SearchLacima is pleased to introduce Historical Data Simulation (HDS), which is available within the ‘Advanced Models’ module of Lacima Analytics.
Why HDS?
Traditional stochastic models for renewables, weather and other complex variables can be powerful – but often require:
- Extensive calibration
- Specialised parameter fitting
- Ongoing back-testing and maintenance
For many portfolios, this creates model complexity that slows analysis and increases operational burden. HDS provides a faster, simpler, and intuitive alternative.
How It works
HDS generates Monte Carlo simulations directly from historical data, automatically preserving:
- Real-world volatility structures
- Seasonal behaviour
- Cross-factor correlations
- Temporal dependencies
Users can generate realistic market scenarios by selecting historical data ranges and modelling parameters, preserving key market relationships while tailoring simulations using multiple modelling modes, mean adjustments, and forward curve profiling.
Practical example
Consider a portfolio manager modelling solar output for the coming year. Using HDS, they can:
- Generate hundreds of realistic scenarios in minutes
- Preserve correlations with temperature and market prices
- Align simulations with forward curves
- Adjust mean levels where required
This accelerates analysis while maintaining statistical realism.
Key Capabilities
- Historical Monte Carlo engine
- Correlated risk factor simulation
- Forward curve integration
- Mean adjustment controls
- Robust handling of incomplete or irregular data
- Multiple modelling modes (Levels, Additive, Proportional)
HDS combines historical realism with configurable forward expectations to deliver actionable insight into both spot and forward behaviours under historically grounded conditions.
