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Small and Medium Enterprises (SMEs) in Taiwan play an important role in the economic system, but compared to other listed or larger companies, SMEs are more difficult in obtaining fund, and this is the reason why Small and Medium Enterprises Credit Guarantee Fund (SME Credit Guarantee Fund) sets up. The purpose of this study is to discuss the guaranteed risk of the SME Credit Guarantee Fund, including the estimate of the ex-ante probability of default and the hypothesis test of the ex-post recovery rate. The sample data, which are divided into twenty three industries, are established for the estimation of the market value for all kinds of industries, using the Moody's KMV Private Firm Model as the basic theory to estimate the company¡¦s probability of default and revising the default point to fit the features among different economic periods in Taiwan. This research uses the Chi-square homogeneity test to test how characteristic variables of companies affect the recovery rate. The study finds that the default point of the original definition of KMV may underestimate SME¡¦s probability of default in Taiwan, and there is lower estimated probability of default at good times rather than that at bad times. The recovery rate shows a right-skewed distribution, and the results also indicate that the how many banks the companies transact with, whether the directors and supervisors of the companies are joint and several guarantee, how old the companies are and how long the responsible officers are in business, have significantly affected the recovery rate.

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In almost all nations, credit guarantee is an important system that the government relies on to help small and medium enterprises (SMEs) obtain finance and provide guidance to them. In Taiwan, Small and Medium Enterprise Credit Guarantee Fund (SMEG) is an institution mandated by the government to assist SMEs to obtain necessary funds from financial institutions. Although SMEG is a non-profit organization, its financial status still affects its sustainability. Therefore, this paper modifies the model presented by Merrick (2001) and uses data of loans submitted by a domestic bank to SMEG for credit guarantee to estimate probability of default and recovery rate of credit guaranteed loans. As this model quantifies risk of credit guarantee, it can help SMEG calculate the necessary reserve for prepayment in subrogation. In this increasingly complicated financial environment, quality of risk control determines the prosperity or survival of an organization. The proposed model is a feasible risk evaluation model that credit guarantee institutions can utilize to effectively improve their quality of risk control.

Several reports research the best prediction power of the credit risk models for different industries. The structural models use firm¡¦s information for firms¡¦ structural variables, such as asset value and asset volatility, to determine the time of default, but it suffer from some drawbacks, which represent the main reasons behind their relatively poor empirical performance. It require estimates for the parameters of the firm¡¦s asset value, which is nonobservable. Moody's KMV model is well known and useful among them, but it ignores recovery rate and difference in financial structure and industry. The reduced-form models fundamentally differ from typical structural models in the degree of predictability of the default. Reduced-form models use market data and assume the probability of default is exogenously generated. However, the basel committee for banking supervision proposed that risk is endogenous. The purpose of this paper is using quantile and threshold regression to introduce a new approach which is based on the Moody¡¦s KMV model, the Lu and Kuo ( 2005) and the Altman, Brooks Brady, Resti and Sironi (2005) to the evaluation of the endogenous probability of default and the endogenous recovery rate.

We examine recovery rates of defaulted bonds in the US corporate bond market, based on a complete set of traded prices and volumes. A study of the trading microstructure around various types of default events is provided. We document temporary price pressure with high trading volumes on the default day and the following 30 days, and low trading activity thereafter. Based on this analysis, we determine market-based recovery rates and quantify various liquidity measures. We study the relation between the recovery rates and these measures, considering additionally a comprehensive set of bond characteristics, firm fundamentals, and macroeconomic variables. (authors' abstract)