Future Price Models
Future Price Model Types
DiffFusion.MarkovFutureModel
— Typestruct MarkovFutureModel <: SeparableHjmModel
hjm_model::GaussianHjmModel
end
A Markov model for Future prices with piece-wise constant benchmark price volatility and constant mean reversion.
We implement an object adapter for the GaussianHjmModel
to re-use implementation for common modelling parts.
The MarkovFutureModel
differs from the GaussianHjmModel
essentially only by the drift Theta.
Moreover, we do not require the integrated state variable and want to identify correlations with Future prices instead of forward rates.
DiffFusion.markov_future_model
— Functionmarkov_future_model(
alias::String,
delta::ParameterTermstructure,
chi::ParameterTermstructure,
sigma_f::BackwardFlatVolatility,
correlation_holder::Union{CorrelationHolder, Nothing},
quanto_model::Union{AssetModel, Nothing},
scaling_type::BenchmarkTimesScaling = ForwardRateScaling,
)
Create a Gausian Markov model for Future prices.
Model Functions for Payoff Evaluation
DiffFusion.log_future
— Functionlog_future(m::Model, alias::String, t::ModelTime, T::ModelTime, X::ModelState)
Calculate the Future price term h(t,T)'[x(t) + 0.5y(t)(1 - h(t,T))].
log_future(m::CompositeModel, alias::String, t::ModelTime, T::ModelTime, X::ModelState)
Calculate the Future price term h(t,T)'[x(t) + 0.5y(t)(1 - h(t,T))].
log_future(m::MarkovFutureModel, alias::String, t::ModelTime, T::ModelTime, X::ModelState)
Calculate the Future price term (h(t,T)'[x(t) + 0.5y(t)(1 - h(t,T))])'.