We develop a flexible discrete-time hedging methodology that minimizes the expected value of any desired penalty function of the hedging error within a general regime-switching framework. A numerical algorithm based on backward recursion allows for the sequential construction of an optimal...

We develop a flexible discrete-time hedging methodology that minimizes the expected value of any desired penalty function of the hedging error within a general regime-switching framework. A numerical algorithm based on backward recursion allows for the sequential construction of an optimal...

An empirical comparison of prices for two categories of financial derivatives of the NYISO power market, namely Transmission Congestion Contracts (TCCs) and futures contracts, is performed. The objective is to assess whether these two categories of derivatives are priced consistently, as their...

Statistical learning models are proposed for the prediction of the probability of a spike in the electricity DART (day-ahead minus real-time price) spread. Assessing the likelihood of DART spikes is of paramount importance for virtual bidders, among others. The model's performance is evaluated...

We develop a flexible discrete-time hedging methodology that miminizes the expected value of any desired penalty function of the hedging error within a general regime-switching framework. A numerical algorithm based on backward recursion allows for the sequential construction of an optimal...

This paper develops a dynamic joint model of the implied volatility (IV) surface and its underlying asset, impervious to arbitrage and quick to estimate. It combines an asymptotically well-behaved, parametric IV surface representation with a two-component variance, and non-Gaussian asymmetric...