cQuant.io provides several price simulation models that consume the most up-to-date market data available and use it to generate forecasts of future market trends and prices.

Some key capabilities of cQuant’s market forecasting solution include:

• Correlated Monte Carlo price simulation across multiple commodities and price locations down to the sub-hourly level.
• Robust parameterization of statistical market trends and price relationships.
• Highly configurable simulation parameters to ensure proper calibration against historical market data.

cQuant’s stochastic Monte Carlo price simulations capture robust statistical market trends down to the sub-hourly level. This means that each simulated future price outcome is assigned a likelihood of occurrence, providing an integrated view across the full range of possible future market states and an understanding of the uncertainty in future prices.

Energy and fuels prices are highly locational by their very nature, and cQuant’s models are designed to capture the unique market characteristics of each individual pricing location. Our forecasting models parameterize important location-specific market relationships so that simulations accurately reflect cross-commodity interactions, such as market heat rates and price correlations. This provides a market-consistent view of expected future prices and a statistically valid measurement of uncertainty around the mean.

Whether you’re looking for a market forecast to feed into other downstream business reporting processes or a fully-integrated portfolio-wide future cash flow analysis, cQuant’s market forecasting solution provides you with a solid analytical backbone you can rely on.