Increasing adoption of renewable energy, which is inherently intermittent, poses several business risks for renewable energy producers. We identify the core co-dependencies of electricity demand, temperature and radiation risk exposures of a solar energy producer at different times of the year, which offer a valuable risk mitigation opportunity. By capturing the co-dependencies in a vector autoregressive, multivariate GARCH model, we investigate the extent of natural hedge embedded in the solar energy producer's cash flows. We further develop the framework to use explicit optimal cross hedging strategies for risk mitigation using temperature-based weather derivatives. We find that there is significant benefit of natural hedge in certain months of the year, while in others, explicit hedges can effectively modify risk exposure.
- Cooling/Heating degree days (CDD/HDD)
- Cross hedging
- Optimal hedge
- Vector auto-regression
- Volatility dynamics