• The Korean Journal of Financial Management
• /
• v.21 no.2
• /
• pp.183-209
• /
• 2004

This paper uses both the bivariate GARCH type BEKK error correction model and Bivariate-AR(1)-Markov-Switching-VECM model to estimate the volatility, time-varying correlation and hedge ratio for the KTB spot and futures indexes, sampled daily over 1/4/2000-10/30/2003. This study suggests that the volatility regime has more significant influence on KTB markets than incline/decline regime does. The results support the importance of the bivariate model in stead of univariate model between KTB spot and futures markets, which may consider not only individual variance process but also covariance process at the same time.

• The Korean Journal of Financial Management
• /
• v.26 no.2
• /
• pp.93-112
• /
• 2009

The purpose of this study is to examine market timing and volatility timing abilities in Korean equity funds by distinguishing 'skill' and 'luck' for individual funds. In this study, we use the funds, which exist more than consecutive 24 month non-overlapping periods. This procedure leaves 545 funds among total 1,904 funds during sample priod January 2001 to December 2007. To derive the 'luck' distribution, the cross-sectional bootrap approach is adopted. From our results, it is found that when the traditional regression approach is adopted, only few Korean equity funds possess market timing and volatility timing abilities. However, based on the 'luck' distributions, which are derived from cross-sectional bootstrap approach, it is found that market timing and volatility timing abilities of Korean equity funds are merely from 'luck' rather than 'skill'.

• The Journal of the Korea Contents Association
• /
• v.17 no.5
• /
• pp.165-173
• /
• 2017

The purpose of this study was to analyze the correlation and volatility of Korea and neighboring East Asia stock markets. East Asian stock markets were selected for Japan, China, Hong Kong and Taiwan by economically and geographically close with Korea. If you understand the volatility and the correlation between Korea and the East Asian stock market, it may be helpful in predicting investment. And It may reduce the risk of investing of asset allocation in global portfolio level. For this using the national monthly return data for the last 163 months, I was calculating and comparison the rate and correlation, and regression analysis. Result of the correlation analysis, Korea have shown a low correlation with China. while showing a high correlation with Taiwan and Hong Kong. China has been forming its own market in East Asia and showing a low correlation with other countries exception Hong Kong. Hong Kong has been determined as the highest harmonization within the East Stock Market.

• International Area Studies Review
• /
• v.18 no.3
• /
• pp.67-88
• /
• 2014

The Chinese stock market has increasingly strengthened its market power on other stock markets due to rapid growth of its economy. In this context, this study investigated return spillover effect as well as asymmetric volatility spillover effect using a VAR-Bivariate EGARCH model among stock markets(China, US, Japan, Korea). Furthermore, we conjectured the impact of 2008 global financial crisis on the spillover effect of the Chinese stock market. In our empirical results, the Chinese stock market has a weak return spillover effect to other markets(US, Japan, Korea), but after the global financial crisis, its return spillover effect becomes stronger among other stock markets. In addition, the Chinese stock market have strengthened its asymmetric volatility spillover effect on other stock markets after the Global financial crisis. As a result, the Chinese stock market has an strong influence on other stock markets.

• International Area Studies Review
• /
• v.12 no.2
• /
• pp.3-26
• /
• 2008

This article investigates the usefulness of the skewed Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Australian financial series; the ASX200 stock index and AUD/USD exchange rate. For this purpose we assess the performance of FIGARCH and FIAPARCH Value-at-Risk (VaR) models based on the normal, Student-t, and skewed Student-t distribution innovations. Our results support the argument that the skewed Student-t distribution models produce more accurate VaR estimates of Australian financial markets than the normal and Student-t distribution models. Thus, consideration of skewness and excess kurtosis in asset return distributions provides appropriate criteria for model selection in the context of long memory volatility models in Australian stock and foreign exchange markets.

• Korea Real Estate Review
• /
• v.27 no.3
• /
• pp.41-50
• /
• 2017

Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications. This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.4
• /
• pp.29-38
• /
• 2022

The enormous sway of COVID-19 on the international financial market has been felt across the globe. The financial markets of Bangladesh have also been similarly affected by the global epidemic and experienced a significant increase in volatility. To scrutinise the connection between COVID-19 and the Dhaka Stock Exchange (DSE) indices' return and instability, this study uses data of the DSE from February 2014 to September 2021. A comparative examination of the return and instability of the stock indices of the DSE has also been done considering the outbreak of the current COVID-19 situation. After using the GJR-GARCH (1,1) model, this review uncovers that the outbreak of COVID-19 has a statistically positive noteworthy association with the DSE stock indices' instability, which increases the market's volatility. Traders' fear and the rising frequency of COVID-19 reported patients could cause this. Besides, according to this study, COVID-19 shows a substantial positive linkage with stock market returns that increases the market's return. An appealing valuation, lower interest rates in the banking channel, economic rebound following the closure to prevent coronavirus transmission, improved remittance inflows, and a return of export revenues could all have contributed to this outcome. In addition, the findings also reveal that all market indices are in a mean-reverting phase.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 1999.06a
• /
• pp.359-365
• /
• 1999

We have evaluated the performance and some physical properties of 25 automotive engine oils (21 domestic and 5 imported products) which are purchased on the market to verify the API(American Petroleum Institute) or ILSAC(International Lubricant Standardization and Approval Committee) certification marks attached on the products and to determine the necessity of the quality control of the engine oils on the market. 12 test items are chosen according to API engine oil specification, which are flash point, pour point, cold cranking simulator apparent viscosity, pumping viscosity, gelation index, HTHS(High Temperature High Shear viscosity), foam, high temperature foam, filterability, volatility, high temperature deposit(TEOST), phosphorus content. We have found one product which did not meet the API specification on gelation index, one on HTHS, four on foam, and one on volatility, which implies that the quality control system is in need to check the fidelity of the certification marks attached on the engine oils being marketed. In addition, this works raises the necessity of the upgrade of the present Korean engine oil specification.

• Korean Management Science Review
• /
• v.28 no.3
• /
• pp.61-72
• /
• 2011

This study suggests a DEA(Data Envelopment Analysis) based model to evaluate the value of corporate stock. The model integrating PER(Price-Earning Ratio), PBR(Price-BookValue Ratio), PSR(Price-Sales Ratio) and volatility in DEA structure has an advantage of overcome the limitation of traditional financial ratio based models. In order to show the effectiveness of the suggested model. we compare the performance of portfolio composed by DEA approach with those of portfolios made by traditional approaches such as PER, PBR, and PSR in terms of stock return and volatility. Specifically, we use the data of all the enterprises listed on the S&P 500 in the U.S. in 2007 and 2009 as the sample data for the experiments. The results of the experiments show that the performance of the DEA approach is clearly better than those of other approaches. Particularly, in sharply plummeting market, the performance of the DEA approach is shown to be prominently better than those of other approaches as the DEA approach reflects investment risk as well as profitability and growth. The DEA score combining the existing investment indices may serve as a useful barometer for selecting a stable and profitable portfolio.

• The Korean Journal of Applied Statistics
• /
• v.28 no.3
• /
• pp.541-559
• /
• 2015

VaR is now widely used as an important tool to evaluate and manage financial risks. In particular, it is important to select an appropriate volatility model for the rate of return of financial assets. In this study, both univariate and multivariate models are considered to evaluate VaR of the portfolio composed of KOSPI, Hang-Seng, Nikkei indexes, and their performances are compared through back testing techniques. Overall, multivariate models are shown to be more appropriate than univariate models to estimate the portfolio VaR, in particular DCC and ADCC models are shown to be more superior than others.