Abstract | PDF (621 kilobytes)

No abstract is available for this article.

Abstract | PDF (218 kilobytes)

This paper examines the relationship between house prices, rents, and user costs of housing in the United States from January 2009 to March 2022. We first use the time-varying coefficient cointegration model to explain the long-run relationship and adopt an error correction model with endogenous regime switching, which turns out to fit the data better than existing models. Our results show that, following the sub-prime mortgage crisis, the U.S. housing market has switched between a strong or a weak house price momentum state. In the strong momentum regime, house price returns have been more persistent and error correction has been slower. The degree of house price momentum is estimated to be very high and explosive in the strong regime while it is moderate in the weak regime. It is estimated that 74% of the data remains in the strong momentum regime. The extracted latent factor determines the regime of the housing market, and we run the adaptive lasso on the FRED-MD to identify the link between house price momentum and the macroeconomic and financial variables.

Abstract | PDF (705 kilobytes)

This paper examines the impact of investor sentiment on stock market volatility using a natural language processing classification method applied to a large-scale dataset of social network data. We also apply numerous forecasting techniques not only including conventional linear models, but also different machine learning models and compare its results. Among various economic and sentiment features, we employ the least absolute shrinkage and selection operator (Lasso) for linear models and a tree-based nonlinear variable selection method to demonstrate the critical role of sentiment measures in market volatility. The results show that sentiment variables are identified to be one of the most important variables in relationship with stock market volatility and improve the future prediction of volatility when considered.

Abstract | PDF (2946 kilobytes)

This study analyzes the state-dependent characteristics of fiscal policy in Korea using a two-agent New Keynesian dynamic stochastic general equilibrium model. According to the findings, there is a crowding-out effect of increases in government spending, which reduces private consumption and investment, thereby decreasing GDP. However, this effect is smaller when household borrowing constraints are binding compared to the opposite case. When household borrowing constraints are slack, households save the temporarily increased income from increases in government spending in anticipation of future tax hikes. Conversely, when borrowing constraints are binding, borrowing households spend the temporarily increased income on final goods and housing services consumption. This increases total consumption and boosts GDP. Currently, Korea exhibits a household leverage ratio lower than the long-term trend, suggesting that the proportion of borrowing households facing binding constraints is below the long-run trend. This indicates that the crowding-out effect of increases in government spending on GDP is significant.

Abstract | PDF (660 kilobytes)

We examine the herding behavior of individual investors on institutional investors in the US equity fund market. In this paper, individual investors are households entrusting money to mutual funds, while institutional investors are non-household entities. Our empirical investigation determines that the significant herding behavior of individual investors is based on the trading size of institutional investors. In particular, we find evidence that herding in the US equity mutual fund market is triggered by the largest selling and buying of institutional investors. This indicates that the presence of asymmetry in individual investors’ herding behavior depends on the size of institutional investors’ trade. Further, we find that herding in the US equity fund market is related to marketwide risk aversion, which is intensified in institutional investors’ big selling.