struct CorrelationCovariance{__T_ce} <: AbstractCovarianceEstimatorAnswers both cov and cor with the wrapped estimator's correlation matrix.
Statistics.cor(ce::CorrelationCovariance, X::MatNum; dims::Int = 1,
kwargs...)Compute the correlation matrix using the underlying estimator.
Statistics.cov(ce::CorrelationCovariance, X::MatNum; dims::Int = 1,
kwargs...)Compute the correlation matrix using the underlying estimator.
abstract type AbstractArray{var"#s90"<:(Union{var"#s89", var"#s88"} where {var"#s89"<:Number, var"#s88"<:AbstractJuMPScalar}), 2}Alias for an abstract matrix of numeric types or JuMP scalar types.
Statistics.cov(ce::AbstractCovarianceEstimator, X::MatNum; dims::Int = 1, kwargs...)Generic covariance fallback assembling the covariance matrix from the estimator's correlation matrix and the marginal standard deviations of its variance estimator ce.ve.
struct Covariance{__T_me, __T_ce, __T_alg} <: AbstractCovarianceEstimatorEstimates the covariance matrix of asset returns from a centring estimator, a covariance estimator, and a moment algorithm.
Statistics.cor(
ce::Covariance,
X::MatNum;
dims::Int = 1,
mean = nothing,
kwargs...
) -> MatNumCompute the correlation matrix using a Covariance estimator.
cor(
ce::Covariance{<:Any, <:Any, <:SemiMoment},
X::AbstractMatrix{<:Union{var"#s89", var"#s88"} where {var"#s89"<:Number, var"#s88"<:AbstractJuMPScalar}};
dims,
mean,
kwargs...
) -> AnySemiMoment variant of cor(ce::Covariance, X::MatNum; dims::Int = 1, mean = nothing, kwargs...).
Statistics.cor(
ce::GeneralCovariance,
X::MatNum;
dims::Int = 1,
mean = nothing,
kwargs...
) -> MatNumCompute the correlation matrix using a GeneralCovariance estimator.
Statistics.cov(
ce::Covariance,
X::MatNum;
dims::Int = 1,
mean = nothing,
kwargs...
) -> MatNumCompute the covariance matrix using a Covariance estimator.
cov(
ce::Covariance{<:Any, <:Any, <:SemiMoment},
X::AbstractMatrix{<:Union{var"#s89", var"#s88"} where {var"#s89"<:Number, var"#s88"<:AbstractJuMPScalar}};
dims,
mean,
kwargs...
) -> AnySemiMoment variant of cov(ce::Covariance, X::MatNum; dims::Int = 1, mean = nothing, kwargs...).
Statistics.cov(
ce::GeneralCovariance,
X::MatNum;
dims::Int = 1,
mean = nothing,
kwargs...
) -> MatNumCompute the covariance matrix using a GeneralCovariance estimator.
Statistics.cor(
ce::GerberCovariance,
X::MatNum;
dims::Int = 1,
kwargs...
) -> MatNumCompute the Gerber correlation matrix using the algorithm specified in ce.alg.
Statistics.cov(
ce::GerberCovariance,
X::MatNum;
dims::Int = 1,
kwargs...
) -> MatNumCompute the Gerber covariance matrix using the algorithm specified in ce.alg.
Statistics.cor(ce::SmythBrobyCovariance, X::MatNum; dims::Int = 1, kwargs...)Compute the Smyth-Broby correlation matrix.
Statistics.cov(ce::SmythBrobyCovariance, X::MatNum; dims::Int = 1, kwargs...)Compute the Smyth-Broby covariance matrix.
Statistics.cor(ce::DistanceCovariance, X::MatNum; dims::Int = 1, kwargs...)Compute the pairwise distance correlation matrix for all columns in a data matrix using a configured DistanceCovariance estimator.
Statistics.cov(ce::DistanceCovariance, X::MatNum; dims::Int = 1, kwargs...)Compute the pairwise distance covariance matrix for all columns in a data matrix using a configured DistanceCovariance estimator.
Statistics.cor(ce::LowerTailDependenceCovariance, X::MatNum; dims::Int = 1, kwargs...)Compute the lower tail dependence correlation matrix using a LowerTailDependenceCovariance estimator.
Statistics.cor(::KendallCovariance, X::MatNum; dims::Int = 1, kwargs...)Compute the Kendall's tau rank correlation matrix using a KendallCovariance estimator.
Statistics.cor(::SpearmanCovariance, X::MatNum; dims::Int = 1, kwargs...)Compute the Spearman's rho rank correlation matrix using a SpearmanCovariance estimator.
Statistics.cor(ce::MutualInfoCovariance, X::MatNum; dims::Int = 1, kwargs...)Compute the mutual information (MI) correlation matrix using a MutualInfoCovariance estimator.
Statistics.cor(ce::PortfolioOptimisersCovariance, X::MatNum; dims = 1, kwargs...)Compute the correlation matrix with post-processing using a PortfolioOptimisersCovariance estimator.
Statistics.cov(ce::PortfolioOptimisersCovariance, X::MatNum; dims = 1, kwargs...)Compute the covariance matrix with post-processing using a PortfolioOptimisersCovariance estimator.
Statistics.cor(ce::ImpliedVolatility, X::MatNum; dims::Int = 1, mean = nothing,
iv::MatNum, ivpa::Option{<:Num_VecNum} = nothing, kwargs...)Compute the correlation matrix using implied volatility scaling.
Statistics.cov(ce::ImpliedVolatility, X::MatNum; dims::Int = 1, mean = nothing,
iv::MatNum, ivpa::Option{<:Num_VecNum} = nothing, kwargs...)Compute the covariance matrix using implied volatility scaling.
Statistics.cor(ce::WindowedCovariance, X::MatNum; dims::Int = 1, mean = nothing, iv::Option{<:MatNum} = nothing, kwargs...)Compute Statistics.cor over a rolling or indexed observation window (matrix input).
Statistics.cov(ce::WindowedCovariance, X::MatNum; dims::Int = 1, mean = nothing, iv::Option{<:MatNum} = nothing, kwargs...)Compute Statistics.cov over a rolling or indexed observation window (matrix input).
Statistics.cor(
ce::GerberIQCovariance,
X::MatNum;
dims::Int = 1,
kwargs...
) -> MatNumCompute the Gerber IQ correlation matrix.
Statistics.cov(
ce::GerberIQCovariance,
X::MatNum;
dims::Int = 1,
kwargs...
) -> MatNumCompute the Gerber IQ covariance matrix.