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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.

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 explored how soft information and hard information were used when SBC(Small Business Corporation, Korea) reviewed government loan applications. The data set is made up of financial and non-financial data of small-business firms since 2004. A non-financial data set is considered as soft information. Relative importance of three kinds information such as credit information, soft information, financial information is compared with each other by using the logit model. As a result, credit information is most critical to the loan approval, and then soft information follows, lastly financial information has the smallest effect on the loan approval. This is because the credit information is made up of the non-linear combination of soft information and financial information. When the relative importance of soft information and financial information is considered, soft information is relatively more critical to the loan approval then financial information. This is because financial ratios provided by small-business firms are not reliable enough.