The Valuation of government guarantees provided for municipalities

Date

2015

Author

Mert, Mehmet Esat

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Credit risk is defined as the risk of portfolio value variations due to unforeseeable fluctuations in the credit quality of a party in a financial contract. The operations that create receivable and contingent liability are the basic sources of credit risk. Credit risk models are needed in order to quantify the risk related to these sources better and minimize them by monitoring regularly. Although credit risk models are widely used in private sector, there are also usage areas for various operations of the government especially in public debt operations. In public debt management, credit risks are mainly arised from Treasury guarantees, on-lent credits and other Treasury receivables. In this regard, government repayment guarantees and on-lent credits provided for municipalities are basic contingent liabilities of the government. These guarantees turn into liabilities in case of municipality defaults. In order to prevent the unexpected distress due to mentioned contingent liabilities and meeting the cash needed without creating pressure on government borrowing, a comprehensive credit risk management is a requirement. Therefore accurate guarantee premium pricing of guarantees and on-lent credits provided for municipalities is our focus in this study. In the first part of the study, Logistic Regression and Artificial Neural Networks (ANNs) are utilized to estimate the default probabilities of several municipalities in Turkey. Then the cost of the insurance for guaranteed and on-lent credits provided to the municipalities is computed by relating the guarantee premium to several tools such as Credit Default Swap, Interest Rate Difference and Expected Loss.

Subject Keywords

Finance, Public., Government securities., Municipal government., Local finance

URI

http://etd.lib.metu.edu.tr/upload/12618972/index.pdf
https://hdl.handle.net/11511/25039

Collections

Graduate School of Natural and Applied Sciences, Thesis