• Earthquakes and Structures
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• v.24 no.4
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• pp.303-316
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• 2023

Rapid post-earthquake damage estimation of subway stations is particularly necessary to improve short-term crisis management and safety measures of urban subway systems after a destructive earthquake. The conventional Performance-Based Earthquake Engineering (PBEE) framework with constant earthquake occurrence rate is invalid to estimate the aftershock risk because of the time-varying rate of aftershocks and the uncertainty of mainshock-damaged state before the occurrence of aftershocks. This study presents a time-varying probabilistic seismic risk assessment framework for underground structures considering mainshock and aftershock hazards. A discrete non-omogeneous Markov process is adopted to quantify the time-varying nature of aftershock hazard and the uncertainties of structural damage states following mainshock. The time-varying seismic risk of a typical rectangular frame subway station is assessed under mainshock-only (MS) hazard and mainshock-aftershock (MSAS) hazard. The results show that the probabilities of exceeding same limit states over the service life under MSAS hazard are larger than the values under MS hazard. For the same probability of exceedance, the higher response demands are found when aftershocks are considered. As the severity of damage state for the station structure increases, the difference of the probability of exceedance increases when aftershocks are considered. PSDR=1.0% is used as the collapse prevention performance criteria for the subway station is reasonable for both the MS hazard and MSAS hazard. However, if the effect of aftershock hazard is neglected, it can significantly underestimate the response demands and the uncertainties of potential damage states for the subway station over the service life.

• Journal of Navigation and Port Research
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• v.41 no.2
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• pp.71-78
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• 2017

This paper aims to investigate the time-varying systematic risk of the stocks of Korean logistics firms. For this purpose, the period from January 1991 to October 2016 was examined with respect to 21 logistics companies that are listed on the Korea Exchange. The systematic risk of the logistics stocks is measured in terms of the Capital Asset Pricing Model (CAPM) beta for which the sensitivity of a stock is compared to the return changes of the whole market. Overall, the betas of the stocks of the Korean logistics companies are significantly lower than those of the market unity; however, it was revealed that the logistics betas are not constant, but are actually time-varying according to different economic regimes, which is consistent with the previous empirical findings. This finding is robust across different measurements of the logistics betas. In addition, the impact of macroeconomic factors on the logistics betas was examined. The present study shows that the logistics betas are positively associated with foreign exchange-rate changes.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.63-71
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• 2020

In this paper, we propose the new time-varying coefficient GARCH-in-Mean model. The benefit of our model is to allow the risk-return parameter in the mean equation to vary over time. At the end of 2019 to the beginning of 2020, the world witnessed two shocking events: COVID-19 pandemic and 2020 oil price war. So, we decide to use the daily data from December 2, 2019 to May 29, 2020, which cover these two major events. The purpose of this study is to find the dynamic movement between risk and return in four major oil markets: Brent, West Texas Intermediate, Dubai, and Singapore Exchange, during COVID-19 pandemic and 2020 oil price war. For the European oil market, our model found a significant and positive risk-return relationship in Brent during March 26-April 21, 2020. For the North America oil market, our model found a significant positive risk return relationship in West Texas Intermediate (WTI) during March 12-May 8, 2020. For the Middle East oil market, we found a significant and positive risk-return relationship in Dubai during March 12-April 14, 2020. Lastly, for the South East Asia oil market, we found a significant positive risk return relationship in Singapore Exchange (SGX) from March 9-May 29, 2020.

• Journal of Industrial Convergence
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• v.2 no.2
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• pp.127-147
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• 2004

This paper examines the time-series relations among expected return, risk, and book-to-market(B/M) at the portfolio level. The time-series analysis is a natural alternative to cross-sectional regressions. An alternative feature of the time-series regressions is that they focus on changes in expected returns, not on average returns. Using the time-series analysis, we can directly test whether the three-factor model explains time-varying expected returns better than the characteristic-based model. These results should help distinguish between the risk and mispricing stories. We find that B/M is strongly associated with changes in risk, as measured by the Fama and French(1993) three-factor model. After controlling for changes in risk, B/M contains little additional information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than the characteristic-based model.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.165-187
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• 2005

This paper tries to investigate the relationships among stock return volatility, time-varying risk premium and Korea Discount. Using Korean Composite Stock Price Index (KOSPI) return from January 4, 1980 to August 31, 2005, this study finds possible links between time-varying risk premium and Korea Discount. First of all, this study classifies Korean stock returns during the sample period by three regime-switching volatility period that is to say, low-volatile period medium-volatile period and highly-volatile period by estimating Markov-Switching ARCH model. During the highly volatile period of Korean stock return (09/01/1997-05/31/2001), the estimated time-varying unit risk premium from the jump-diffusion GARCH model was 0.3625, where as during the low volatile period (01/04/1980-l1/30/1985), the time-varying unit risk premium was estimated 0.0284 from the jump diffusion GARCH model, which was about thirteen times less than that. This study seems to find the evidence that highly volatile Korean stock market may induce large time-varying risk premium from the investors and this may lead to Korea discount.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.715-724
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• 2010

