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This paper assesses empirically the tax implications of population aging in Japan and Korea. We achieve the research objectives by taking two approaches: (1) a vector autoregression (VAR) analysis; and (2) an estimated dynamic stochastic general equilibrium (DSGE) framework. Based on data from 1973 to 2015, the VAR results for both countries reveal that an aging shock decreases GDP, hours, consumption, and investment. As the tax bases fall, so do tax revenues. Conditioned on estimated DSGE models, the second approach is to characterize Laffer curves at two steady states: the current (as of 2015) and aged (as of 2060) states. We find that the adverse effect of population aging on tax revenues is more serious for Korea than for Japan. Even in the aged state, Japan has room to generate tax revenues comparable to the current state, whereas combinations of tax rates satisfying this property do not exist for Korea.

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This paper addresses implementable, optimal macroprudential and monetary policies in a standard DSGE model augmented with nominal price and wage rigidities and housing sector. The paper also discusses the effect of introducing of time-varying LTV regulation in cooling down large swings of household debt. In particular, it examines macroprudential policy reacting to credit growth or housing price growth to dampen the excessive volatility of household debt. The paper shows that the time-varying macroprudential policy rules reacting to the debt to income ratio or to the credit growth are more effective in moderating the household debt swings to exogenous shocks than the time-varying macroprudential policy rule responding to the housing price growth. In particular, the time-varying macroprudential policy reacting to debt to borrower's labor income is most effective in moderating the debt fluctuations to housing demand shocks. The macroprudential policy reacting to the housing price growth is almost indifferent to the time-invariant macroprudential policy when the economy is hit by the house demand shock. The time-varying macroprudential policy is against the winds in the sense that the LTV ratio goes down when the economy expands with an increase in housing price and demand. The paper shows that there is a substantial welfare increase associated with the time-varying macroprudential policy compared to the time-invariant macroprudential policies.

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This article estimates regime-switching continuous-time stochastic volatility models using daily KOSPI 200. We consider single regime Heston, GARCH, and CEV stochastic volatility models and 6 regime-switching stochastic volatility models which have two different regimes $L$ and $H$. We employ Hamilton algorithm (Hamilton (1989)) to compute the log-likelihood and to apply MLE. Because the true transition probability density functions (TPDFs) of our stochastic volatility models are unknown, we use Ait-Sahalia (2008) and Choi (2015b) to obtain closed-form approximate TPDF. The regime-switching CEV model where the transition probability is allowed to vary over time has been found to be the best to explain the movements of KOSPI 200. Regime $L$ has a stronger leverage effect than regime $H$. Comparing to regime $L$, the volatility variable tends to revert to its long-run mean level more rapidly, the volatility of volatility variable is greater, the probability of staying in the same regime $H$ in the next period is bigger in regime $H$. And the transition probability varies with time depending on the stock price rather than the volatility. When the probabilities of regime $H$ are high we could identify various economic and political events between South Korea and North Korea or inside or outside South Korea that could have affected Korean stock market.

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This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we adopt the cross-quantilogram, a correlation statistic of quantile hit processes, and analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. Second, we consider a simple quantile-augmented volatility model that accommodates the quantile dependence and directional predictability from the US market to these other markets. The quantile-augmented volatility model provides superior in-sample and out-of-sample volatility forecasts. Finally, we set up a generalized quantile-based approach to improve volatility forecasting for a wide class of asset portfolios.