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This study analyzes the direct and indirect effects of air pollution on the return and volatility of the KOSPI index through the trading ratio by investor type. The main results are as follows. First, air pollutants have a direct effect on volatility; coarse particulate matter (PM10) and fine particulate matter (PM2.5) have a negative effect on volatility. Second, air pollutants have a significant effect on the trading ratio by investor type; as the concentration of fine particulate matter (PM2.5) increases, the trading ratio of institutionals and those of foreigners decrease. Third, the effect of cumulative exposure of air pollutants on stock return and volatility is greater than that of daily exposure. Although there is no effect on daily exposure to air pollution, stock return decreases during cumulative exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and carbon monoxide (CO). The volatility changes significantly with cumulative exposure compared to daily exposure to coarse particulate matter (PM10), fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2). Fourth, air pollutants have an indirect effect on stock return and volatility through trading ratio by institutional and foreign investors. In particular, the effect of air pollution through the foreign investors’ trading ratio is a remarkable result revealed in this study.

Smart contract is an important building block of blockchain. Automated market makers are working without an order book, and they determine the price of assets automatically. It is reported that he automated market makers have the impermanent loss, which causes financial damage to liquidity providers. Impermanent loss makes the liquidity providers hesitant to deposit assets in the liquidity pool. Therefore, their participation incentive from liquidity provision should be anticipated by automatic market makers inherently. However, the existence of impermanent gain has never been reported. Impermanent gain is important to attract liquidity providers without giving compensation incentives. This study shows that for some automated market makers, impermanent gain coexists with impermanent loss. Examples showing the coexistence and conditions are provided.

In this study, we analyzed whether the expiration day effect of domestic single stock futures exists. One-minute stock prices and trading volume by trader types is used. Data ranges from May 2008 to June 2016. The expiration day effects are measured by price reversal, price shock, volatility effect, and volume effect. Since the expiration day of single stock futures is on the second Thursday of each month, we analyzed whether the expiration day effects differ between expiration Thursday and non-expiration Thursday. The price reversal effect is evident in Samsung Electronics and Hyundai Steel, and the price shock effect is evident for KT and KT&G. However, price reversals and price shocks are not generally found in other stocks. On the other hand, in most stocks (16 out of 22), the volatility effect variables were statistically significantly larger on the expiration Thursday than non-expiration Thursday. The expiration day effects of single stocks are evident in the trading volume. First of all, trading volume increased significantly on expiration Thursday than non-expiration Thursday. In particular, the trading-volume shares of institutional investors and foreign investors increase and the share of individual investors is decreasing. This suggests that the increase in trading volume on expiration Thursday is mainly due to the increase in the trading-volume shares of institutional investors and foreign investors, who are supposed to be in the information superiority. In addition, we can conjecture that the larger volatility level on expiration Thursday than on non-expiration Thursday may be due to institutional investors and foreign investors rather than individual investors.

In this paper, a structural equation model is applied to Korean firms listed in KOSPI and KOSDAQ from 1990 to 2016 to analyze the determinants of capital structure and stock returns and discern how capital structure and stock returns affect each other. We find that stock returns have a strongly negative (-) effect on capital structure in the structural equation model. However, leverage has no significant effect on stock returns in the structural equation model, perhaps because we use data from before, during, and after recent financial crises. In addition, asset structure (+) and profitability (-) have strongly significant effects on capital structure. Uniqueness and size show unstable effects on capital structure. As for the determinants of stock returns, size (-), B/M (+), investment (+), and market premium (+) show strongly significant effects on stock returns in all the models. Profitability shows no significant influence on stock returns.

This paper examines the relationship between idiosyncratic volatility(IVOL) puzzle and trading volumes by trader types in the Korean stock market. The data set includes all stock in both KRX and KOSDAQ for the period from January 1999 through December 2013. Idiosyncratic volatility is measured by using the Fama-French's three-factor model. Traders are classified into individual, institution, and foreign trader. We construct (5X5) portfolios based on each trader's net buying and idiosyncratic volatility. We find that there are some special portfolios that show the idiosyncratic volatility puzzle. For individual investors, top net buying portfolios show clear the idiosyncratic volatility puzzle. However, for institution and foreign investors, lowest net buying portfolio show the idiosyncratic volatility puzzle. This results imply that the idiosyncratic volatility puzzle in the Korean stock market is mainly caused by individual investors.

