• 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.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.19-27
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• 2022

The size of the government is one of the most fundamental debates of open economies. In any economy, government plays an important role, but a pertinent level of economic prosperity has never been obtained in history without government. Therefore, the objective of this paper investigates the association of government size, economic volatility, and institutional quality for 182 economies from the time period 1996-2016 is collected from the World Bank database. GE is defined as the General government's final consumption expenditure. Health expenditure is represented by HE. Government expenditure on education is denoted by EDUEXP. The economic volatility is measured by the rolling standard deviation of GDP per capita growth rate, Population growth, Trade openness, GINI represented Gini index which measures the degree to which the income distributed or consumption expenses among citizens deviates from a perfectly equal distribution. The results proposed that economic volatility has a significant effect on government size and institutional qualities. Moreover, the paper extends the investigation by finding the link between economic volatility with government health and education expenditure separately. The policy implication drawn from this analysis is that controlling economic volatility may reduce the size of government and also significantly affect health and education expenditures.

• Bulletin of the Korean Mathematical Society
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• v.53 no.5
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• pp.1411-1425
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• 2016

We provide a partial differential equation for European options on a stock whose price process follows a general geometric Riemannian Brownian motion. The existence and the uniqueness of solutions to the partial differential equation are investigated, and then an expression of the value for European options is obtained using the fundamental solution technique. Proper Riemannian metrics on the real number field can make the distribution of return rates of the stock induced by our model have the character of leptokurtosis and fat-tail; in addition, they can also explain option pricing bias and implied volatility smile (skew).

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1027-1036
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• 2008

Value at Risk(VaR) is a fundamental tool for managing market risks. It measures the worst loss to be expected of a portfolio over a given time horizon under normal market conditions at a given confidence level. Calculation of VaR frequently involves estimating the volatility of return processes and quantiles of standardized returns. In this paper, we introduced and applied the CreditMetrics model to estimate the credit VaR of Korean Property and Casuality insurers.

• Asia-Pacific Journal of Business
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• v.9 no.4
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• pp.85-97
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• 2018

This paper investigates factors underlying a decrease in trading values in KOSPI 200 futures/options market on the basis of the current state of the markets. Among the factors that could affect trading values in KOSPI 200 derivatives market, we focus on the market activity of underlying assets as it has an impact on the trading of financial derivatives. Trading value and volatility are designated as market activity and the empirical results confirm that the market activity of the underlying assets is significant in explaining the decrease in trading values in KOSPI 200 futures/options market. To figure out fundamental reasons of the decrease in trading values in this market, we examine mitigation of home bias and decrease in leverage incentives as they are presumed to have influence on KOSPI 200 index market. As the global and local financial environment is time-varying, the degree of home bias and the leverage demand also changes. It implies that institutional change and/or policy effort to promote the trading of KOSPI 200 financial derivatives should be made taking into account the fact that considerable portion of the change in trading values in financial derivatives market depends on the state of the market.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.825-835
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• 2008

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.3
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• pp.1-7
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• 2009

Ethanol has properties of a lower setting point, higher oxygen contents, lower cetane numbers, and also higher volatility compared to biodiesel. Thus, biodiesel fuel can be improved in the fluidity of and exhaust emissions by blended ethanol fuel. This research aims to understand combustion characteristics of biodiesel-ethanol blending fuel inside a constant volume chamber by obtaining some fundamental data in order to improve combustion atmosphere. To understand the physics of combustion, high speed camera was applied to visualize the development of combustion processes, and combustion pressure and exhaust emission were measured at several blending ratios of ethanol and biodiesel fuel. This information may contribute to improve the performance of biodiesel engine and reduce emissions in future.

• Korean System Dynamics Review
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• v.12 no.4
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• pp.93-124
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• 2011

The introduction of competition in the generation of electricity has raised the fundamental question of whether markets provide the right incentives for the provision of the capacity needed to maintain system reliability. Capacity mechanisms are adopted around the world to guarantee appropriate level of investment in electricity generation capacity. In this study, we discuss these approaches and analyze the capacity pricing mechanisms from the adequacy perspective. We conclude that the design of capacity mechanism is very important to decrease electricity spot price and increase total electric capacity. Specifically, the constant of capacity pricing mechanism made a difference to the performance of electricity market. However, the slope of capacity price mechanism is better than the constant of that in improving performance of electricity market.