The Contingent Valuation Method is used to evaluate individual preferences for a change concerning a public non-market resource or property. The objective is to build a nonparametric forecasting model of an individual...The Contingent Valuation Method is used to evaluate individual preferences for a change concerning a public non-market resource or property. The objective is to build a nonparametric forecasting model of an individual＇s Willingness To Pay according to geographical location. Within this framework, an estimator （of type Nadaraya-Watson） is proposed for the regression of the variable related to geolocation. The specific characteristics of the location variable lead us to a more general regression model than the traditional models. Results are established for convergence of our estimator.展开更多
In modem financial markets, the credit default swap （CDS） market has supplanted the bond market as the industry gauge for a borrower＇s credit quality. Therefore, it is very important to value CDS accurately by gett...In modem financial markets, the credit default swap （CDS） market has supplanted the bond market as the industry gauge for a borrower＇s credit quality. Therefore, it is very important to value CDS accurately by getting closer to more realistic pricing models. So far there have been no models for extracting forward-looking credit information to value CDS. In current practice, historical data is used in a credit default swap pricing model. One of the reasons was the difficulty when the market for credit derivatives was small, to extract forward-looking credit information such as recovery rates and default probabilities from traded securities. Since the CDS market has undergone rapid expansion in recent years, the possibilities of extracting forward-looking credit information have increased. Our work significantly extends Das and Hanouma （2009） where a flexible jump-to-default model was introduced to obtain implied recovery rates. We improve the flexible jump-to-default model where forecasted forward-looking hazard rates and recovery rates can be extracted using stock prices, stock volatilities and data from credit default markets to forecast CDS spreads. Instead of using exogenously assumed constant recovery rates and default probabilities from a credit rating agency, we use forward-looking hazard rates and recovery rates to price and forecast CDS spreads. We also compare out-of-sample market CDS spreads with our forecasted CDS spreads to check how well our model performs. Our model fit the market CDS spreads very well across all time to maturity CDS contracts except in some extreme cases when there is a big jump in CDS spreads.展开更多
文摘The Contingent Valuation Method is used to evaluate individual preferences for a change concerning a public non-market resource or property. The objective is to build a nonparametric forecasting model of an individual＇s Willingness To Pay according to geographical location. Within this framework, an estimator （of type Nadaraya-Watson） is proposed for the regression of the variable related to geolocation. The specific characteristics of the location variable lead us to a more general regression model than the traditional models. Results are established for convergence of our estimator.
文摘In modem financial markets, the credit default swap （CDS） market has supplanted the bond market as the industry gauge for a borrower＇s credit quality. Therefore, it is very important to value CDS accurately by getting closer to more realistic pricing models. So far there have been no models for extracting forward-looking credit information to value CDS. In current practice, historical data is used in a credit default swap pricing model. One of the reasons was the difficulty when the market for credit derivatives was small, to extract forward-looking credit information such as recovery rates and default probabilities from traded securities. Since the CDS market has undergone rapid expansion in recent years, the possibilities of extracting forward-looking credit information have increased. Our work significantly extends Das and Hanouma （2009） where a flexible jump-to-default model was introduced to obtain implied recovery rates. We improve the flexible jump-to-default model where forecasted forward-looking hazard rates and recovery rates can be extracted using stock prices, stock volatilities and data from credit default markets to forecast CDS spreads. Instead of using exogenously assumed constant recovery rates and default probabilities from a credit rating agency, we use forward-looking hazard rates and recovery rates to price and forecast CDS spreads. We also compare out-of-sample market CDS spreads with our forecasted CDS spreads to check how well our model performs. Our model fit the market CDS spreads very well across all time to maturity CDS contracts except in some extreme cases when there is a big jump in CDS spreads.
