• Journal of Korea Port Economic Association
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• v.27 no.1
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• pp.263-280
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• 2011

The global financial crisis, triggered by the subprime mortgage crisis in 2007, has put the world economy into the recession with financial market turmoil. I tested whether variables were cointegrated or whether there was an equilibrium relationship. Also, Generalized impulse-response function (GIRF) and accumulation impulse-response function (AIRF) may be used to understand and characterize the time series dynamics inherent in economical systems comprised of variables that may be highly interdependent. Moreover, the IRFs enables us to simulate the response in freight to a shock in the USD/JPY exchange rate, Dow Jones industrial average index, Dow Jones volatility, Chinese Import volatility. The result on the cointegration test show that the hypothesis of no cointergrating vector could be rejected at the 5 percent level. Also, the empirical analysis of cointegrating vector reveals that the increases of USD/JPY exchange rate have negative relations with freight. The result on the impulse-response analysis indicate that freight respond negatively to volatility, and then decay very quickly. Consequently, the results highlight the potential usefulness of the multivariate time series techniques accounting to behavior of Freight.

• Journal of Venture Innovation
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• v.2 no.2
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• pp.117-126
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• 2019

This study conducted correlation analysis and multiple regression analysis using financial statements, loan structure, ROA and ROA volatility of domestic commercial banks, regional banks and special banks for the past five years (2012 ~ 2016). The result is as follows. First, as a result of correlation analysis, bank's ROA is positively related to household loans and SME loans, but it is negatively correlated with the ratio of loans to large companies, sector bias, and loan loss provision ratio. Second, ROA volatility was negatively related to household loans and SME loans, but it was positively correlated with large corporate loans, sector bias ratio, and loan loss provision ratio. Third, as a result of the regression analysis, the variables that have a statistically significant effect on the ROA volatility of banks were household loans, SME loans, and large enterprise loans. From these empirical results, special banks with high volatility in profits need to diversify loan types and sectors in order to achieve business performance outside of policy finance. and Especially, Suhyup Bank and Nonghyup Bank, which have a large commercial role, have a large size per unit by focusing on short-term profit and Rather than focusing on large companies or large loans that are easy to obtain financial information, it is necessary to focus management capabilities on household loans and SME loans by developing capabilities such as screening techniques.

• Journal of Environmental Science International
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• v.27 no.6
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• pp.349-358
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• 2018

In this study, using coke dust from ironwork, the pulse pressure on a pulse air jet bag filter was investigated considering the influence of the pressure loss due to filtration velocity and pressure intervals. The research on the optimal pulse pressure prediction of a pulse air jet type bag filter using coke dust showed the following results. Pressure loss volatility produced by the pulse pressure under low dust concentration(0.5, $1g/m^3$) and low face velocity(1.25 m/min) was less than $10mmH_2O$. This suggests that the pulse pressure has a low impact on the pressure loss. In contrast, pressure loss volatility under high dust concentration($3g/m^3$) and high face velocity(1.75 m/min) was $25mmH_2O$. Therefore, pulse pressure with high dust concentration and high face velocity has a strong influence on the pressure loss volatility, compared to the condition of low dust concentration and low face velocity. The optimal pulse pressure of inlet dust concentration($0.5g/m^3$) was $6kg/cm^2$ under the same face velocity(1.75 m/min). As concentration increased from 1 to $2g/m^3$, the pulse pressure gradually reached $5kg/cm^2$ thus indicating that the pulse pressure($5kg/cm^2$) is pertinent at a high concentration($3g/m^3$). The pulse intervals: 20, 25 and 30 sec, which are relatively longer than 10 and 15 sec, corresponded to high pressure loss volatility produced by the pulse pressure. Furthermore, low pressure loss volatility was noted at $5kg/cm^2$ of the overall pulse pressure.

