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First issuer of these securities approach rating agencies to rate their securities so that they can sell it in capital market. And issuer provide all the data information of the assets underlying the securities like loan data, proposed capital structure of SPV, proposed credit enhancement for each tranche of the securities.

Rating agency will assign an analyst to analyze the tranches for rating it. First probable looses incurred on all tranches are calculated. Rating agencies used complex statistical models for analyzing loss. The loss analysis gives rough idea of how much credit enhancement is required for each tranche to give particular rating.

Then analyst analyzes proposed capital structure of SPV to check whether it meets particular rating. Then finally analyst analysis the cash flow which gives information of interest and principal paid out of SPV and analyzes whether particular asset which is under tranche meets payment obligation. Analyst then rates each tranche and submits his rating to committee where they vote on the analyst view.

Once rating is confirmed they send the rating to issues rather than publishing it. If the issuer is satisfied with the rating he makes it public. If issuer makes rating public, rating Agencies get paid if not they get breakup fee. (Rousseau Stephane, 2009).

2.2.3 Concerns on models used in RMBS

According to (Daníelsson J, 2002), Rating traditional bonds is much easier because of availability of historical data where as rating structural products like RMBS you need much more complex models than that of normal models.

As the financial system become more complex, the need for complicated statistical models becomes greater. More the complexity, lesser the reliability on these models, so does these models tends to be less reliable. It is clear from the credit crisis of 2007 that the rating agencies used over optimistic input data, inappropriate modeling and insufficient checking of data quality and permitting gaming of models. Despite of advanced models, stress tests, and all the numbers, risk models do have important role to play in modeling risk as long as its limitations are known. Risk models are good at managing particularly trading desk but when asked to model whole institution it fails. So relying on such folly statistical models to model risk is foolishness. And the numbers that these models give are inappropriate.

Financial models are not simple and do not have basic or fundamental thermos to build on. These models can easily make you believe the results are accurate, the reason for these are;

1. Endogenous risk: In finance we can only model aggregate behavior. Financial modeling changes the statistical laws governing the financial system in real-time, leaving the modelers to play catch-up. This becomes especially pronounced as the financial system gets into a crisis. This is a phenomenon is called endogenous risk.

2. Quality of assumptions: we can't take it to consideration all parameters in to model so it is important to take it to consideration the main parameters that affect t本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。