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This study examines how the independence of monetary policy changes in situations where the interest rate differential between domestic and foreign rates inverts, utilizing the trilemma indices. For analysis, this paper uses the trilemma indices developed by Kim et al. (2017) to analyze the relationship between the monetary policy independence index and the other two trilemma indices, namely the capital account openness index and the exchange rate stability index, across 45 countries from 2002 to 2018. The analysis reveals that the trilemma's validity is contingent. In particular, no statistically significant negative correlation was found between the monetary policy independence index and exchange rate stability index during periods of interest rate differential inversion. A positive correlation emerges between exchange rate stability and the independence of monetary policy, particularly when the inverted interest rate differential exceeds a certain threshold. This situation, where the exchange rate remains stable despite low domestic interest rates, implies that the central bank is effectively managing monetary policy to appropriately respond to economic conditions, which is reflected in the monetary policy independence index. [ABSTRACT FROM AUTHOR]

In this paper, we address how the monetary authority should react to financial market status and exchange rate movements in a small open economy New Keynesian model with financial frictions due to asymmetric information between savers and borrowers. We show that the small economy with financial frictions is more susceptible to the exogenous shocks under the fixed exchange rate regime than under the flexible exchange regime. The small economy experiences a more prolonged and deeper economic recession under the fixed exchange rate regime than under the flexible exchange rate regime. The monetary policy taking into account external finance premium is better than the interest rate rule without considering the financial market status. [ABSTRACT FROM AUTHOR]

Applying Ismailov and Rossi (2018), I newly construct the Korea FX uncertainty based on the density distribution of historical forecast errors. This uncertainty index properly captures the rare but significant events in the Korean currency market and provides information distinct from other uncertainty measures in recent studies. I show that 1) FX uncertainty arising from unexpected depreciation has a stronger impact on Korea- U.S. exchange rates and that 2) macro variables, such as capital flows or interest rate differentials, have predictive ability regarding Korea FX uncertainty for short horizons. These findings enable us to predict the events of sudden currency crashes and understand the Korea-U.S. exchange rate dynamics. [ABSTRACT FROM AUTHOR]

We propose a simple identification scheme for the causes of the violations of uncovered interest parity. Our method uses the serial dependence patterns of excess returns as a criterion for judging performance of economic models. We show that a mean reverting component in excess returns, representing a violation of uncovered interest parity, mainly contributes to generating different serial dependence patterns of excess returns: rational expectations risk premium models tend to generate negative serial dependence of excess returns, while expectational errors models tend to generate positive serial dependence. [ABSTRACT FROM AUTHOR]

The paper analyzes and compares the effects of domestic monetary policy using DSGE, DSGE-VAR, and VAR based on a two-country open economy model of Korea and the U.S. According to impulse response analysis, a domestic interest rate hike raises won value in the case of DSGE and DSGE-VAR models, while in the case of the unrestricted VAR model, it lowers won value. In the marginal data density standard, DSGE-VAR (µ=1) is superior to DSGE or Bayesian VAR over the sample period. Conversely, in the in-sample RMSE criterion, especially for the won/dollar exchange rate, VARs are superior to DSGE or DSGE-VAR. It is necessary to study further if these differences are caused by model misspecification or omitted variable bias. [ABSTRACT FROM AUTHOR]