KMV模型对房地产行业信用风险的应用

发布时间：2018-01-06 13:24

本文关键词：KMV模型对房地产行业信用风险的应用　出处：《贵州财经大学》2013年硕士论文　论文类型：学位论文

更多相关文章： 房地产 信用风险 KMV模型 违约距离

【摘要】：风险和风险管理在我们的生活中无处不在。在复杂的信用关系的多层次网络环节中，任何一环节出现问题，都会导致信用风险。信用风险对区域经济甚至是全球经济产生的危害都是巨大而且深远的，，因此，对信用风险进行有效地度量和管理是各国政府、学术界共同关注的焦点问题。当前，国际经济形势过于复杂，加之全球经济刚历经金融危机，尚处于调整阶段，国际市场环境不稳定因素过多；国内原有竞争优势被削弱、增长动力减缓，新优势不能及时形成，经济运行处于寻去新平衡过程中。为此，中国将会出现大面积的信贷与债务危机，加上西方特别是欧元区的经济的低迷，对中国手上的外汇和外国债券都造成了大面积的缩水冲击，房地产市场也持续低迷，处于危机中的我国商业银行和其他金融机构面临较为严重的打击。房地产行业是关系到我国经济民生的的重要行业，其最为典型的特征就是资金密集型产业。改革开放以来，房地产业在我国GDP中所占的比重不断增加，成为我国经济增长的重要力量。本文通过对几种常见的信用风险测度模型进行简述以及分析比较，选择使用KMV模型进行实证研究，该实证分析针对我国的房地产上市公司，选取在上海证券交易所挂牌上市的10家房地产上市公司（其中5家为绩优类，5家为级差类），选取了绩优类和绩差类公司2010年、2011年和2012年的数据，通过对数据进行处理汇集，研究分析ST类（绩差类）和非ST类（绩优类）上市公司违约概率,应用KMV模型进行实证研究。本文研究得出绩优类房地产上市公司的预期违约率高于ST类房地产上市公司的预期违约率，也就是说，非ST房地产上市公司的信用风险状况较好；2011和2012年度非ST类房地产上市公司的信用风险情况较好。在2010、2011和2012的三年中，绩差类ST房地产上市公司的信用风险是逐年恶化的。通过结合其他因素进行分析，笔者认为基于KMV模型对我国房地产业上市公司进行实证研究的结果与理论预期还是比较符合实际情况的。在此基础上，笔者针对不同类型的房地产上市企业的发展提出一系列建议。与此同时我们也看到KMV模型在我国资本市场的运用依然受到宏观政策、国情等多方面因素的综合影响。笔者认为，可以从以下几个方面对KMV模型进行改进：首先,我国股票市场成立时间较短，违约情况出现的还比较少，尚无法建立起有效的信用数据库，不能使用经验违约率作为预期违约率，理论违约率和经验违约率的不同给信用风险的度量造成不便的情况十分普遍。其次,由于我国上市公司与国外成熟的资本市场不同，直接照搬的KMV模型并不一定适合我国资本市场的实际情况，因此，应该对KMV模型进行修正，比如对违约距离的设定、对股票波动率计算方法的选择等，以使KMV模型在我国得到更加广泛地应用。
[Abstract]:Risk and risk management are ubiquitous in our lives . In the multi - level network of complex credit relationships , any problem occurs in any link , which can lead to credit risk . Credit risk is a great and far - reaching impact on regional economy and even global economy . Therefore , the effective measurement and management of credit risk is the focus problem of governments and academia . At present , the international economic situation is too complex , the global economy has just been in the adjustment stage after the financial crisis , the international market environment is not stable , the new advantage cannot be formed in time , the economic operation is in the pursuit of the new balance process . For this reason , China will have a large area of credit and debt crisis , and the economic operation is in the pursuit of the new balance process . Based on the analysis and comparison of several common credit risk measurement models , the paper selects 10 real estate listed companies listed in Shanghai Stock Exchange by analyzing and comparing several common credit risk measurement models . The empirical analysis is conducted on 10 real estate listed companies listed in Shanghai Stock Exchange ( five of them are excellent and five are classified ) , and the credit risk of non - ST real estate listed companies is better . In the three years of 2010 , 2011 and 2012 , the credit risk of ST real estate listed companies in 2011 and 2012 is worsened by year . Based on the analysis of other factors , the author thinks that based on the KMV model , the results of the empirical research on Chinese real estate listed companies are consistent with the actual situation . At the same time , the author puts forward a series of suggestions on the development of different types of real estate listed companies . At the same time , we also find that the use of KMV model in China ' s capital market is still influenced by many factors , such as macro - policy and national conditions . First of all , China ' s stock market has been set up short , the default situation is relatively few , it is not possible to establish an effective credit database , can ' t use the experience default rate as the expected default rate , the theory breach rate and the experience default rate different to the credit risk measure cause inconvenience . Secondly , because the listed company of our country is different from the mature capital market , the direct reference KMV model does not necessarily fit the actual situation of our country ' s capital market . Therefore , we should amend the KMV model , such as the setting of the default distance , the choice of the calculation method of the fluctuation rate of the stock , etc . , so as to make the KMV model more widely used in our country .

【学位授予单位】：贵州财经大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F832.4;F299.233.4

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