Future Price Models

Future Price Model Types

DiffFusion.MarkovFutureModel — Type

struct MarkovFutureModel <: SeparableHjmModel
    hjm_model::GaussianHjmModel
    state_alias::Vector{String}
    factor_alias::Vector{String}
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.

source

DiffFusion.markov_future_model — Function

markov_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.

source

Model Functions for Payoff Evaluation

DiffFusion.log_future — Function

log_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))].

source

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))].

source

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))])'.

source