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Relativistic X at Risk: private API

PortfolioOptimisers.resolve_deferred_quantities — Method
resolve_deferred_quantities(x, ) -> StandardDeviation
resolve_deferred_quantities(x, , ) -> StandardDeviation

Resolve every Deferred Quantity held by x against prior result pr, returning a struct of the same type whose deferred slots hold plain values.

This resolves the deferred state and nothing else. A slot left unstated stays nothing, so whichever fallback the consumer already applies — sel on the factory path, chol_sigma_selector and its siblings on the JuMP path — keeps working unchanged. The two paths are separate: a JuMP model builder reads the risk measure's slots directly and never calls factory, so both entry points resolve.

Given a prior result the rule has two halves. Container recursion is derived from deferred_slots, so a type that only holds children needs no method at all. A type that resolves a quantity of its own defines a method, which overrides the derived one. Writing that half per type — rather than per field — is what lets slots that travel together be resolved together: a deferred sigma supplies chol from the same fit, so the pair is never mixed across two sources.

slv is the effective solver, and it is what a Calibration Rule in the same struct reads. It carries the value the optimisation settled on, so a rule resolves against one solver on both routes. On the factory route the @cprop selection has already put that solver on the struct, so the argument stays at its default. On the JuMP route no selection runs, so set_risk_constraints! reads the solver off the estimator and threads it here. A type that carries a solver of its own settles it locally as sel(x.slv, slv), beside the observation weights it already settles that way, and a type that carries none gives its rules none on either route.

Algorithm

  1. Return x unchanged. This method is the arm for a second argument that is not a prior result: with no prior in hand nothing can be fitted, so the deferred state travels on.

A more specific method dominates this one on a prior result: the one that deferred_slots derives for a container, and the hand-written one of a type that resolves a quantity of its own.

Related

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resolve_deferred_quantities(x, pr::AbstractPriorResult, slv = nothing)

Resolve the children that deferred_slots declared and the slots that calibration_slots declared, and return x itself when none of them changed.

This is the derived half of the resolution rule. A container declares its children once and both entry points follow: factory reaches them through @fprop, and the JuMP builders reach them through this method. Neither needs a forwarding method per container.

A type that resolves a quantity of its own overrides this with its own method, which is more specific. So the derivation carries container recursion alone, and never guesses how a matrix, a tensor or the centre a moment was taken about comes out of a fit.

Both channels end in one rebuild: a measure that carries both kinds of slot must not be rebuilt twice. resolve_calibration_slots states the calibration half and returns its resolved slots rather than a rebuilt object, and the two answers merge here. The deferred half merges last, so it wins a key both channels declare. A container names one child in both, and the child the recursion resolved is the one to keep.

slv is the effective solver, and the recursion threads it to every child. A container states no solver of its own, so it changes none: each child settles the one it was handed against the one it carries.

Algorithm

  1. Read the slots x declares with deferred_slots, giving slots.
  2. Read the resolved calibration slots with resolve_calibration_slots, giving calibrated.
  3. Return x unchanged when both are empty. A type with neither kind of slot needs no method of its own.
  4. Resolve every entry of slots with resolve_deferred_child, threading pr and slv to each, giving resolved.
  5. Refuse a slot the recursion left unresolved with assert_declared_slot_resolver.
  6. Hand merge(calibrated, resolved) to rebuild_with_slots, which returns x itself when no entry moved and a rebuilt copy when one did.

Returns

  • x itself when no slot moved, and a rebuilt copy of x when one did.

Related

source
resolve_deferred_quantities(
    x::RelativisticValueatRisk,
    pr::AbstractPriorResult
) -> RelativisticValueatRisk
resolve_deferred_quantities(
    x::RelativisticValueatRisk,
    pr::AbstractPriorResult,
    slv
) -> RelativisticValueatRisk

Resolve the significance level alpha and the deformation parameter kappa of a RelativisticValueatRisk against prior result pr.

alpha and kappa are a travelling pair: EntropyBudget reads the significance level of its sibling slot. So alpha resolves first, and the number it produced is stated in the CalibrationContext of the kappa slot before that slot is resolved. A stated number, a plain function and a rule that reads no sibling all ignore the field, so the order costs nothing where no rule reads a sibling.

The series this measure prices travels in the same context. It is the returns, which is the default calibration_series states, so this site names what the default context already holds. It is written all the same, for the reason every site writes it: the marker belongs to the measure, and no rule carries one of its own to be corrected.

The solver is settled once, as sel(x.slv, slv), and handed to both rules, so a rule may call RRM itself. The rebuild goes through rebuild_with_slots, whose positional call runs the inner constructor and re-runs both range checks on the calibrated numbers.