• Journal of Korea Technology Innovation Society
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• v.20 no.3
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• pp.732-753
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• 2017

Recently, when evaluating the technology values in the fields of biotechnology, pharmaceuticals and medicine, we have needed more to estimate those values in consideration of the period and cost for the commercialization to be put into in future. The existing discounted cash flow (DCF) method has limitations in that it can not consider consecutive investment or does not reflect the probabilistic property of commercialized input cost of technology-applied products. However, since the value of technology and investment should be considered as opportunity value and the information of decision-making for resource allocation should be taken into account, it is regarded desirable to apply the concept of real options, and in order to reflect the characteristics of business model for the target technology into the concept of volatility in terms of stock price which we usually apply to in evaluation of a firm's value, we need to consider 'the continuity of stock price (relatively minor change)' and 'positive condition'. Thus, as discussed in a lot of literature, it is necessary to investigate the relationship among volatility, underlying asset values, and cost of commercialization in the Black-Scholes model for estimating the technology value based on real options. This study is expected to provide more elaborated real options model, by mathematically deriving whether the ratio of the present value of the underlying asset to the present value of the commercialization cost, which reflects the uncertainty in the option pricing model (OPM), is divided into the "no action taken" (NAT) area under certain threshold conditions or not, and also presenting the estimation logic for option values according to the observation variables (or input values).

• Journal of Distribution Science
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• v.13 no.11
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• pp.39-46
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• 2015

Purpose - In Korea, there has been a recent trend that shows housing prices have risen rapidly following the International Monetary Fund crisis. The rapid rise in housing prices is spreading recognition of this as a factor in housing price volatility. In addition, this raises the expectations of housing prices in the future. These expectations are based on the assumption that a relationship exists between the current housing prices and expected housing prices in the real estate industry. By performing an empirical analysis on the validity of the claim that an increase in current housing prices can be correlated with expected housing prices, this study examines whether a long-term equilibrium relationship exists between expected housing prices and existing housing prices. If such a relationship exists, the recovery of equilibrium from disequilibrium is analyzed to derive related implications. Research design, data, and methodology - The relationship between current housing prices and expected housing prices was analyzed empirically using the Vector Error Correction Model. This model was applied to the co-integration test, the long-term equilibrium equation among variables, and the causality test. The housing prices used in the analysis were based on the National Housing Price Trend Survey released by Kookmin Bank. Additionally, the Index of Industrial Product and the Consumer Price Index were also used and were obtained from the Bank of Korea ECOS. The monthly data analyzed were from January 1987 to May 2015. Results - First, a long-term equilibrium relationship was established as one co-integration between current housing price distribution and expected housing prices. Second, the sign of the long-term equilibrium relationship variable was consistent with the theoretical sign, with the elasticity of housing price distribution to expected housing price, the industrial production, and the consumer price volatility revealed as 1.600, 0.104,and 0.092, respectively. This implies that the long-term effect of expected housing price volatility on housing price distribution is more significant than that of the industrial production and consumer price volatility. Third, the sign of the coefficient of the error correction term coincided with the theoretical sign. The absolute value of the coefficient of the correction term in the industrial production equation was 0.006, significantly larger than the coefficients for the expected housing price and the consumer price equation. In case of divergence from the long-term equilibrium relationship, the state of equilibrium will be restored through changes in the interest rate. Fourth, housing-price volatility was found to be causal to expected housing price, and was shown to be bi-directionally causal to industrial production. Conclusions - Based on the finding of this study, it is required to relieve the association between current housing price distribution and expected housing price by using property taxes and the loan-to-value policy to stabilize the housing market. Further, the relationship between housing price distribution and expected housing price can be examined and tested using a sophisticated methodology and policy variables.

