) cross-currency basis (CCB) at the maturity of 1-and5-years by segregating the timeframe from March 2003 to September 2019 into four periods: before the global financial crisis (GFC), during GFC, during eurozone crisis and after the crisis. Hence, for this time series data, simple linear regression with ARMA errors is employed to determine the direction and magnitude of regressors’ impact on KRW as well as to deal with possible heteroskedasticity and serial correlation issues. Vector autoregression (VAR) is used to obtain forecast error variance (FEV) decomposition to ascertain the dynamic relationship and predictive influence of these variables, especially in the advent of shocks. It is affirmed that CDS, stocks and Libor-OIS were most significant during GFC. Also, all regressors were relatively significant for the overall dataset with oil having the least impact.
Abstract
This study seeks to explore the peculiar impact of critical indicators: dollar index, oil price, sovereign credit default swap (CDS), stock market returns and Libor-OIS on the Korean won (KRW) cross-currency basis (CCB) at the maturity of 1-and5-years by segregating the timeframe from March 2003 to September 2019 into four periods: before the global financial crisis (GFC), during GFC, during eurozone crisis and after the crisis. Hence, for this time series data, simple linear regression with ARMA errors is employed to determine the direction and magnitude of regressors’ impact on KRW as well as to deal with possible heteroskedasticity and serial correlation issues. Vector autoregression (VAR) is used to obtain forecast error variance (FEV) decomposition to ascertain the dynamic relationship and predictive influence of these variables, especially in the advent of shocks. It is affirmed that CDS, stocks and Libor-OIS were most significant during GFC. Also, all regressors were relatively significant for the overall dataset with oil having the least impact.
- 발행기관:
- 한국재무학회
- 분류:
- 경영학