Related

source
PortfolioOptimisers.resolve_deferred_quantities — Method
resolve_deferred_quantities(x, ) -> StandardDeviation
resolve_deferred_quantities(x, , ) -> StandardDeviation

Resolve every Deferred Quantity held by x against prior result pr, returning a struct of the same type whose deferred slots hold plain values.

This resolves the deferred state and nothing else. A slot left unstated stays nothing, so whichever fallback the consumer already applies — sel on the factory path, chol_sigma_selector and its siblings on the JuMP path — keeps working unchanged. The two paths are separate: a JuMP model builder reads the risk measure's slots directly and never calls factory, so both entry points resolve.

Given a prior result the rule has two halves. Container recursion is derived from deferred_slots, so a type that only holds children needs no method at all. A type that resolves a quantity of its own defines a method, which overrides the derived one. Writing that half per type — rather than per field — is what lets slots that travel together be resolved together: a deferred sigma supplies chol from the same fit, so the pair is never mixed across two sources.

slv is the effective solver, and it is what a Calibration Rule in the same struct reads. It carries the value the optimisation settled on, so a rule resolves against one solver on both routes. On the factory route the @cprop selection has already put that solver on the struct, so the argument stays at its default. On the JuMP route no selection runs, so set_risk_constraints! reads the solver off the estimator and threads it here. A type that carries a solver of its own settles it locally as sel(x.slv, slv), beside the observation weights it already settles that way, and a type that carries none gives its rules none on either route.

Algorithm

  1. Return x unchanged. This method is the arm for a second argument that is not a prior result: with no prior in hand nothing can be fitted, so the deferred state travels on.

A more specific method dominates this one on a prior result: the one that deferred_slots derives for a container, and the hand-written one of a type that resolves a quantity of its own.

Related

source
resolve_deferred_quantities(x, pr::AbstractPriorResult, slv = nothing)

Resolve the children that deferred_slots declared and the slots that calibration_slots declared, and return x itself when none of them changed.

This is the derived half of the resolution rule. A container declares its children once and both entry points follow: factory reaches them through @fprop, and the JuMP builders reach them through this method. Neither needs a forwarding method per container.

A type that resolves a quantity of its own overrides this with its own method, which is more specific. So the derivation carries container recursion alone, and never guesses how a matrix, a tensor or the centre a moment was taken about comes out of a fit.

Both channels end in one rebuild: a measure that carries both kinds of slot must not be rebuilt twice. resolve_calibration_slots states the calibration half and returns its resolved slots rather than a rebuilt object, and the two answers merge here. The deferred half merges last, so it wins a key both channels declare. A container names one child in both, and the child the recursion resolved is the one to keep.

slv is the effective solver, and the recursion threads it to every child. A container states no solver of its own, so it changes none: each child settles the one it was handed against the one it carries.

Algorithm

  1. Read the slots x declares with deferred_slots, giving slots.
  2. Read the resolved calibration slots with resolve_calibration_slots, giving calibrated.
  3. Return x unchanged when both are empty. A type with neither kind of slot needs no method of its own.
  4. Resolve every entry of slots with resolve_deferred_child, threading pr and slv to each, giving resolved.
  5. Refuse a slot the recursion left unresolved with assert_declared_slot_resolver.
  6. Hand merge(calibrated, resolved) to rebuild_with_slots, which returns x itself when no entry moved and a rebuilt copy when one did.

Returns

  • x itself when no slot moved, and a rebuilt copy of x when one did.

Related

source
resolve_deferred_quantities(
    x::RelativisticValueatRiskRange,
    pr::AbstractPriorResult
) -> RelativisticValueatRiskRange
resolve_deferred_quantities(
    x::RelativisticValueatRiskRange,
    pr::AbstractPriorResult,
    slv
) -> RelativisticValueatRiskRange

Resolve the two significance levels and the two deformation parameters of a RelativisticValueatRiskRange against prior result pr.

Each end carries a travelling pair of its own: kappa_a reads alpha and kappa_b reads beta. The gain-side pair defaults to the loss-side pair, beta to alpha and kappa_b to kappa_a, so a pair stated on the loss side alone reaches both ends. The resolution runs the pair of the loss side and then the pair of the gain side, and neither side reads the other's number. That is the pairing range_tails builds and the functor evaluates.

The four slots carry four different bounds, so a rule of the wrong end or the wrong family is refused at construction. The solver is settled once and handed to all four rules.

Both ends price one series, which is the returns, so the same marker stands in the context of both kappa slots. The series is a property of the measure and not of an end, where the significance level is a property of the end.

Related

source
PortfolioOptimisers.resolve_deferred_quantities — Method
resolve_deferred_quantities(x, ) -> StandardDeviation
resolve_deferred_quantities(x, , ) -> StandardDeviation

Resolve every Deferred Quantity held by x against prior result pr, returning a struct of the same type whose deferred slots hold plain values.