• Management & Information Systems Review
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• v.29 no.2
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• pp.97-118
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• 2010

This paper explores the mean and volatility spillover effects among Chinese, Korean, and the U.S. stock markets during the Asian and global financial crises. We found that, during the Asian Financial crisis, there was no mean spillover effect to the Chinese stock markets. However, there were reciprocal mean spillover effects between the U.S. and the Korean market. This implies that Korean market was open, while Chinese market was secluded from the international financial market at that time. The negative volatility spillover effect between the U.S. and China reinforces this finding. During the global financial crisis, there was reciprocal mean spillover effect between the U.S. and China, and between the U.S. and Korea. This may reflect the fact that Chinese market has opened to the international financial market. However, the volatility spillover effect does not exist between China and the U.S., while the U.S. and Korea has reciprocal volatility spillover effect to each other. These findings may imply that China is still in the process of opening her stock market to international investors.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.49-67
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• 2006

This study examines whether won/dollar futures have price discovery function and volatility spillover effect or not, using intraday won/dollar futures prices, volumes, and spot rates for the interval from March 2, 2005 through May 30, 2005. Futures prices and spot rates are non-stationary, but there is the cointegration relationship between two time series. Futures returns, spot returns, and volumes are stationary. Asymmetric effects on volatility in futures returns and spot returns does not exist. Analytical results of mean equations of the BGARCH-EC (bivariate GARCH-error correction) model show that the increase of futures returns raise spot returns after 5 minutes, which implies that futures returns lead spot returns and won/dollar futures have price discovery function. In addition, the long-run equilibrium relationship between the two returns could help forecast spot returns. Analytical results of variance equations indicate that short-run innovations in the futures market positively affect the conditional variances of spot returns, that is, there is the volatility spillover effect in the won/dollar futures market. A dummy variable of volumes does not have an effect on two returns but influences significantly on two conditional variances.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.63-89
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• 2007

This study aims at empirically verifying whether long memory properties exist in returns and volatility of the financial time series and then, empirically observing influential factors of long-memory properties. The presence of long memory properties in the financial time series is examined with the Hurst exponent. The Hurst exponent is measured by DFA(detrended fluctuation analysis). The empirical results are summarized as follows. First, the presence of significant long memory properties is not identified in return time series. But, in volatility time series, as the Hurst exponent has the high value on average, a strong presence of long memory properties is observed. Then, according to the results empirically confirming influential factors of long memory properties, as the Hurst exponent measured with volatility of residual returns filtered by GARCH(1, 1) model reflecting properties of volatility clustering has the level of $H{\approx}0.5$ on average, long memory properties presented in the data before filtering are no longer observed. That is, we positively find out that the observed long memory properties are considerably due to volatility clustering effect.

• Management & Information Systems Review
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• v.31 no.3
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• pp.307-321
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• 2012

This paper examines the spillover effect between Korea stock market and Chinese stock market according to increasing economic power of Chinese. Chinese stock market start the transaction one hour and half early than Korea stock market. Especially we focuses the response of Korea stock market after Chinese stock market starts. So we analyze the return an volatility of Korea stock market after 10:30. We employee daily and intraday stock return and volatility. The sample period ranges from January 2008 to April 2010 total 28 months. Our results show that the gap of open price between Korea and Chinese stock market affect the five minute return and volatility of Korea stock market but don't affect the ten minute return and volatility. Recently, this spillover effect has increased more and more. This shows the rapid increase of economic power of Chinese to affect the Korea capital market.

• Land and Housing Review
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• v.12 no.3
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• pp.27-37
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• 2021

This paper empirically examined the relationship between the housing market and the stock market to investigate the price and the asymmetric volatility spillover effects. The monthly housing price index and the monthly KOSPI were used for analysis. This research employed the EGARCH model. The analysis period was from January 1986 until June 2021 with periodization centered on the Asian Financial Crisis: before and after the crisis - the end of December 1997. The EGARCH model allows analysis of 'good news' and 'bad news' in understanding volatility. The price spillover effect was observed one way from the stock market to the housing market. On the contrary, the spillover effect was not found from the housing market to the stock market. The empirical evidence suggests that there are price and asymmetric volatility effects in the entire period of analysis in both housing and the stock markets. In the housing market, the negative effects of information were found pre-financial crisis while the positive effects, in other periods. However, in the stock market, the negative effects of information were found in the pre- and post-financial crisis periods. This means that the housing market is more affected by 'good news' than 'bad news' when information spreads to the markets while the stock market is more affected by 'bad news' than 'good news'. It is of significance to discover the variable returns by different information.