Recurrent event data occurs when a subject experience the event of interest several times and has been found in biomedical studies, sociology and engineering. Several diverse approaches have been applied to analyze the recurrent events (Cook and Lawless, 2007). In this study, we analyzed the YTOP(Young Traffic Offenders Program) dataset which consists of 192 drivers with conviction dates by speeding violation and traffic rule violation. We consider a subject-specific effect, frailty, to reflect the individual's driving behavior and extend to time-varying frailty effect. Another feature of this study is about the redefinition of risk set. During the study, subject may be under suspension and this period is regarded as non-risk period. Thus the risk variables are reformatted according to suspension and termination time.

• Korean Management Science Review
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• v.33 no.3
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• pp.105-117
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• 2016

In this paper, we present empirical testing result to examine the validity of inbound supply and outbound demand risk factors in the sense of early predicting the firm's bankruptcy risk level. The risk factors are drawn from industry uncertainty attributes categorized as uncertainties of input market (inbound supply), and product market (outbound demand). On the basis of input-output table, industry level inbound and outbound sectors are identified to formalize supply chain structures, relevant inbound and outbound uncertainty attributes and corresponding risk factors. Subsequently, publicly available macro-economic indicators are used to appropriately quantify these risk factors. Total 68 industry level bankruptcy risk forecasting results are presented with the average R-square scores of between 53.4% and 37.1% with varying time lag. The findings offers useful insights to incorporate supply chain risk to the body of firm's bankruptcy risk level prediction literature.

• Environmental and Resource Economics Review
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• v.22 no.1
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• pp.99-125
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• 2013

Renewable energy industry not only has a promising future but also has more risk than conventional energy industry because of its characteristics. Therefore, in this study, an analysis of domestic renewable energy company risk has been performed. The risk of domestic wind and photovoltaic energy companies has been analyzed by using time varying beta model. The model has been constructed based on risk factors like firm size, firm diversification index, domestic installation, and so on. The principal result of analysis can be summarized as follows. First, risk factors affect domestic renewable energy companies have been discovered. Variables like firm size, growth rate of debt ratio, firm diversification index are statistically significant. I found that large firms are less riskier than small firms. It is also confirmed that companies with high diversification index and high debt ratio have high risk. Second, I got the result that policy factors like domestic renewable energy installation and government R&D expenditure could decrease risk of domestic renewable energy company. Third, relative sensitivity of each risk factor have been discovered. The effect of each variable gets bigger in this order: growth rate of domestic installation, firm size or diversification index, growth rate of debt ratio, growth rate of government R&D expenditure.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.107-118
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• 2022

In this study, daily data from January 2002 to June 2022 were used to investigate the relationship between risk-return relationship and market fear, uncertainty, stock market, and maritime freight index for the crude oil market. For this study, the time varying EGARCH-M model was applied to the risk-return relationship, and the wavelet consistency model was used to analyze the relationship between market fear, uncertainty, stock market, and maritime freight index. The analysis results of this study are as follows. First, according to the results of the time-varying risk-return relationship, the crude oil market was found to be related to high returns and high risks. Second, the results of correlation and Granger causality test, it was found that there was a weak correlation between the risk-return relationship and VIX, EPU, S&P500, and BDI. In addition, it was found that there was no two-way causal relationship in the risk-return relationship with EPU and S&P500, but VIX and BDI were found to affect the risk-return relationship. Third, looking at the results of wavelet coherence, it was found that the degree of the risk-return relationship and the relationship between VIX, EPU, S&P500, and BDI was time-varying. In particular, it was found that the relationship between each other was high before and after the crisis period (financial crisis, COVID-19). And it was found to be highly associated with organs. In addition, the risk-return relationship was found to have a positive relationship with VIX and EPU, and a negative relationship with S&P500 and BDI. Therefore, market participants should be well aware of economic environmental changes when making decisions.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.636-647
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• 2004

The objective of this paper is to provide an improved reorder decision policy for general multi-echelon distribution systems utilizing the shared stock information. It has been known that traditional reorder policies sometimes show poor performance in distribution systems. Thus, in our previous research we introduced the order risk policy which utilizes the shared stock information more accurately for the 2-echelon distribution system and proved the optimality. However, since the real world supply chain is generally composed with more than 2 echelons, we extend the order risk policy for the general multi-echelon systems. Since the calculation of the exact order risk value for general multi-echelon systems is very complex, we provide two approximation methods for the real-time calculation. Through the computational experiment comparing the order risk policy with the existing policies under various conditions, we show the performance of the order risk policy and analyze the value of the shared stock information varying with the characteristics of the supply chain.