An Approach to Quantify the Heat Wave Strength and Price a Heat Derivative for Risk Hedging

Mitigating the heat stress via a derivative policy is a vital financial option for agricultural producers and other business sectors to strategically adapt to the climate change scenario. This study has provided an approach to identifying heat stress events and pricing the heat stress weather derivative due to persistent days of high surface air temperature （SAT）. Cooling degree days （CDD） are used as the weather index for trade. In this study, a call-option model was used as an example for calculating the price of the index. Two heat stress indices were developed to describe the severity and physical impact of heat waves. The daily Global Historical Climatology Network （GHCN-D） SAT data from 1901 to 2007 from the southern California, USA, were used. A major California heat wave that occurred 20-25 October 1965 was studied. The derivative price was calculated based on the call-option model for both long-term station data and the interpolated grid point data at a regular 0.1~ x0.1~ latitude-longitude grid. The resulting comparison indicates that （a） the interpolated data can be used as reliable proxy to price the CDD and （b） a normal distribution model cannot always be used to reliably calculate the CDD price. In conclusion, the data, models, and procedures described in this study have potential application in hedging agricultural and other risks.

Risk-Neutral Dynamic of Forward-Looking Default Probabilities and Recovery Rates： Evidence From Credit Default Swap Prices of DOW30 Companies

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.