This resolves the deferred state and nothing else. A slot left unstated stays nothing, so whichever fallback the consumer already applies — sel on the factory path, chol_sigma_selector and its siblings on the JuMP path — keeps working unchanged. The two paths are separate: a JuMP model builder reads the risk measure's slots directly and never calls factory, so both entry points resolve.

Given a prior result the rule has two halves. Container recursion is derived from deferred_slots, so a type that only holds children needs no method at all. A type that resolves a quantity of its own defines a method, which overrides the derived one. Writing that half per type — rather than per field — is what lets slots that travel together be resolved together: a deferred sigma supplies chol from the same fit, so the pair is never mixed across two sources.

slv is the effective solver, and it is what a Calibration Rule in the same struct reads. It carries the value the optimisation settled on, so a rule resolves against one solver on both routes. On the factory route the @cprop selection has already put that solver on the struct, so the argument stays at its default. On the JuMP route no selection runs, so set_risk_constraints! reads the solver off the estimator and threads it here. A type that carries a solver of its own settles it locally as sel(x.slv, slv), beside the observation weights it already settles that way, and a type that carries none gives its rules none on either route.

Algorithm

  1. Return x unchanged. This method is the arm for a second argument that is not a prior result: with no prior in hand nothing can be fitted, so the deferred state travels on.

A more specific method dominates this one on a prior result: the one that deferred_slots derives for a container, and the hand-written one of a type that resolves a quantity of its own.

Related

source
resolve_deferred_quantities(x, pr::AbstractPriorResult, slv = nothing)

Resolve the children that deferred_slots declared and the slots that calibration_slots declared, and return x itself when none of them changed.

This is the derived half of the resolution rule. A container declares its children once and both entry points follow: factory reaches them through @fprop, and the JuMP builders reach them through this method. Neither needs a forwarding method per container.

A type that resolves a quantity of its own overrides this with its own method, which is more specific. So the derivation carries container recursion alone, and never guesses how a matrix, a tensor or the centre a moment was taken about comes out of a fit.

Both channels end in one rebuild: a measure that carries both kinds of slot must not be rebuilt twice. resolve_calibration_slots states the calibration half and returns its resolved slots rather than a rebuilt object, and the two answers merge here. The deferred half merges last, so it wins a key both channels declare. A container names one child in both, and the child the recursion resolved is the one to keep.

slv is the effective solver, and the recursion threads it to every child. A container states no solver of its own, so it changes none: each child settles the one it was handed against the one it carries.

Algorithm

  1. Read the slots x declares with deferred_slots, giving slots.
  2. Read the resolved calibration slots with resolve_calibration_slots, giving calibrated.
  3. Return x unchanged when both are empty. A type with neither kind of slot needs no method of its own.
  4. Resolve every entry of slots with resolve_deferred_child, threading pr and slv to each, giving resolved.
  5. Refuse a slot the recursion left unresolved with assert_declared_slot_resolver.
  6. Hand merge(calibrated, resolved) to rebuild_with_slots, which returns x itself when no entry moved and a rebuilt copy when one did.

Returns

  • x itself when no slot moved, and a rebuilt copy of x when one did.

Related

source
resolve_deferred_quantities(
    x::RelativisticDrawdownatRisk,
    pr::AbstractPriorResult
) -> RelativisticDrawdownatRisk
resolve_deferred_quantities(
    x::RelativisticDrawdownatRisk,
    pr::AbstractPriorResult,
    slv
) -> RelativisticDrawdownatRisk

Resolve the significance level alpha and the deformation parameter kappa of a RelativisticDrawdownatRisk against prior result pr.

It carries the reading of resolve_deferred_quantities on the value-at-risk twin unchanged: alpha resolves first and reaches the kappa slot in its CalibrationContext. The drawdown series has one entry per row of the sample, so a rule reads the same sample size here as it does there.

The series does not carry over, and the context is what says so. This measure prices the absolute drawdown series of the portfolio, so calibration_series states AbsoluteDrawdownSeries and the marker travels beside alpha. A rule that reads the shape of a series then reads the drawdown series of each column of the sample, in place of the columns themselves, and the alpha it reads is the level of that same drawdown series. The key :kappa names this slot and the twin's slot alike, so nothing else could have told the rule which quantity it stands in front of.

Related

source
PortfolioOptimisers.resolve_deferred_quantities — Method
resolve_deferred_quantities(x, ) -> StandardDeviation
resolve_deferred_quantities(x, , ) -> StandardDeviation

Resolve every Deferred Quantity held by x against prior result pr, returning a struct of the same type whose deferred slots hold plain values.

This resolves the deferred state and nothing else. A slot left unstated stays nothing, so whichever fallback the consumer already applies — sel on the factory path, chol_sigma_selector and its siblings on the JuMP path — keeps working unchanged. The two paths are separate: a JuMP model builder reads the risk measure's slots directly and never calls factory, so both entry points resolve.