There have always been North Korea Risks in South Korea stock market since its opening. Some studies have concluded that it does not have a substantial impact on South Korea’s economy due to chronic geopolitical risks, while others have argued it has had an impact. However, in light of the Efficient Market Hypothesis (EMH) it can be argued that both opinions view that information about North Korea Risks affects stock markets and that stock prices react to it. This study analyzed the effects of North Korea Risks on South Korea’s stock market using event study methodology empirically, and it tested the semi-strong EMH-a market in which prices always fully reflect available information. The research results are following: First of all, North Korea Risks have an impact on South Korea’s stock market and the data was statistically significant. In particular, stock market already reflected information about the forewarned events like nuclear test. However, market also responded to information about sudden events such as the impact of Kim Jung-il’s death on the South-North economic cooperation stock. Portfolio analysis demonstrated that small capital stocks were affected more than large caps. These results cannot reject the EMH. Also, estimates of market model and that of Fama-French three-factor model did not show a statistically significant difference in different verification. There was no statistically significant difference between growth and value stock in large caps portfolio either. However, there was a statistically significant difference between defense stock and South-North economic cooperation stock, small caps and big caps, and weighted average and simple average. The significance of this study lies in that it conducted the event study by variety estimation model with objective standards for selecting events when measuring the effect of North Korea Risks.

In this study, we explore the empirical relationship between trading volume and volatility among KOSPI200 index stock market, futures and options markets. In particular, in explaining the volatility of each market, the trading in other markets, as well as the trading volume of other markets, also served as explanatory variables. In other words, cross-market effects of trading volume by investor types are analyzed. The empirical results show that there exist the cross-market effects of the relationship between trading volume and volatility in deeply integrated financial markets such as KOSPI200 index stock, futures and options markets. That is, the volatility of one market is explained by the trading volume of trader types in other financial markets. And, overall options trading increases the volatility of each market, while the overall futures trading volume of foreign investors reduce the volatility of each market. Trading volume of Individual investors does not reduce the volatilities of KOSPI200 index and futures markets. That is, trading volume of Individual investors in stock, futures, and options markets increase the volatilities of stock and futures. This implies that foreign investors are informed traders, whereas individual investors are liquidity traders.

In this research, we investigate the relationship between volatility and the trading volumes of trader types in the KOSPI 200 index stock market, futures market, and options market. Three types of investors are considered: individual, institutional, and foreign investors. The empirical results show that the volatility of the stock market and futures market are affected by the transaction information from another market. This means that there exists the cross-market effect of trading volume to explain volatility. It turns out that the option market volatility is not explained by any trading volume of trader types. This is because the option market volatility, VKOSPI, is the volatility index that reflects traders' expectation on one month ahead underlying volatility. Third, individual investors tend to increase volatilities, whereas institutions and foreign investors tend to stabilize volatilities. These results can be used in the areas of investment strategies, risk management, and financial market stability.

Volatility Index is the index that represents future volatility of underlying asset implied in option price and expected value of market that measures the possibility of stock price`s change expected by investors. The Korea Exchange announces a volatility Index, VKOSPI, since April, 13, 2009. This paper used daily data from January, 2002 through December, 2008 and tested power of Volatility index for future returns of portfolios sorted by size, book-to-market equity and beta. As a result, VKOSPI has the predictive power to future returns and then VKOSPI may be determinants of returns. Also if beta is included when sorting portfolio, the predictive power of VKOSPI is stronger for future portfolio returns.

민간 주도형 사회간접자본(social overhead capital: SOC) 사업은 프로젝트의 규모와 장기에 걸친 공사의 불확실성 때문에 프로젝트의 경제성 분석을 하는 것은 복잡하다. 일반적으로 사용하고 있는 현금흐름 할인모형(discount cash flow model; DCF)은 미래의 불확실성 변수들을 적절히 반영하지 못한다는 한계점을 가지고 있지만 현실적으로 많이 쓰이고 있는 방식이다. 본 논문은 옵션기반모형에 근거한, BOT-옵션가치평가(BOT-OV)모형을 국내 민간 SOC 사업의 경제성 분석을 위해 적용한다. BOT-OV모형을 적용하는 것이 기존 순현재가치(net present value; NPV)방식을 통하여 프로젝트 가치 평가 할 때보다 더 정밀한 경제적 타당성 평가를 할 수 있다는 것을 확인하여 기존의 NPV 평가방식의 방법을 대체할 수 있는 가능성을 확인하는데 의의가 있다고 할 수 있다. 【The evaluation of the financial viability of a privatized infrastructure project is complex because of the uncertainties involved due to the project's scale, long concession period and complexity. Use the BOT option valuation(BOT-OV) model, for evaluating the financial analysis of a privatized infrastructure project. This sophisticated for financial evaluation compared with a traditional NPV analysis.】

This paper tried to find out whether the information from foreign capital markets can improve the forecasting power for the realized volatility of KOSPI200 index. The realized volatility is estimated by using both daily return series and 5 minutes intraday data of KOSPI200. The volatilities of S&P100 return series and Won/Dollar exchange rate are considered as the information from foreign capital markets, and the volatility of Korean domestic interest rate is introduced as an additional variable to improve the forecasting power for the realized volatility of KOSPI200 returns. It turns out that those additional variables are statistically significant to improve the forecasting power for the realized volatility of KOSPI200.