• Korean Business Review
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• v.20 no.2
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• pp.15-27
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• 2007

This paper analyzed the relationship between Housing Purchase Price volatility of Seoul and Housing Purchase Price volatility of local large city. Other studies investigates the effect on the observed volatility Observed volatility consists of fundamental volatility and transitory volatility. Fundamental volatility is caused by information arrival and transitory volatility is caused by noise trading. Fundamental volatility is trend component and is modelled as a random walk with drift. Transitory volatility is cyclical component and is modelled as a stationary process. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Observed volatility is estimated by GJR GARCH(1,1) model. We find that GJH GARCH model is superior to GARCH model and good news is more remarkable effect on volatility than bad news. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. The findings in this paper is as follows. The correlation between Seoul housing price volatility and Busan housing price volatility is high. But, the correlation between Seoul and Daejeon is low. And the correlation between Daejeon and Busan is low. As a distinguishing feature, the correlation between fundamental volatilities is high in the case of all pairs. But, the correlation between transitory volatilities turns out low. The reason is as follows. When economic information arrives, Seoul, Daejeon, and Busan housing markets, all together, are affected by this information.

• Korean Business Review
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• v.19 no.1
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• pp.81-92
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• 2006

This study investigates the relation between institutional investor's net purchase and the volatility of KOSPI. Some portion of volatility in stock prices comes from noise trading of irrational traders. Observed volatility may be defined as the sum of the portion caused by information arrival, fundamental volatility, and the portion caused by noise trading, transitory volatility. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Estimation results show that institutional investor's net purchase was not significantly related to all kinds of volatility(observed volatility, fundamental volatility and transitory volatility). This means that institutional investor's net purchase did not increase noise trading.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.1
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• pp.78-82
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• 2004

Volatility may be defined as the sum of fundamental volatility caused by information arrival and transitory volatility caused by noise trading. This study decomposes the observed KOSPI volatility into fundamental volatility and transitory volatility using Kalman filtering method. This study investigates the effects of the introduction of cyber trading on the KOSPI volatility. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatilty and transitory volatility individually. Analysis showed that observed volatility is increased significantly at 1％ level, but transitory volatility is not increased. This means that noise trading by irrational investors is not increased.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.582-586
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• 2004

The purpose of this study is to examine the lead-lag relationship between volatility and returns in high frequency stock market data to see the validity of two hypotheses that explain volatility asymmetry. Specifically, wavelet analysis is applied to decompose the volatility process into permanent and transitory components and then each component is investigated in conjunction with returns. The results from cross-correlation analysis between volatility and returns support the leverage effect hypothesis rather than the volatility feedback hypothesis in all cases.

• KDI Journal of Economic Policy
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• v.43 no.2
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• pp.1-22
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• 2021

The effects of social distancing measures on income distributions and aggregate variables are examined with an off-the-shelf heterogeneous-agent incomplete-market model. The model shows that social distancing measures, which limit households' labor supply, can decrease the labor supply of low-income households who hold insufficient assets and need income the most given their borrowing constraints. Social distancing measures can therefore exacerbate income inequality by lowering the incomes of the poor. An equilibrium interest rate can fall when the social distancing shock is expected to be persistent because households save more to prepare for rising consumption volatility given the possibility of binding to the labor supply constraint over time. When the shock is expected to be transitory, in contrast, the interest rate can rise upon the arrival of the shock because constrained households choose to borrow more to smooth consumption given the expectation that the shock will fade away. The model also shows that social distancing shocks, which diminish households' consumption demand, can decrease households' incomes evenly for every income quantile, having a limited impact on income inequality.