I am attempting to calculate the one-month 95th and 99th percentile profits for a two-year portfolio of energy-generating assets over the next three months. This means that the calculation has two components: 1) monthly electricity generation during the aforementioned two-year span and 2) energy futures prices (for each month in the two aforementioned year span).
My analysis to date has shown that: a) the generation of each asset follows its own distribution family (based on minimizing the sum of square errors between empirical percentiles and theoretical distribution percentiles) and b) electricity forward prices (not their returns!) for the energy markets follow ARIMA models with significant variables, significant residual normality, insignificant residual autocorrelation, and insignificant heteroskedasticity. I have ~40 observations per month per electricity-generating asset and about 40 observations per electricity contract (liquidity increases substantially during the final 2 months, or 40 trading days, of an electricity contract -- which substantially changes its behavior)
- Running N energy generation simulations per month per asset
- Simulating N electricity forward prices per month
- Taking the product of (1) and (2)
- Summing (3) across all assets
- Taking the desired quantile (either q=0.05 or q=0.01) of (4)
Is this approach sound? Generation data are decidedly non-normal (even with the help of Box-Cox transformations and/or standardization and/or differencing) and seasonal (mean reverting at the monthly level) and different sites have starkly different distributions (e.g., Normal, Laplace, Levy, Johnson's Bounded, Cauchy) across months, so I've ruled out simpler VaR approaches like variance-covariance and historical VaR.
I am hesitant because it does not explicitly account for correlation among: a) assets, b) between an asset and its respective forward price, or c) prices of successively expiring forward contracts, or d) generation of successive months.
Your help and time are immensely appreciated!