Given a prior result the rule has two halves. Container recursion is derived from deferred_slots, so a type that only holds children needs no method at all. A type that resolves a quantity of its own defines a method, which overrides the derived one. Writing that half per type — rather than per field — is what lets slots that travel together be resolved together: a deferred sigma supplies chol from the same fit, so the pair is never mixed across two sources.

slv is the effective solver, and it is what a Calibration Rule in the same struct reads. It carries the value the optimisation settled on, so a rule resolves against one solver on both routes. On the factory route the @cprop selection has already put that solver on the struct, so the argument stays at its default. On the JuMP route no selection runs, so set_risk_constraints! reads the solver off the estimator and threads it here. A type that carries a solver of its own settles it locally as sel(x.slv, slv), beside the observation weights it already settles that way, and a type that carries none gives its rules none on either route.

Algorithm

  1. Return x unchanged. This method is the arm for a second argument that is not a prior result: with no prior in hand nothing can be fitted, so the deferred state travels on.

A more specific method dominates this one on a prior result: the one that deferred_slots derives for a container, and the hand-written one of a type that resolves a quantity of its own.

Related

source
resolve_deferred_quantities(x, pr::AbstractPriorResult, slv = nothing)

Resolve the children that deferred_slots declared and the slots that calibration_slots declared, and return x itself when none of them changed.

This is the derived half of the resolution rule. A container declares its children once and both entry points follow: factory reaches them through @fprop, and the JuMP builders reach them through this method. Neither needs a forwarding method per container.

A type that resolves a quantity of its own overrides this with its own method, which is more specific. So the derivation carries container recursion alone, and never guesses how a matrix, a tensor or the centre a moment was taken about comes out of a fit.

Both channels end in one rebuild: a measure that carries both kinds of slot must not be rebuilt twice. resolve_calibration_slots states the calibration half and returns its resolved slots rather than a rebuilt object, and the two answers merge here. The deferred half merges last, so it wins a key both channels declare. A container names one child in both, and the child the recursion resolved is the one to keep.

slv is the effective solver, and the recursion threads it to every child. A container states no solver of its own, so it changes none: each child settles the one it was handed against the one it carries.

Algorithm

  1. Read the slots x declares with deferred_slots, giving slots.
  2. Read the resolved calibration slots with resolve_calibration_slots, giving calibrated.
  3. Return x unchanged when both are empty. A type with neither kind of slot needs no method of its own.
  4. Resolve every entry of slots with resolve_deferred_child, threading pr and slv to each, giving resolved.
  5. Refuse a slot the recursion left unresolved with assert_declared_slot_resolver.
  6. Hand merge(calibrated, resolved) to rebuild_with_slots, which returns x itself when no entry moved and a rebuilt copy when one did.

Returns

  • x itself when no slot moved, and a rebuilt copy of x when one did.

Related

source
resolve_deferred_quantities(
    x::RelativeRelativisticDrawdownatRisk,
    pr::AbstractPriorResult
) -> RelativeRelativisticDrawdownatRisk
resolve_deferred_quantities(
    x::RelativeRelativisticDrawdownatRisk,
    pr::AbstractPriorResult,
    slv
) -> RelativeRelativisticDrawdownatRisk

Resolve the significance level alpha and the deformation parameter kappa of a RelativeRelativisticDrawdownatRisk against prior result pr.

The measure is a hierarchical one, so it reaches no JuMP model and the factory route is its only resolution. The travelling pair is resolved in the order the absolute twin states.

The series is the twin's reading in its own units: this measure compounds the path, so calibration_series states RelativeDrawdownSeries and the context carries it. The two markers name two different series of the same column, and a rule that reads the shape of a series answers differently on each.

Related

source
PortfolioOptimisers.RRM — Function
RRM(x, slv, alpha = 0.05, kappa = 0.3, ...; kwargs...)

Compute the Relativistic Risk Measure (RRM) for a vector of portfolio returns.

Solves a convex optimisation problem to compute the RRM at confidence level alpha with relativistic parameter kappa, using the specified solver(s).

The primal power-cone programme is tried first. If no solver in slv succeeds on it, the equivalent dual programme is tried, which is numerically better conditioned for some solvers. If neither succeeds, the result is NaN.

Arguments

  • x: Vector of portfolio returns.
  • slv: Solver or vector of solvers.
  • alpha: Confidence level (default 0.05).
  • kappa: Relativistic parameter (default 0.3).
  • Additional parameters depending on the specific RRM formulation.
  • kwargs...: Additional keyword arguments passed to the solver.

Returns

  • RRM value (scalar), or NaN if neither the primal nor the dual programme is solved.

Related

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