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This paper investigates the relationship between firm characteristics and the capital market response to the introduction of stock options. The capital market responds positively to the announcement of adopting stock options. However, the positive response is significant only over the period of clustered announcement dates. In addition, the announcement effect of manufacturing firms is significantly positive, while that of financial companies is negative For manufacturing firms, the announcement effect has a positive relationship with the growth potential and capital efficiency and a negative relationship with the size, liquidity, labor costs and leverage. Thus, the smaller growing firms with liquidity constraints and low leverage adopt stock options to get over the agency problem and compensate a relatively low salary of their employees. However, the variables for chaebol affiliation and the stock price volatility are not significant. The results for financial firms are almost identical except for the positive coefficient of liquidy variable.

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This paper reveals two properties of equity options in a market with price limits on their underlying assets. The first one is that an eq 비 ty option in such a market is equivalent to a portf이 io of a risk-free zero-coupon bond and equity options without price limits. The second one is that an equi ty option with price limits is path-dependent. This paper also discusses whether it is legitimate to use the risk-neutral valuation to price such an option. Finally this paper exemplifies a simplified numerical approach to identify the payo什distribution of such an option, incorporating its path-dependency.

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We introduce a theoretical valuation model of the venture company’s firm value. The most important characteristic of this model is its capacity to estimate the value of intellectual assets. as well as the enterprise. uSing publicly disclosed financial data. The valuation model consists of six groups of publicly available information ; namely. current financial assets. current and the prior period’s tangible asset, current and the prior period ’s net working capitals. current operating income, current expenses of knowledge management, and current investment assets. Because this makes the model very practical to apply in anal yses, it is possible to apply this model of firm value valuation to suit individual venture companies. The results of the positive study are as f이 lows . First, the size of intellectual assets. which is estimated from the expenses of knowledge management and networking investment turns out to be sizable. The estimated size of intellectual assets is reported to be 0.7 to 1.0 times of total assets in the balance sheet Second. the venture company valuation model can be used to estimate the proportion of the traditional value (including tangible asset value, working capital value and earnings’ value) , and the intellectual asset value (including technology. marketing, management, and networking asset values). The weight of each asset value is reported to be 40% : 60% in case of venture companies and 33% : 67% for internet companies.

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This paper analyzes the incentives of credit banks and firms that face financial crisis, focusing on their debt-equity swaps. Empirical analyses show that firms with larger asset sizes, higher debt ratios, lower debt coverage ratios, lower ownership by largest shareholder, and smaller fixed assets tend to implement debt-equity swaps. It also shows that banks with lower BIS ratios and higher loan concentration prefer debt-equity swaps as a way of restructuring failing corporate customers. These results provide an indirect evidence that supports too-big-to-fail hypothesis in corporate loan market.

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This study investigates hedging performance of KTB futures with respect to KTB and various bond portfolios uSing VECM, VAR, Bivariate GARCH (1 ,1) and OLS regression models. Both weekly and daily hedging performance is evaluated. The sample period covers from January 4, 2000 to June 30, 2001. We found the f이 lowing results. Firstly, unit roots are found in KTB futures and various spot prices. Secondly, we can not find statistical differences among hedge ratios estimated from VECM, VAR. Bivariate GARCH (1 , 1) and OLS regression models. Thirdly, there are no significant differences in hedging performance among various models. Fourthly, weekly hedging produces the better hedging performance than daily hedging. Finally, overall hedging performance of KTB futures is relatively poor. This result implies that underlying spot bond portf이 io comprising KTB futures does not represent spot interest rate movements very well. It seems that we need more various futures products in KTB futures to enhance the hedging performance.

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